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  • 51.
    Pham, Tuan D
    Department of Electrical Engineering, University of Sydney, Sydney NSW 2006, Australia;.
    Grade Estimation Using Fuzzy-Set Algorithms1997Ingår i: MATHEMATICAL GEOLOGY, ISSN 1874-8961, Vol. 29, nr 2, s. 291-305Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper presents a new approach for estimating unknown ore grades within a mining deposit in a fuzzy environment using fuzzy c-means clustering and a fuzzy inference system. Based on a collection of cluster centers obtained from fuzzy c-means, a fuzzy rule base and fuzzy search domains are established to compute grades at these cluster centers. These cluter center-grade pairs act as control information in the fuzzy space-grade system in order to infer unknown grades on the basis of fuzzy interpolation, fuzzy extrapolation, and a defuzzification process of fuzzy control.

  • 52.
    Pham, Tuan D
    Aizu Research Cluster for Medical Engineering and Informatics Research Center for Advanced Information Science and Technology.
    Image texture analysis using geostatistical information entropy2012Ingår i: Intelligent Systems (IS), 2012 6th IEEE International Conference, 2012, s. 353-356Konferensbidrag (Refereegranskat)
    Abstract [en]

    Extraction of effective features of objects is an important area of research in the intelligent processing of image data. A well-known feature in images is texture which can be used for image description, segmentation and classification. This paper presents a novel texture extraction method using the principles of geostatistics and the concept of entropy in information theory. Experimental results on medical image data have shown the superior performance of the proposed approach over some popular texture extraction methods.

  • 53.
    Pham, Tuan D
    Aizu Research Cluster for Medical Engineering and Informatics, Research Center for Advanced Information Science and Technology, The University of Aizu Aizu-Wakamatsu, Fukushima, Japan.
    Image texture analysis using geostatistical information entropy2012Ingår i: Intelligent Systems (IS), 2012 6th IEEE International Conference, IEEE , 2012, s. 353-356Konferensbidrag (Refereegranskat)
    Abstract [en]

    Extraction of effective features of objects is an important area of research in the intelligent processing of image data. A well-known feature in images is texture which can be used for image description, segmentation and classification. This paper presents a novel texture extraction method using the principles of geostatistics and the concept of entropy in information theory. Experimental results on medical image data have shown the superior performance of the proposed approach over some popular texture extraction methods.

  • 54.
    Pham, Tuan D
    Bioinformatics Applications Research Center, James Cook University School of Information Technology Townsville, QLD 4811, Australia.
    Integration of fuzzy and geostatistical models for estimating missing multivariate observations.2005Ingår i: WSEAS Transactions on Systems, ISSN 1991-8763, Vol. 4, nr 4, s. 233-237Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The estimation of missing observations is an important research field which has practical applications in many science and engineering disciplines. In analyzing the variability of a particular data set which can be spatially related, classical statistical methods make no use of this type of information; whereas geostatistics accomodates the spatial information of the data set in its regression analysis for estimating missing observations or unknown data. This paper incorporates the modeling of fuzzy protoptyes in the cokriging system of geostatistics in order to improve the accuracy of the estimates and alleviate the computational complexity of cokriging.

  • 55.
    Pham, Tuan D
    Bioinfonnatics Applications Research Centre/School of Mathematics, Physics, and Infonnation Technology James Cook University Townsville, AUSTRALIA.
    Matching and fusing signal-estimation errors for similarity-based pattern classification2007Ingår i: WSEAS Transactions on Systems, ISSN 1109-2777, Vol. 6, nr 1, s. 125-132Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Error estimation using different optimal models for signal processing has been an active research field in data analysis such as speech recognition, image analysis, geophysics, and earth science. A popular direction of research in pattern classification is to develop computational models for comparing objects being either abstract or physical based on some measure of similarity or dissimilarity. This paper explores some linear-prediction models for deriving signal estimation errors and their fusion for similarity-based pattern classification.

  • 56.
    Pham, Tuan D
    School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, Australia.
    Medical image restoration using multiple-point geostatistics2010Ingår i: 2010 3rd International Conference on Biomedical Engineering and Informatics (BMEI 2010): Volume 1, IEEE , 2010, s. 371-374Konferensbidrag (Övrigt vetenskapligt)
    Abstract [en]

    Noise inherently exists in medical and biological images as any imaging device, by a finite exposure time, is subject to stochastic noise from the random arrival events of photons. The purpose of image restoration is to bring back as much as possible the original image from its degraded state. This paper presents a spatial multiple-point statistical approach for restoration of medical image degradation.

