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  • 1.
    Pham, Tuan
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Prince Mohammad Bin Fahd Univ, Saudi Arabia.
    Fan, Chuanwen
    Linköpings universitet, Institutionen för biomedicinska och kliniska vetenskaper, Avdelningen för kirurgi, ortopedi och onkologi. Linköpings universitet, Medicinska fakulteten. Sichuan Univ, Peoples R China.
    Pfeifer, Daniella
    Linköpings universitet, Institutionen för biomedicinska och kliniska vetenskaper, Avdelningen för kirurgi, ortopedi och onkologi. Linköpings universitet, Medicinska fakulteten.
    Zhang, Hong
    Orebro Univ, Sweden.
    Sun, Xiao-Feng
    Linköpings universitet, Institutionen för biomedicinska och kliniska vetenskaper, Avdelningen för kirurgi, ortopedi och onkologi. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Centrum för kirurgi, ortopedi och cancervård, Onkologiska kliniken US.
    Image-Based Network Analysis of DNp73 Expression by Immunohistochemistry in Rectal Cancer Patients2020Ingår i: Frontiers in Physiology, ISSN 1664-042X, E-ISSN 1664-042X, Vol. 10, artikel-id 1551Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Background: Rectal cancer is a disease characterized with tumor heterogeneity. The combination of surgery, radiotherapy, and chemotherapy can reduce the risk of local recurrence. However, there is a significant difference in the response to radiotherapy among rectal cancer patients even they have the same tumor stage. Despite rapid advances in knowledge of cellular functions affecting radiosensitivity, there is still a lack of predictive factors for local recurrence and normal tissue damage. The tumor protein DNp73 is thought as a biomarker in colorectal cancer, but its clinical significance is still not sufficiently investigated, mainly due to the limitation of human-based pathology analysis. In this study, we investigated the predictive value of DNp73 in patients with rectal adenocarcinoma using image-based network analysis.

    Methods: The fuzzy weighted recurrence network of time series was extended to handle multi-channel image data, and applied to the analysis of immunohistochemistry images of DNp73 expression obtained from a cohort of 25 rectal cancer patients who underwent radiotherapy before surgery. Two mathematical weighted network properties, which are the clustering coefficient and characteristic path length, were computed for the image-based networks of the primary tumor (obtained after operation) and biopsy (obtained before operation) of each cancer patient.

    Results: The ratios of two weighted recurrence network properties of the primary tumors to biopsies reveal the correlation of DNp73 expression and long survival time, and discover the non-effective radiotherapy to a cohort of rectal cancer patients who had short survival time.

    Conclusion: Our work contributes to the elucidation of the predictive value of DNp73 expression in rectal cancer patients who were given preoperative radiotherapy. Mathematical properties of fuzzy weighted recurrence networks of immunohistochemistry images are not only able to show the predictive factor of DNp73 expression in the patients, but also reveal the identification of non-effective application of radiotherapy to those who had poor overall survival outcome.

  • 2.
    Pham, Tuan
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Center for Artificial Intelligence, Prince MohammadBin Fahd University, Al Khobar, Kingdom of Saudi Arabia.
    Wårdell, Karin
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Eklund, Anders
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Institutionen för datavetenskap, Statistik och maskininlärning. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Salerud, Göran
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Salerud, Göran
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Classification of Short Time Series in Early Parkinson’s Disease With Deep Learning of Fuzzy Recurrence Plots2019Ingår i: IEEE/CAA Journal of Automatica Sinica, ISSN 2329-9266, Vol. 6, nr 6, s. 1306-1317Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    There are many techniques using sensors and wearable devices for detecting and monitoring patients with Parkinson's disease (PD). A recent development is the utilization of human interaction with computer keyboards for analyzing and identifying motor signs in the early stages of the disease. Current designs for classification of time series of computer-key hold durations recorded from healthy control and PD subjects require the time series of length to be considerably long. With an attempt to avoid discomfort to participants in performing long physical tasks for data recording, this paper introduces the use of fuzzy recurrence plots of very short time series as input data for the machine training and classification with long short-term memory (LSTM) neural networks. Being an original approach that is able to both significantly increase the feature dimensions and provides the property of deterministic dynamical systems of very short time series for information processing carried out by an LSTM layer architecture, fuzzy recurrence plots provide promising results and outperform the direct input of the time series for the classification of healthy control and early PD subjects.

  • 3.
    Pham, Tuan
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Deep learning of p73 biomarker expression in rectal cancer patients2019Konferensbidrag (Refereegranskat)
  • 4.
    Jin, Qiangguo
    et al.
    School of Computer Software, College of Intelligence and Computing, Tianjin University, Tianjin, China.
    Meng, Zhaopeng
    School of Computer Software, College of Intelligence and Computing, Tianjin University, Tianjin, China; Tianjin University of Traditional Chinese Medicine, Tianjin, China.
    Pham, Tuan
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Chen, Qi
    School of Computer Software, College of Intelligence and Computing, Tianjin University, Tianjin, China.
    Wei, Leyi
    School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China.
    Su, Ran
    School of Computer Software, College of Intelligence and Computing, Tianjin University, Tianjin, China.
    DUNet: A deformable network for retinal vessel segmentation2019Ingår i: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 178, s. 149-162Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Automatic segmentation of retinal vessels in fundus images plays an important role in the diagnosis of some diseases such as diabetes and hypertension. In this paper, we propose Deformable U-Net (DUNet), which exploits the retinal vessels’ local features with a U-shape architecture, in an end to end manner for retinal vessel segmentation. Inspired by the recently introduced deformable convolutional networks, we integrate the deformable convolution into the proposed network. The DUNet, with upsampling operators to increase the output resolution, is designed to extract context information and enable precise localization by combining low-level features with high-level ones. Furthermore, DUNet captures the retinal vessels at various shapes and scales by adaptively adjusting the receptive fields according to vessels’ scales and shapes. Public datasets: DRIVE, STARE, CHASE_DB1 and HRF are used to test our models. Detailed comparisons between the proposed network and the deformable neural network, U-Net are provided in our study. Results show that more detailed vessels can be extracted by DUNet and it exhibits state-of-the-art performance for retinal vessel segmentation with a global accuracy of 0.9566/0.9641/0.9610/0.9651 and AUC of 0.9802/0.9832/0.9804/0.9831 on DRIVE, STARE, CHASE_DB1 and HRF respectively. Moreover, to show the generalization ability of the DUNet, we use another two retinal vessel data sets, i.e., WIDE and SYNTHE, to qualitatively and quantitatively analyze and compare with other methods. Extensive cross-training evaluations are used to further assess the extendibility of DUNet. The proposed method has the potential to be applied to the early diagnosis of diseases.

  • 5.
    Pham, Tuan
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Fuzzy weighted recurrence networks of time series Chock2019Ingår i: Physica A: Statistical Mechanics and its Applications, ISSN 0378-4371, E-ISSN 1873-2119, Vol. 513, s. 409-417Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The concept of networks in the context of graph theory delineates a wide variety of real-life complex systems. The theory of networks finds its applications very useful in many scientific and intellectual domains. Weighted networks can characterize complex statistical graph properties, particularly where node connections are heterogeneous. A framework of fuzzy weighted recurrence networks of time series is presented in this letter. Popular graph measures including the average clustering coefficient and characteristic path length of fuzzy weighted recurrence networks are shown to be more robust than those of unweighted recurrence networks derived from binary recurrence plots. (C) 2018 Elsevier B.V. All rights reserved.

    Publikationen är tillgänglig i fulltext från 2020-09-08 10:45
  • 6.
    Pham, Tuan
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Geostatistical Simulation of Medical Images for Data Augmentation in Deep Learning2019Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 7, s. 68752-68763Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Data augmentation, which is the process for creating alternative copies of each sample in a small training data set, is important for extracting deep-learning features for medical image classification problems. However, data augmentation has not been well explored and existing methods are heuristic. In this paper, a geostatistical simulation of images is introduced as a data augmentation approach for extracting deep-learning features from medical images that are characterized with texture. The stochastic simulation procedure is to generate realizations of an image by modeling its spatial variability through the generation of multiple equiprobable stochastic realizations. The approach employed for the geostatistical simulation is based on the concepts of regionalized variables and kriging formulation to create multiple textural variations of medical images. Experimental results on classifying two medical-image data sets show that the use of geostatistical simulation for extracting deep-learning features with several popular pre-trained deep-learning models (AlexNet, ResNet-50, and GoogLeNet/Inception) provided better accuracy rates and more balanced results in terms of sensitivity and specificity than either with or without the implementation of conventional data augmentation. The proposed approach can be applied to other pre-trained networks as well as those without data pre-training for effective deep-feature extraction from medical images. The proposed application of the theory of geostatistics for medical image data augmentation in deep learning is original. The novelty of this approach can also be applied to many other types of data that are inherently textural. Geostatistical simulation opens a new door to the development of state-of-the-art artificial intelligence in feature extraction of medical images.

  • 7.
    Pham, Tuan
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Quantification of white matter lesions on brain MRI with 2D fuzzy weighted recurrence networks2019Konferensbidrag (Refereegranskat)
    Abstract [en]

    White matter lesions detected on magnetic resonance imaging scans of the brain have been hypothesized to have associations with the causes of several diseases. Accurate quantification of white matter lesions is important for the hypothesis validation. However, the clinical quantification is highly variable due to subjective opinions of different raters and is likely to compromise the reliability of the assessment. This paper introduces a new method of two-dimensional fuzzy weighted recurrence networks that can numerically express the quantity of white matter lesions. The results illustrate the promising application of the proposed method that offers as a useful computational tool in brain research.

  • 8.
    Cirillo, Marco Domenico
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Mirdell, Robin
    Linköpings universitet, Institutionen för klinisk och experimentell medicin, Avdelningen för Kirurgi, Ortopedi och Onkologi. Linköpings universitet, Medicinska fakulteten.
    Sjöberg, Folke
    Linköpings universitet, Institutionen för klinisk och experimentell medicin, Avdelningen för Kirurgi, Ortopedi och Onkologi. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Sinnescentrum, Hand- och plastikkirurgiska kliniken US.
    Pham, Tuan
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Tensor Decomposition for Colour Image Segmentation of Burn Wounds2019Ingår i: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 9, artikel-id 3291Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Research in burns has been a continuing demand over the past few decades, and important advancements are still needed to facilitate more effective patient stabilization and reduce mortality rate. Burn wound assessment, which is an important task for surgical management, largely depends on the accuracy of burn area and burn depth estimates. Automated quantification of these burn parameters plays an essential role for reducing these estimate errors conventionally carried out by clinicians. The task for automated burn area calculation is known as image segmentation. In this paper, a new segmentation method for burn wound images is proposed. The proposed methods utilizes a method of tensor decomposition of colour images, based on which effective texture features can be extracted for classification. Experimental results showed that the proposed method outperforms other methods not only in terms of segmentation accuracy but also computational speed.

  • 9.
    Pham, Tuan
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Tensor decomposition of non-EEG physiological signals for visualization and recognition of human stress2019Konferensbidrag (Refereegranskat)
  • 10.
    Cirillo, Marco Domenico
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Mirdell, Robin
    Linköpings universitet, Institutionen för klinisk och experimentell medicin, Avdelningen för Kirurgi, Ortopedi och Onkologi. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Sinnescentrum, Hand- och plastikkirurgiska kliniken US.
    Sjöberg, Folke
    Linköpings universitet, Institutionen för klinisk och experimentell medicin, Avdelningen för Kirurgi, Ortopedi och Onkologi. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Sinnescentrum, Hand- och plastikkirurgiska kliniken US.
    Pham, Tuan
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Time-Independent Prediction of Burn Depth using Deep Convolutional Neural Networks2019Ingår i: Journal of Burn Care & Research, ISSN 1559-047X, E-ISSN 1559-0488, Vol. 40, nr 6, s. 857-863Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We present in this paper the application of deep convolutional neural networks, which are a state-of-the-art artificial intelligence (AI) approach in machine learning, for automated time-independent prediction of burn depth. Colour images of four types of burn depth injured in first few days, including normal skin and background, acquired by a TiVi camera were trained and tested with four pre-trained deep convolutional neural networks: VGG-16, GoogleNet, ResNet-50, and ResNet-101. In the end, the best 10-fold cross-validation results obtained from ResNet- 101 with an average, minimum, and maximum accuracy are 81.66%, 72.06% and 88.06%, respectively; and the average accuracy, sensitivity and specificity for the four different types of burn depth are 90.54%, 74.35% and 94.25%, respectively. The accuracy was compared to the clinical diagnosis obtained after the wound had healed. Hence, application of AI is very promising for prediction of burn depth and therefore can be a useful tool to help in guiding clinical decision and initial treatment of burn wounds.