  • 57.
    Pham, Tuan D
    Aizu Research Cluster for Medical Engineering and Informatics Center for Advanced Information Science and Technology The University of Aizu Aizu-Wakamatsu, Fukushima 965-8580, Japan.
    Modeling spatial uncertainty of imprecise information in images.2014Konferensbidrag (Refereegranskat)
    Abstract [en]

    The description of information content in images is imprecise in nature. Quantification of uncertainty in images for pattern analysis has been addressed with the theories of probability and fuzzy sets. In this paper, an approach for modeling the spatial uncertainty of images is proposed in the setting of geostatistics and probability measure of fuzzy events. The proposed approach can be utilized to extract an effective feature for image classification.

  • 58.
    Pham, Tuan D
    James Cook Univ., Townsville .
    Predictive Modeling in Proteomics-based Disease Detection2007Konferensbidrag (Refereegranskat)
    Abstract [en]

    Recent advent of mass-spectrometry data generated by proteomic technology provides a new type of biological information which is very promising in the search for diagnostic and therapeutic approaches that enables the early detection of fatal diseases and the development of personalized medicine. Successful analysis of such high-throughput proteomic data relies much on signal-processing and pattern-recognition techniques. This paper addresses the application of prediction models for cancer detection using mass spectral data.

  • 59.
    Pham, Tuan D
    School of Computer Science and Engineering, Research Center for Advanced Information Science and Technology, The University of Aizu, Tsuruga, Ikki-machi, Aizu-Wakamatsu City, Japan.
    Regularity dimension of sequences and its application to phylogenetic tree reconstruction2012Ingår i: Chaos, Solitons & Fractals, ISSN 0960-0779, E-ISSN 1873-2887, Vol. 45, nr 6, s. 879-887Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The concept of dimension is a central development of chaos theory for studying nonlinear dynamical systems. Different types of dimensions have been derived to interpret different geometrical or physical observations. Approximate entropy and its modified methods have been introduced for studying regularity and complexity of time-series data in physiology and biology. Here, the concept of power laws and entropy measure are adopted to develop the regularity dimension of sequences to model a mathematical relationship between the frequency with which information about signal regularity changes in various scales. The proposed regularity dimension is applied to reconstruct phylogenetic trees using mitochondrial DNA (mtDNA) sequences for the family Hominidae, which can be validated according to the hypothesized evolutionary relationships between organisms.

  • 60.
    Pham, Tuan D
    Bioinformatics Applications Research Centre / School of Information Technology, James Cook University, Townsville, Australia.
    Similarity searching in DNA sequences by spectral distortion measures2006Ingår i: Advances in Data Mining. Applications in Medicine, Web Mining, Marketing, Image and Signal Mining: 6th Industrial Conference on Data Mining, ICDM 2006, Leipzig, Germany, July 14-15, 2006. Proceedings / [ed] Petra Perner, Springer Berlin/Heidelberg, 2006, s. 24-37Kapitel i bok, del av antologi (Refereegranskat)
    Abstract [en]

    Searching for similarity among biological sequences is an important research area of bioinformatics because it can provide insight into the evolutionary and genetic relationships between species that open doors to new scientific discoveries such as drug design and treament. In this paper, we introduce a novel measure of similarity between two biological sequences without the need of alignment. The method is based on the concept of spectral distortion measures developed for signal processing. The proposed method was tested using a set of six DNA sequences taken from Escherichia coli K-12 and Shigella flexneri, and one random sequence. It was further tested with a complex dataset of 40 DNA sequences taken from the GenBank sequence database. The results obtained from the proposed method are found superior to some existing methods for similarity measure of DNA sequences.

  • 61.
    Pham, Tuan D
    James Cook University Townsville, QLD 4811 AUSTRALIA.
    Spatial linear predictive coding and its error matching for signal classification2006Konferensbidrag (Refereegranskat)
    Abstract [en]

    Mathematical analysis of the behavior of general dynamic systems based on linear prediction plays an essential role in many fields of science and engineering concerning the processing and representation of complex signals. This paper addresses the parameter estimation of the all-pole model of the linear predictive coding in the sense that the signal has both deterministic and random properties. Estimate of the model variance error is used as a basis for the derivation of a spatial distortion measure which can be used for matching spectral patterns.

  • 62.
    Pham, Tuan D
    Bioinformatics Applications Research Centre, School of Information Technology, James Cook University, Townsville, Australia .
    Spectral analysis of protein sequences2006Ingår i: Advances in Machine Learning and Cybernetics: 4th International Conference, ICMLC 2005, Guangzhou, China, August 18-21, 2005, Revised Selected Papers / [ed] Daniel S. Yeung, Zhi-Qiang Liu, Xi-Zhao Wang and Hong Yan, Springer Berlin/Heidelberg, 2006, s. 595-604Kapitel i bok, del av antologi (Övrigt vetenskapligt)
    Abstract [en]

    Analysis of protein sequences can avoid many problems inherently existing in the study of nucleotide sequences given the knowledge that DNA sequences contain all the information for regulating protein expression. This paper presents a spectral approach for calculating the similarity of protein sequences, which can be useful for the inferences of protein functions. The proposed method is based on the mathematical concepts of linear predictive coding and cepstral distortion measure. We show that this spectral approach can reveal non-trivial results from an experimental study of a set of functionally related and functionally non-related protein sequences, and has advantages over some existing approaches.