    Publikationen är tillgänglig i fulltext från 2020-06-11 08:35
  • 11.
    Pham, Tuan
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Yan, Hong
    College of Science and Engineering, City University of Hong Kong, Kowloon, Hong Kong.
    A regularity statistic for images2018Ingår i: Chaos, Solitons & Fractals, ISSN 0960-0779, E-ISSN 1873-2887, Vol. 106, s. 227-232Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Measures of statistical regularity or complexity for time series are pervasive in many fields of research and applications, but relatively little effort has been made for image data. This paper presents a method for quantifying the statistical regularity in images. The proposed method formulates the entropy rate of an image in the framework of a stationary Markov chain, which is constructed from a weighted graph derived from the Kullback–Leibler divergence of the image. The model is theoretically equal to the well-known approximate entropy (ApEn) used as a regularity statistic for the complexity analysis of one-dimensional data. The mathematical formulation of the regularity statistic for images is free from estimating critical parameters that are required for ApEn.

  • 12.
    Chan, Yung-Kuan
    et al.
    Natl Chung Hsing Univ, Taiwan.
    Chen, Yung-Fu
    Cent Taiwan Univ Sci and Technol, Taiwan.
    Pham, Tuan
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Chang, Weide
    Calif State Univ Sacramento, CA 95819 USA.
    Hsieh, Ming-Yuan
    Natl Taichung Univ Educ, Taiwan.
    Artificial Intelligence in Medical Applications2018Ingår i: Journal of Healthcare Engineering, ISSN 2040-2295, E-ISSN 2040-2309, artikel-id 4827875Artikel i tidskrift (Övrigt vetenskapligt)
    Abstract [en]

    n/a

  • 13.
    Liu, Jin
    et al.
    School of Computer Science, China University of Mining and Technology, Xuzhou, Jiangsu, China.
    Pham, Tuan
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Yan, Hong
    Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong.
    Liang, Zhizhen
    School of Computer Science, China University of Mining and Technology, Xuzhou, Jiangsu, China.
    Fuzzy mixed-prototype clustering algorithm for microarray data analysis2018Ingår i: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 276, s. 42-54Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Being motivated by combining the advantages of hyperplane-based pattern analysis and fuzzy clustering techniques, we present in this paper a fuzzy mix-prototype (FMP) clustering for microarray data analysis. By integrating spherical and hyper-planar cluster prototypes, the FMP is capable of capturing latent data models with both spherical and non-spherical geometric structures. Our contributions of the paper can be summarized into three folds: first, the objective function of the FMP is formulated. Second, an iterative solution which minimizes the objective function under given constraints is derived. Third, the effectiveness of the proposed FMP is demonstrated through experiments on yeast and leukemia data sets.

  • 14.
    Pham, Tuan
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Nonlinear dynamics analysis of short-time photoplethysmogram in Parkinson's disease2018Konferensbidrag (Refereegranskat)
  • 15.
    Pham, Tuan
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Oyama-Higa, Mayumi
    Chaos Technol Res Lab, Japan.
    NONLINEAR DYNAMICS ANALYSIS OF SHORT-TIME PHOTOPLETHYSMOGRAM IN PARKINSONS DISEASE2018Ingår i: 2018 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE), IEEE , 2018Konferensbidrag (Refereegranskat)
    Abstract [en]

    Photoplethysmogram (PPG) signals obtained from wearable sensors have been utilized for monitoring health conditions in both clinical and non-clinical environments, mostly concerning with heart-rate events. This paper shows the potential use of short-time PPG signals for differentiating patients with Parkinsons disease (PD) from healthy control (HC) subjects with nonlinear dynamics analysis. Multiscale entropy, time-shift multiscale entropy, and fuzzy recurrence plots were applied for extracting features from PPG signals of PD patients and HC subjects. Least-square support vector machine based cross-validations of the features extracted from the three nonlinear dynamics analysis methods achieve high classification rates, where those obtained from fuzzy recurrence plots are the highest.

  • 16.
    Pham, Tuan
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Pattern analysis and classification of blood oxygen saturation signals with nonlinear dynamics features2018Konferensbidrag (Refereegranskat)
    Abstract [en]

    Pattern analysis of blood oxygen saturation is important for gaining insights into the cardiorespiratory control system, real-time monitoring during operations, identifying potential predictors for the diagnosis of disease severity, and improving the hospitalization of patients with critical chronic diseases. This paper investigates the use of nonlinear dynamics features for machine learning and classification of blood oxygen saturation signals in healthy young and healthy old subjects. The validation of the feature reliability for the signal variability analysis has a clinical implication for differentiating blood oxygen saturation in patients with respect to the particular influence of aging, when patient's data become available.

  • 17.
    Pham, Tuan
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Pattern analysis of computer keystroke time series in healthy control and early-stage Parkinson's disease subjects using fuzzy recurrence and scalable recurrence network features2018Ingår i: Journal of Neuroscience Methods, ISSN 0165-0270, E-ISSN 1872-678X, Vol. 307, s. 128-130Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A noncommutative algebra corresponding to the classical catenoid is introduced together with a differential calculus of derivations. We prove that there exists a unique metric and torsion-free connection that is compatible with the complex structure, and the curvature is explicitly calculated. A noncommutative analogue of the fact that the catenoid is a minimal surface is studied by constructing a Laplace operator from the connection and showing that the embedding coordinates are harmonic. Furthermore, an integral is defined and the total curvature is computed. Finally, classes of left and right modules are introduced together with constant curvature connections, and bimodule compatibility conditions are discussed in detail.

  • 18.
    Pham, Tuan
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Yan, Hong
    Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong.
    Spatial-dependence recurrence sample entropy2018Ingår i: Physica A: Statistical Mechanics and its Applications, ISSN 0378-4371, E-ISSN 1873-2119, Vol. 494, s. 581-590Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Measuring complexity in terms of the predictability of time series is a major area of research in science and engineering, and its applications are spreading throughout many scientific disciplines, where the analysis of physiological signals is perhaps the most widely reported in literature. Sample entropy is a popular measure for quantifying signal irregularity. However, the sample entropy does not take sequential information, which is inherently useful, into its calculation of sample similarity. Here, we develop a method that is based on the mathematical principle of the sample entropy and enables the capture of sequential information of a time series in the context of spatial dependence provided by the binary-level co-occurrence matrix of a recurrence plot. Experimental results on time-series data of the Lorenz system, physiological signals of gait maturation in healthy children, and gait dynamics in Huntington’s disease show the potential of the proposed method.

  • 19.
    Pham, Tuan
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Yan, Hong
    City University of Hong Kong, Hong Kong.
    Tensor Decomposition of Gait Dynamics in Parkinson's Disease2018Ingår i: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. 65, nr 8, s. 1820-1827Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Objective: The study of gait in Parkinson's disease is important because it can provide insights into the complex neural system and physiological behaviors of the disease, of which understanding can help improve treatment and lead to effective developments of alternative neural rehabilitation programs. This paper aims to introduce an effective computational method for multi-channel or multi-sensor data analysis of gait dynamics in Parkinson's disease.

    Method: A model of tensor decomposition, which is a generalization of matrix-based analysis for higher dimensional analysis, is designed for differentiating multi-sensor time series of gait force between Parkinson's disease and healthy control cohorts.

    Results: Experimental results obtained from the tensor decomposition model using a PhysioNet database show several discriminating characteristics of the two cohorts, and the achievement of 100% sensitivity and 100% specificity under various cross-validations.

    Conclusion: Tensor decomposition is a useful method for the modeling and analysis of multi-sensor time series in patients with Parkinson's disease.

    Significance: Tensor-decomposition factors can be potentially used as physiological markers for Parkinson's disease, and effective features for machine learning that can provide early prediction of the disease progression.

  • 20.
    Pham, Tuan
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Texture Classification and Visualization of Time Series of Gait Dynamics in Patients with Neuro-Degenerative Diseases2018Ingår i: IEEE transactions on neural systems and rehabilitation engineering, ISSN 1534-4320, E-ISSN 1558-0210, Vol. 26, nr 1, s. 188-196Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The analysis of gait dynamics is helpful for predicting and improving the quality of life, morbidity, and mortality in neuro-degenerative patients. Feature extraction of physiological time series and classification between gait patterns of healthy control subjects and patients are usually carried out on the basis of 1-D signal analysis. The proposed approach presented in this paper departs itself from conventional methods for gait analysis by transforming time series into images, of which texture features can be extracted from methods of texture analysis. Here, the fuzzy recurrence plot algorithm is applied to transform gait time series into texture images, which can be visualized to gain insight into disease patterns. Several texture features are then extracted from fuzzy recurrence plots using the gray-level co-occurrence matrix for pattern analysis and machine classification to differentiate healthy control subjects from patients with Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis. Experimental results using only the right stride-intervals of the four groups show the effectiveness of the application of the proposed approach.

  • 21.
    Pham, Tuan
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Karlsson, Matilda
    Region Östergötland, Sinnescentrum, Hand- och plastikkirurgiska kliniken US.
    Andersson, Caroline M.
    Region Östergötland, Sinnescentrum, Hand- och plastikkirurgiska kliniken US.
    Mirdell, Robin
    Linköpings universitet, Institutionen för klinisk och experimentell medicin, Avdelningen för Kirurgi, Ortopedi och Onkologi. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Sinnescentrum, Hand- och plastikkirurgiska kliniken US.
    Sjöberg, Folke
    Linköpings universitet, Institutionen för klinisk och experimentell medicin, Avdelningen för Kirurgi, Ortopedi och Onkologi. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Sinnescentrum, Hand- och plastikkirurgiska kliniken US.
    Automated VSS-based Burn Scar Assessment using Combined Texture and Color Features of Digital Images in Error-Correcting Output Coding2017Ingår i: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 7, artikel-id 16744Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Assessment of burn scars is an important study in both medical research and clinical settings because it can help determine response to burn treatment and plan optimal surgical procedures. Scar rating has been performed using both subjective observations and objective measuring devices. However, there is still a lack of consensus with respect to the accuracy, reproducibility, and feasibility of the current methods. Computerized scar assessment appears to have potential for meeting such requirements but has been rarely found in literature. In this paper an image analysis and pattern classifcation approach for automating burn scar rating based on the Vancouver Scar Scale (VSS) was developed. Using the image data of pediatric patients, a rating accuracy of 85% was obtained, while 92% and 98% were achieved for the tolerances of one VSS score and two VSS scores, respectively. The experimental results suggest that the proposed approach is very promising as a tool for clinical burn scar assessment that is reproducible and cost-efective.

  • 22.
    Zhong, Guoqiang
    et al.
    Ocean University of China, Qingdao, China.
    Yao, Hui
    Ocean University of China, Qingdao, China.
    Liu, Yutong
    Ocean University of China, Qingdao, China.
    Hong, Chen
    Ocean University of China, Qingdao, China.
    Pham, Tuan
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska fakulteten.
    Classification of photographed document images based on deep-learning features2017Ingår i: Eighth International Conference on Graphic and Image Processing (ICGIP 2016) / [ed] Tuan D. Pham; Vit Vozenilek; Zhu Zeng, SPIE - International Society for Optical Engineering, 2017, Vol. 10225, artikel-id UNSP 102250XKonferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper, we propose two new problems related to classification of photographed document images, and based on deep learning methods, present the baseline solutions for these two problems. The first problem is that, for some photographed document images, which book do they belong to? The second one is, for some photographed document images, what is the type of the book they belong to? To address these two problems, we apply “AexNet” to the collected document images. Using the pre-trained “AlexNet” on the ImageNet data set directly, we obtain 92.57% accuracy for the book-name classification and 93.33% accuracy for the book-type one. After fine-tuning on the training set of the photographed document images, the accuracy of the book-name classification increases to 95.54% and that of the booktype one to 95.42%. To our best knowledge, although there exist many image classification algorithm, no previous work has targeted to these two challenging problems. In addition, the experiments demonstrate that deep-learning features outperform features extracted with traditional image descriptors on these two problems. 

  • 23.
    Pham, Tuan
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Complementary features for radiomic analysis of malignant and benign mediastinal lymph nodes2017Konferensbidrag (Refereegranskat)
  • 24.
    Pham, Tuan
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    COMPLEMENTARY FEATURES FOR RADIOMIC ANALYSIS OF MALIGNANT AND BENIGN MEDIASTINAL LYMPH NODES2017Ingår i: 2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), IEEE , 2017, s. 3849-3853Konferensbidrag (Refereegranskat)
    Abstract [en]

    The importance of developing effective strategies for investigating mediastinal lymph-node metastases in non-small cell lung cancers is increasingly emphasized. It is because the precise detection of this metastatic disease is critical for optimal surgical intervention and treatment for patients with lung cancer. Existing medical image analysis is of limited power for mediastinal lymph-node staging on computed tomography (CT). Motivated by the radiomics hypothesis, this paper explored deep-learning, texture features and their combinations to ascertain subtle difference between malignant and benign mediastinal lymph nodes on CT. The radiomics-based results are found to be promising for differentiating malignant from benign mediastinal lymph nodes of patients with lung cancer.

  • 25.
    Pham, Tuan
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    From fuzzy recurrence plots to scalable recurrence networks of time series2017Ingår i: Europhysics letters, ISSN 0295-5075, E-ISSN 1286-4854, Vol. 118, artikel-id 20003Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Recurrence networks, which are derived from recurrence plots of nonlinear time series, enable the extraction of hidden features of complex dynamical systems. Because fuzzy recurrence plots are represented as grayscale images, this paper presents a variety of texture features that can be extracted from fuzzy recurrence plots. Based on the notion of fuzzy recurrence plots, defuzzified, undirected, and unweighted recurrence networks are introduced. Network measures can be computed for defuzzified recurrence networks that are scalable to meet the demand for the network-based analysis of big data.