  • 63.
    Pham, Tuan D
    School of Engineering and Information Technology, University of New South Wales, Canberra, Australia.
    Supervised restoration of degraded medical images using multiple-point geostatistics2012Ingår i: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 106, nr 3, s. 201-209Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Reducing noise in medical images has been an important issue of research and development for medical diagnosis, patient treatment, and validation of biomedical hypotheses. Noise inherently exists in medical and biological images due to the acquisition and transmission in any imaging devices. Being different from image enhancement, the purpose of image restoration is the process of removing noise from a degraded image in order to recover as much as possible its original version. This paper presents a statistically supervised approach for medical image restoration using the concept of multiple-point geostatistics. Experimental results have shown the effectiveness of the proposed technique which has potential as a new methodology for medical and biological image processing.

  • 64.
    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.

  • 65. Pham, Tuan D
    Towards flexihle formal specifications with fuzzy information granulation1999Ingår i: Computational Intelligence for Modelling, Control & Automation: Evolutionary Computation & Fuzzy Logic for Intelligent Control, Knowledge Acquisition & Information Retrieval, ISSN 1383-7575, Vol. 2, s. 288-293Artikel i tidskrift (Refereegranskat)
  • 66.
    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.

  • 67.
    Pham, Tuan D
    et al.
    James Cook University, Townsville, QLD 4811, Australia..
    Beck, Dominik
    University of Applied Sciences Weihenstephan Weihenstephan, 85350 Freising, Germany.
    Crane, Denis I
    Nathan Campus, Griffith University, QLD 4111, Australia..
    Hidden Markov Models for Unaligned DNA Sequence Comparison2005Ingår i: WSEAS Transactions on Biology and Biomedicine, ISSN 1109-9518, E-ISSN 2224-2902, Vol. 2, s. 64-69Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Comparison of similarity between sequences can provide information for inferring the function of a newly discovered sequence, and understanding the evolutionary relationships among genes, proteins, and entire species. This paper presents a technique for computing the similarity between unaligned DNA sequences. The computation is based on the Kullback-Leibler divergence of hidden Markov models. We used the data sets taken from the threonine operons of Escherichia coli K-12 and Shigella flexneri to test the proposed method. The result obtained agrees with an alignment-based method. We further tested the proposed method with a data set of 34 complete mammalian mtDNA genomes. The phylogenetic tree derived from the second experiment shows reasonable evolutionary relationships between these species.

  • 68.
    Pham, Tuan D.
    et al.
    School of Engineering and Information Technology, University of New South Wales, Canberra, Australia.
    Berger, Klaus
    Institute of Epidemiology and Social Medicine, University of Muenster, Germany.
    Automated detection of white matter changes in elderly people using fuzzy, geostatistical, and information combining models2011Ingår i: IEEE transactions on information technology in biomedicine, ISSN 1089-7771, E-ISSN 1558-0032, Vol. 15, nr 2, s. 242-250Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Detection of white matter changes of the brain using magnetic resonance imaging (MRI) has increasingly been an active and challenging research area in computational neuroscience. There have rarely been any single image analysis methods that can effectively address the issue of automated quantification of neuroimages, which are subject to different interests of various medical hypotheses. This paper presents new image segmentation models for automated detection of white matter changes of the brain in an elderly population. The methods are based on the computational models of fuzzy clustering, possibilistic clustering, geostatistics, and knowledge combination. Experimental results on MRI data have shown that the proposed image analysis methodology can be applied as a very useful computerized tool for the validation of our particular medical question, where white matter changes of the brain are thought to be the most important social medical evidence.

  • 69.
    Pham, Tuan D
    et al.
    Griffith University, Nathan Campus, QLD 4111, Australia.
    Crane, Denis I
    Griffith University, Nathan Campus, QLD 4111, Australia.
    Tannock, David
    Griffith University, Nathan Campus, QLD 4111, Australia.
    Beck, Dominik
    Griffith University, Nathan Campus, QLD 4111, Australia.
    Kullback-Leibler dissimilarity of Markov models for phylogenetic tree reconstruction2004Ingår i: Intelligent Multimedia, Video and Speech Processing, 2004. Proceedings of 2004 International Symposium on, 2004, s. 157-160Konferensbidrag (Refereegranskat)
    Abstract [en]

    We introduce the Kullback-Leibler dissimilarity measure of Markov-chain models for unaligned DNA sequences with application to the phylogenetic tree reconstruction of complete mammalian mitochondrial genomes. The tree obtained by our approach is generally in agreement with those obtained from other methods. Our proposed method is computationally efficient and very easy for computer implementation.