  • 26.
    Pham, Tuan
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Noise-added texture analysis2017Ingår i: PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS, CIARP 2016 / [ed] Beltrán-Castañón, Cesar, Nyström, Ingela, Famili, Fazel, Springer, 2017, s. 93-100Konferensbidrag (Refereegranskat)
    Abstract [en]

    Noise is unwanted signal that causes a major problem for the task of image classification and retrieval. However, this paper reports that adding noise to texture at certain levels can improve classification performance without training data. The proposed method was tested with images of different texture categories degraded with various noise types: Gaussian (additive), salt-and-pepper (impulsive), and speckle (multiplicative). Experimental results suggest that the inclusion of noise can be useful for extracting texture features for image retrieval.

  • 27.
    Pham, Tuan
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Scaling of Texture in Training Autoencoders for Classification of Histological Images of Colorectal Cancer2017Ingår i: ADVANCES IN NEURAL NETWORKS, PT II, SPRINGER INTERNATIONAL PUBLISHING AG , 2017, Vol. 10262, s. 524-532Konferensbidrag (Refereegranskat)
    Abstract [en]

    Autoencoding in deep learning has been known as a useful tool for extracting image features in multiple layers, which are subsequently configured for classification by deep neural networks. A practical burden for the implementation of autoencoders is the time required for training a large number of artificial neurons. This paper shows the effects of scaling of texture in the histology of colorectal cancer, which can result in significant training time reduction being approximately to an exponential function, with improved classification rates.

  • 28.
    Zhou, Xiaowei
    et al.
    Ocean University of China, China.
    Zhong, Guoqiang
    Ocean University of China, China.
    Qi, Lin
    Ocean University of China, China.
    Dong, Junyu
    Ocean University of China, China.
    Pham, Tuan
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Mao, Jianzhou
    Macau Univ. of Science and Technology, China.
    Surface height map estimation from a single image using convolutional neural networks2017Ingår i: Proceedings of SPIE, Eighth International Conference on Graphic and Image Processing, SPIE - International Society for Optical Engineering, 2017, Vol. 10225, artikel-id UNSP 1022524-1Konferensbidrag (Refereegranskat)
  • 29.
    Pham, Tuan
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Watanabe, Yuzuru
    Fukushima Medical University, Japan.
    Higuchi, Mitsunori
    Fukushima Medical University, Japan.
    Suzuki, Hiroyuki
    Fukushima Medical University, Japan.
    Texture Analysis and Synthesis of Malignant and Benign Mediastinal Lymph Nodes in Patients with Lung Cancer on Computed Tomography2017Ingår i: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 7, artikel-id 43209Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Texture analysis of computed tomography (CT) imaging has been found useful to distinguish subtle differences, which are in-visible to human eyes, between malignant and benign tissues in cancer patients. This study implemented two complementary methods of texture analysis, known as the gray-level co-occurrence matrix (GLCM) and the experimental semivariogram (SV) with an aim to improve the predictive value of evaluating mediastinal lymph nodes in lung cancer. The GLCM was explored with the use of a rich set of its derived features, whereas the SV feature was extracted on real and synthesized CT samples of benign and malignant lymph nodes. A distinct advantage of the computer methodology presented herein is the alleviation of the need for an automated precise segmentation of the lymph nodes. Using the logistic regression model, a sensitivity of 75%, specificity of 90%, and area under curve of 0.89 were obtained in the test population. A tenfold cross-validation of 70% accuracy of classifying between benign and malignant lymph nodes was obtained using the support vector machines as a pattern classifier. These results are higher than those recently reported in literature with similar studies.

  • 30.
    Pham, Tuan
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten. Linkoping University.
    Time-shift multiscale entropy analysis of physiological signals2017Ingår i: Entropy, ISSN 1099-4300, E-ISSN 1099-4300, Vol. 19, nr 6, artikel-id 257Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Measures of predictability in physiological signals using entropy measures have been widely applied in many areas of research. Multiscale entropy expresses different levels of either approximate entropy or sample entropy by means of multiple factors for generating multiple time series, enabling the capture of more useful information than using a scalar value produced by the two entropy methods. This paper presents the use of different time shifts on various intervals of time series to discover different entropy patterns of the time series. Examples and experimental results using white noise, 1/ f noise, photoplethysmography, and electromyography signals suggest the validity and better performance of the proposed time-shift multiscale entropy analysis of physiological signals than the multiscale entropy.

  • 31.
    Pham, Tuan D
    Aizu Research Cluster for Medical Engineering and Informatics, Research Center for Advanced Information Science and Technology, The University of Aizu, Aizuwakamatsu, Fukushima 965-8580, Japan.
    Validation of computer models for evaluating the efficacy of cognitive stimulation therapy2017Ingår i: Wireless personal communications, ISSN 0929-6212, E-ISSN 1572-834X, Vol. 94, nr 3, s. 301-314Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The notion of using computational methods for evaluating cognitive stimulation therapy (CST) based on the synchronized recording of photoplethysmographic (PPG) signals of care-givers and participants offers an objective and cost-effective analysis in health care to improve the patient’s quality of life. While computer models are promising as a useful tool for such a purpose, a question of interest is how the model reliability, which is the degree to which an assessment tool produces stable and consistent results, can be established. This paper addresses this issue with the application of dynamic-time warping and resampling to measure the performance of two PPG features known as the largest Lyapunov exponent and linear predictive coding, which have been applied for studying the efficacy of CST. The potential success of this computerized evaluation can be a precursor to the development of a personalized e-therapy system that operates on mobile devices.

  • 32.
    Pham, Tuan
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Wada, Ikuo
    Fukushima Medical University, Japan.
    Chaos analysis of ER-network dynamics in microscopy imaging2016Ingår i: Handbook of applications of chaos theory / [ed] Christos H. Skiadas, Charilaos Skiadas, Boca Raton, FL, USA: CRC Press, 2016, s. 253-270Kapitel i bok, del av antologi (Refereegranskat)
  • 33.
    Su, R.
    et al.
    Tianjin University, Peoples R China.
    Zhang, C.
    CSIRO Data61, Australia; University of New South Wales, Australia.
    Pham, Tuan
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Davey, R.
    CSIRO Food and Nutr, Australia.
    Bischof, L.
    CSIRO Data61, Australia.
    Vallotton, P.
    CSIRO Data61, Australia; ETH, Switzerland.
    Lovell, D.
    CSIRO Data61, Australia; Queensland University of Technology, Australia.
    Hope, S.
    CSIRO Food and Nutr, Australia.
    Schmoelzl, S.
    CSIRO Food and Nutr, Australia.
    Sun, C.
    CSIRO Data61, Australia.
    Detection of tubule boundaries based on circular shortest path and polar-transformation of arbitrary shapes2016Ingår i: Journal of Microscopy, ISSN 0022-2720, E-ISSN 1365-2818, Vol. 264, nr 2, s. 127-142Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In studies of germ cell transplantation, counting cells and measuring tubule diameters from different populations using labelled antibodies are important measurement processes. However, it is slow and sanity grinding to do these tasks manually. This paper proposes a way to accelerate these processes using a new image analysis framework based on several novel algorithms: centre points detection of tubules, tubule shape classification, skeleton-based polar-transformation, boundary weighting of polar-transformed image, and circular shortest path smoothing. The framework has been tested on a dataset consisting of 27 images which contain a total of 989 tubules. Experiments show that the detection results of our algorithm are very close to the results obtained manually and the novel approach can achieve a better performance than two existing methods. Lay description In studies of germ cell transplantation, counting cells and measuring tubule diameters from different populations using labelled antibodies are important measurement processes. However, it is slow and sanity grinding to do these tasks manually. This paper proposes a way to accelerate these processes using a new image analysis framework based on several novel algorithms: center points detection of tubules, tubule shape classification, skeleton based polar-transformation, boundary weighting of polar-transformed image, and circular shortest path smoothing. The framework has been tested on a dataset consisting of 27 images which contain a total of 989 tubules. Experiments show that the detection results of our algorithm are very close to the results obtained manually and the novel approach can achieve a better performance than two existing methods.

  • 34.
    Tan, Xiao
    et al.
    Department of Computer Science, University of Hong Kong, Hong Kong.
    Sun, Changming
    CSIRO Digital Productivity, Sydney, NSW, Australia.
    Pham, Tuan
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Edge-aware filtering with local polynomial approximation and rectangle based weighting2016Ingår i: IEEE Transactions on Cybernetics, ISSN 2168-2267, E-ISSN 2168-2275, Vol. 46, nr 12, s. 2693-2705Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper presents a novel method for performing guided image filtering using local polynomial approximation (LPA) with range guidance. In our method, the LPA is introduced into a multipoint framework for reliable model regression and better preservation on image spatial variation which usually contains the essential information in the input image. In addition, we develop a weighting scheme which has the spatial flexibility during the filtering process. All components in our method are efficiently implemented and a constant computation complexity is achieved. Compared with conventional filtering methods, our method provides clearer boundaries and performs especially better in recovering spatial variation from noisy images. We conduct a number of experiments for different applications: depth image upsampling, joint image denoising, details enhancement, and image abstraction. Both quantitative and qualitative comparisons demonstrate that our method outperforms state-of-the-art methods.

  • 35.
    Pham, Tuan
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska fakulteten.
    Enhancing texture characteristics with synthesis and noise for image retrieval2016Ingår i: IEEE 8th International Conference on Intelligent Systems (IS), 2016, IEEE, 2016, s. 433-437Konferensbidrag (Refereegranskat)
    Abstract [en]

    Texture images of limited size can be insufficient for statistical learning to perform the task of image retrieval. This paper proposes a hypothesis that the utilization of synthesized texture and noise addition can improve texture analysis with spatial statistics. Rationales for this hypothesis are that texture synthesis allows the enlargement of an image size for better description of its spatial statistics, and noise added to texture pixels at some levels enhances discriminative power of texture randomness. The improvements using synthesized images of coarse-aperiodic and fine-periodic texture categories with added noise for feature extraction with the semi-variogram and feature-vector matching using the log-likelihood ratio suggest the validation of the proposed hypothesis that is promising for handling classification of texture of small sample sizes.

  • 36.
    Pham, Tuan D.
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska fakulteten.
    Entropy rates of physiological aging on microscopy2016Ingår i: Proceedings of 2016 IEEE Symposium Series on Computational Intelligence, Institute of Electrical and Electronics Engineers (IEEE), 2016Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents a method for computing entropy rates of images by modeling  a stationary Markov chain constructed from a weighted graph. The  proposed method was applied to the quantification of the complex behavior of the growing rates of physiological aging of Caenorhabditis elegans (C. elegans) on microscopic images, which has been considered as one of the most challenging problems in the search for metrics that can be used for identifying differences among stages in high- throughput and high-content images of physiological aging.

  • 37.
    Pham, Tuan
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska fakulteten.
    Fuzzy recurrence plots2016Ingår i: Europhysics letters, ISSN 0295-5075, E-ISSN 1286-4854, Vol. 116, s. p1-p5, artikel-id 50008Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Recurrence plots display binary texture of time series from dynamical systems with single dots and line structures. Using fuzzy recurrence plots, recurrences of the phase-space states can be visualized as grayscale texture, which is more informative for pattern analysis. The proposed method replaces the crucial similarity threshold required by symmetrical recurrence plots with the number of cluster centers, where the estimate of the latter parameter is less critical than the estimate of the former.

  • 38.
    Tan, Xiao
    et al.
    The University of Hong Kong, Pokfulam, Hong Kong.
    Sun, Changming
    CSIRO Computational Informatics, North Ryde, NSW, Australia.
    Wong, Kwan-YeeK.
    The University of Hong Kong, Pokfulam, Hong Kong.
    Pham, Tuan
    Aizu Research Cluster for Medical Engineering and Informatics, The University of Aizu, Fukushima, Japan.
    Guided image completion by confidence propagation2016Ingår i: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 50, s. 210-222Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper presents a new guided image completion method which fills any missing values by considering information from a guidance image. We develop a confidence propagation scheme that allows the filling process to be carried out simultaneously without the need of deciding the filling order. We conduct experiments in several applications where the problem can be formulated into a guided image completion problem, such as interactive segmentation and colorization. The experimental results show that our method provides good results and succeeds in situations where conventional methods fail. In addition, as all building blocks are parallel processes, our method is much suitable for hardware acceleration.

  • 39.
    Pham, Tuan
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska fakulteten.
    Measures of spatial distortion using kriging2016Ingår i: IEEE 8th International Conference on Intelligent Systems (IS), 2016 / [ed] Ronald Yager, Vassil Sgurev, Mincho Hadjiski, Vladimir Jotsov, Institute of Electrical and Electronics Engineers (IEEE), 2016, s. 438-442Konferensbidrag (Refereegranskat)
    Abstract [en]

    Texture images of limited size can be insufficient for statistical learning to perform the task of image retrieval. This paper proposes a hypothesis that the utilization of synthesized texture and noise addition can improve texture analysis with spatial statistics. Rationales for this hypothesis are that texture synthesis allows the enlargement of an image size for better description of its spatial statistics, and noise added to texture pixels at some levels enhances discriminative power of texture randomness. The improvements using synthesized images of coarse-aperiodic and fine-periodic texture categories with added noise for feature extraction with the semi-variogram and feature-vector matching using the log-likelihood ratio suggest the validation of the proposed hypothesis that is promising for handling classification of texture of small sample sizes.

  • 40.
    Pham, Tuan
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Quantifying visual perception of texture with fuzzy metric entropy2016Ingår i: Journal of Intelligent & Fuzzy Systems, ISSN 1064-1246, E-ISSN 1875-8967, Vol. 31, nr 2, s. 1089-1097Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The quantitative categorization of textures according to their visual appearances is an important area of research in computer vision and image understanding, because texture analysis and its applications are found useful in many areas of health, medicine, sciences, and engineering. For the first time, the theory of chaos and fuzzy sets are applied in this paper to measure the spatial dynamics of the texture spectrum. Experiments carried out on the well-known Brodatz texture database suggest the promising application of the method proposed for texture quantification.

  • 41.
    Pham, Tuan
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    The Kolmogorov-Sinai entropy in the setting of fuzzy sets for image texture analysis and classification2016Ingår i: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 53, s. 229-237Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The Kolmogorov–Sinai (K–S) entropy is used to quantify the average amount of uncertainty of a dynamical system through a sequence of observations. Sequence probabilities therefore play a central role for the computation of the entropy rate to determine if the dynamical system under study is deterministically non-chaotic, deterministically chaotic, or random. This paper extends the notion of the K–S entropy to measure the entropy rate of imprecise systems using sequence membership grades, in which the underlying deterministic paradigm is replaced with the degree of fuzziness. While constructing sequential probabilities for the calculation of the K–S entropy is difficult in practice, the estimate of the K–S entropy in the setting of fuzzy sets in an image is feasible and can be useful for modeling uncertainty of pixel distributions in images. The fuzzy K–S entropy is illustrated as an effective feature for image analysis and texture classification.

  • 42.
    Pham, Tuan
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    The multiple-point variogram of images for robust texture classification2016Ingår i: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE , 2016, s. 1303-1307Konferensbidrag (Refereegranskat)
    Abstract [en]

    Most texture analysis techniques require training data to perform classification or retrieval of images. In many practical situations, the amount of data representing different texture classes can be too limited to satisfy the training of a reliable classifier. Therefore, finding an effective feature of texture is very useful to cope with a variety of applications. This paper presents the extension of the two-point variogram to multiple-point variogram of images for texture feature extraction, which is also robust to noise and computationally economic. The matching of the variogram functions for pattern classification can be enhanced with the use of a spectral distortion measure without the requirement of training data. Experimental results and comparison with other methods, which require training data, suggest the usefulness of the proposed approach.

  • 43.
    Pham, Tuan D.
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    The semi-variogram and spectral distortion measures for image texture retrieval2016Ingår i: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042, Vol. 25, nr 4, s. 1556-1565Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Semi-variogram estimators and distortion measures of signal spectra are utilized in this paper for image texture retrieval. On the use of the complete Brodatz database, most high retrieval rates are reportedly based on multiple features, and the combinations of multiple algorithms; while the classification using single features is still a challenge to the retrieval of diverse texture images. The semi-variogram, which is theoretically sound and the cornerstone of spatial statistics, has the characteristics shared between true randomness and complete determinism; and therefore can be used as a useful tool for both structural and statistical analysis of texture images. Meanwhile, spectral distortion measures derived from the theory of linear predictive coding provide a rigorously mathematical model for signal-based similarity matching, and have been proven useful for many practical pattern classification systems. Experimental results obtained from testing the proposed approach using the complete Brodatz database, and the UIUC texture database suggest the effectiveness of the proposed approach as a single-feature-based dissimilarity measure for real-time texture retrieval.

  • 44.
    Zhang, Chao
    et al.
    School of Engineering and Information Technology, The University of New South Wales, Canberra, Australia; CSIRO Computational Informatics, North Ryde, Australia.
    Sun, Changming
    CSIRO Computational Informatics, North Ryde, Australia.
    Su, Ran
    Bioinformatics Institute, Matrix, Singapore.
    Pham, Tuan D
    Aizu Research Cluster for Medical Engineering and Informatics, Research Center for Advanced Information Science and Technology, The University of Aizu, Fukushima, Japan.
    Clustered nuclei splitting via curvature information and gray-scale distance transform2015Ingår i: Journal of Microscopy, ISSN 0022-2720, E-ISSN 1365-2818, Vol. 259, nr 1, s. 36-52Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Clusters or clumps of cells or nuclei are frequently observed in two dimensional images of thick tissue sections. Correct and accurate segmentation of overlapping cells and nuclei is important for many biological and biomedical applications. Many existing algorithms split clumps through the binarization of the input images; therefore, the intensity information of the original image is lost during this process. In this paper, we present a curvature information, gray scale distance transform, and shortest path splitting line-based algorithm which can make full use of the concavity and image intensity information to find out markers, each of which represents an individual object, and detect accurate splitting lines between objects using shortest path and junction adjustment. The proposed algorithm is tested on both synthetic and real nuclei images. Experiment results show that the performance of the proposed method is better than that of marker-controlled watershed method and ellipse fitting method.

  • 45.
    Pham, Tuan D
    et al.
    Aizu Research Cluster for Medical Engineering and Informatics, Center for Advanced Information Science and Technology, The University of Aizu, Aizu-Wakamatsu, Japan.
    Oyama-Higa, Mayumi
    Chaos Technology Research Lab, Shiga, Japan.
    Truong, Cong-Thang
    School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu, Japan.
    Okamoto, Kazushi
    School of Nursing and Health, Aichi Prefectural University, Aichi, Japan.
    Futaba, Terufumi
    Faculty of Intercultural Communication, Ryukoku University, Shiga, Japan.
    Kanemoto, Shigeru
    School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu, Japan.
    Sugiyama, Masahide
    School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu, Japan.
    Lampe, Lisa
    Discipline of Psychiatry, Sydney Medical School, The University of Sydney, Sydney, Australia.
    Computerized assessment of communication for cognitive stimulation for people with cognitive decline using spectral-distortion measures and phylogenetic inference2015Ingår i: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 10, nr 3, s. 1-29, artikel-id e0118739Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Therapeutic communication and interpersonal relationships in care homes can help people to improve their mental wellbeing. Assessment of the efficacy of these dynamic and complex processes are necessary for psychosocial planning and management. This paper presents a pilot application of photoplethysmography in synchronized physiological measurements of communications between the care-giver and people with dementia. Signal-based evaluations of the therapy can be carried out using the measures of spectral distortion and the inference of phylogenetic trees. The proposed computational models can be of assistance and cost-effectiveness in caring for and monitoring people with cognitive decline.

  • 46.
    Arganda-Carreras, Ignacio
    et al.
    Institute Jean Pierre Bourgin, France.
    Turaga, Srinivas C.
    Howard Hughes Medical Institute, VA USA.
    Berger, Daniel P.
    Harvard University, MA 02138 USA.
    Ciresan, Dan
    Scuola University of Profess Svizzera Italiana, Switzerland.
    Giusti, Alessandro
    Scuola University of Profess Svizzera Italiana, Switzerland.
    Gambardella, Luca M.
    Scuola University of Profess Svizzera Italiana, Switzerland.
    Schmidhuber, Juergen
    Scuola University of Profess Svizzera Italiana, Switzerland.
    Laptev, Dmitry
    ETH, Switzerland.
    Dwivedi, Sarvesh
    ETH, Switzerland.
    Buhmann, Joachim M.
    ETH, Switzerland.
    Liu, Ting
    University of Utah, UT USA.
    Seyedhosseini, Mojtaba
    University of Utah, UT USA.
    Tasdizen, Tolga
    University of Utah, UT USA.
    Kamentsky, Lee
    Broad Institute, MA USA.
    Burget, Radim
    Brno University of Technology, Czech Republic.
    Uher, Vaclav
    Brno University of Technology, Czech Republic.
    Tan, Xiao
    University of New S Wales, Australia.
    Sun, Changming
    CSIRO, Australia.
    Pham, Tuan
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Bas, Erhan
    Howard Hughes Medical Institute, VA USA.
    Uzunbas, Mustafa G.
    Rutgers State University, NJ 08903 USA.
    Cardona, Albert
    Howard Hughes Medical Institute, VA USA.
    Schindelin, Johannes
    University of Wisconsin, WI USA.
    Sebastian Seung, H.
    Princeton University, NJ 08544 USA; Princeton University, NJ 08544 USA.
    Crowdsourcing the creation of image segmentation algorithms for connectomics2015Ingår i: Frontiers in Neuroanatomy, ISSN 1662-5129, E-ISSN 1662-5129, Vol. 9, nr 142Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    To stimulate progress in automating the reconstruction of neural circuits, we organized the first international challenge on 2D segmentation of electron microscopic (EM) images of the brain. Participants submitted boundary maps predicted for a test set of images, and were scored based on their agreement with a consensus of human expert annotations. The winning team had no prior experience with EM images, and employed a convolutional network. This "deep learning" approach has since become accepted as a standard for segmentation of FM images. The challenge has continued to accept submissions, and the best so far has resulted from cooperation between two teams. The challenge has probably saturated, as algorithms cannot progress beyond limits set by ambiguities inherent in 2D scoring and the size of the test dataset. Retrospective evaluation of the challenge scoring system reveals that it was not sufficiently robust to variations in the widths of neurite borders. We propose a solution to this problem, which should be useful for a future 3D segmentation challenge.

  • 47.
    Pham, Tuan D
    Aizu Research Cluster for Medical Engineering and Informatics Center for Advanced Information Science and Technology, The University of Aizu, Aizu-Wakamatsu 965-8580, Fukushima, Japan.
    ’Current Informatics and Technology in Biology and Medicine’2015Ingår i: Computers in Biology and Medicine, ISSN 0010-4825, E-ISSN 1879-0534, Vol. 68, s. 28-Artikel i tidskrift (Övrigt vetenskapligt)
  • 48.
    Thang, Truong Cong
    et al.
    University of Aizu, Aizuwakamatsu, Japan.
    Tran, Huyen T
    Hanoi University of Science and Technology, Hanoi, Vietnam.
    Trong, Vo D
    Vietnam Academy of Science and Technology, Hanoi, Vietnam.
    Nguyen, Duc V
    University of Aizu, Aizuwakamatsu, Japan.
    Le, Hung T
    University of Aizu, Aizuwakamatsu, Japan.
    Pham, Tuan D
    University of Aizu, Aizuwakamatsu, Japan.
    Design and implementation of an e-Health system for depression detection2015Konferensbidrag (Refereegranskat)
    Abstract [en]

    We present the design and implementation of a cost-effective e-Health system for automatic depression detection. The system is based on a client-server architecture, where clients are popular mobile devices. For practical deployment, various factors that affect the accuracy and speed of depression detection are discussed and evaluated with extensive experiments.

  • 49.
    Pham, Tuan D
    Aizu Research Cluster for Medical Engineering and Informatics, Research Center for Advanced Information Science and Technology, The University of Aizu, Aizuwakamatsu, Japan.
    Dynamic-Time-Warping Analysis of Feature-Vector Reliability for Cognitive Stimulation Therapy Assessment2015Ingår i: Information Science and Applications / [ed] Kuinam J. Kim, Springer Berlin/Heidelberg, 2015, s. 235-241Kapitel i bok, del av antologi (Övrigt vetenskapligt)
    Abstract [en]

    Cognitive stimulation therapy (CST) can help people with mental illness improve their health condition. In particular, CST provides an alternative treatment for people with mild to moderate dementia. Signal processing and pattern recognition methods are promising tools for automated assessment of the effectiveness of CST in treating individuals with dementia. This paper applies the dynamic time-warping for investigating the reliability of photoplethysmography-derived features extracted by the largest Lyapunov exponents and spectral distortion for CST evaluation.

  • 50.
    Pham, Tuan D
    Aizu Research Cluster for Medical Engineering and Informatics, Center for Advanced Information Science and Technology, The University of Aizu, Aizuwakamatsu, Japan.
    Estimating Parameters of Optimal Average and Adaptive Wiener Filters for Image Restoration with Sequential Gaussian Simulation2015Ingår i: IEEE Signal Processing Letters, ISSN 1070-9908, E-ISSN 1558-2361, Vol. 22, nr 11, s. 1950-1954Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Filtering additive white Gaussian noise in images using the best linear unbiased estimator (BLUE) is technically sound in a sense that it is an optimal average filter derived from the statistical estimation theory. The BLUE filter mask has the theoretical advantage in that its shape and its size are formulated in terms of the image signals and associated noise components. However, like many other noise filtering problems, prior knowledge about the additive noise needs to be available, which is often obtained using training data. This paper presents the sequential Gaussian simulation in geostatistics for measuring signal and noise variances in images without the need of training data for the BLUE filter implementation. The simulated signal variance and the BLUE average can be further used as parameters of the adaptive Wiener filter for image restoration.

123 1 - 50 av 137
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