  • 70.
    Pham, Tuan D
    et al.
    ADFA School of Information Technology and Electrical Engineering The University of New South Wales Canberra, Australia.
    Eisenblätter, Uwe
    ADFA School of Information Technology and Electrical Engineering The University of New South Wales Canberra, ACT 2600, Australia.
    A New Spatial Approach to Image Restoration2008Ingår i: Image Processing Theory, Tools and Applications, 2008. IPTA 2008. First Workshops on, Institute of Electrical and Electronics Engineers (IEEE), 2008, s. 1-8Konferensbidrag (Refereegranskat)
    Abstract [en]

    Study in restoring images from their degraded states has been an important research topic in image processing and has potential applications in complex pattern recognition. We propose in this paper a new adaptive image restoration method using the concept of random-function realizations in geostatistics. This conceptual framework allows us to derive the model means and variances in the context of spatial statistics. Experimental results demonstrate the superior performance of the proposed approach to other image restoration algorithms.

  • 71.
    Pham, Tuan D
    et al.
    Bioinformatics Research Group, School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2600, Australia.
    Elfiqi, Heba Z
    Bioinformatics Research Group, School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2600, Australia.
    Knecht, Stefan
    Neurocenter at Schoen Klinik, Hamburg, Germany/ University of Muenster, A. Schweitzer Str. 33, D-48129 Muenster, Germany.
    Wersching, Heike
    University of Muenster, A. Schweitzer Str. 33, D-48129 Muenster, Germany / University of Muenster, Domagkstrasse 3, D-48129 Muenster, Germany.
    Baune, Bernhard T
    Department of Psychiatry and Psychiatric Neuroscience, School of Medicine and Dentistry, James Cook University, Townsville, QLD 4811, Australia.
    Berger, Klaus
    Institute of Epidemiology and Social Medicine, University of Muenster, Domagkstrasse 3, D-48129 Muenster, Germany.
    Structural simplexity of the brain2010Ingår i: Journal of neuroscience methods, ISSN 0165-0270, Vol. 188, nr 1, s. 113-126Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Simplexity is an emerging concept that expresses a possible complementary relationship between complexity and simplicity. The brain has been known as the most complex structure, and tremendous effort has been spent to study how it works. By understanding complex function of the brain, one can hope to unravel the mystery of its diseases and its biological systems. We propose herein an entropy-based framework for analysis of complexity with a particular application to the study of white matter changes of the human brain. In this analysis, the proposed approach takes into account both morphological structure and image intensity values of MRI scans to construct the complexity profiles of the brain. It has been realized that the quantity and spatial distribution of white matter changes play an important role in cognitive decline (i.e. dementia) and other neuropsychiatric disorders (i.e. multiple sclerosis, depression) as well as in other dementia disorders such as Alzheimers disease. Thus, the results can be utilized as a tool for automated quantification and comparison of various spatial distributions and orientations of age-related white matter changes where manual analysis is difficult and leads to different sensitivities for the respective MRI-based information of the brain.

  • 72.
    Pham, Tuan D
    et al.
    Aizu Research Cluster for Medical Engineering and Informatics Research Center for Advanced Information Science and Technology The University of Aizu, Aizuwakamatsu, Fukushima, Japan.
    Ichikawa, Kazuhisa
    Department of Cancer Biology, Division of Mathematical Oncology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan .
    Characterization of Cancer and Normal Intracellular Images by the Power Law of a Fuzzy Partition Functional2013Ingår i: Image Analysis and Recognition: 10th International Conference, ICIAR 2013, Póvoa do Varzim, Portugal, June 26-28, 2013. Proceedings / [ed] Mohamed Kamel, Aurélio Campilho, Berlin, Heidelberg: Springer Berlin/Heidelberg, 2013, s. 597-604Kapitel i bok, del av antologi (Övrigt vetenskapligt)
    Abstract [en]

    The discovery of detailed structures of spatial organelles within a single cell obtained by state-of-the-art molecular imaging technology has provided essential biological information for gaining insights into the study of complex human diseases. In particular, such information is helpful for cancer modeling and simulation. This paper presents a novel concept for characterizing the intracellular space of cancer and normal cells using the mathematical principle of power laws applied to a fuzzy partition functional for cluster validity. Experimental results and comparison with image texture analysis suggest the promising application of the proposed method.

  • 73.
    Pham, Tuan D
    et al.
    Aizu Research Cluster for Medical Engineering and Informatics Research Center for Advanced Information Science and Technology, The University of Aizu, Aizuwakamatsu, Fukushima, Japan .
    Ichikawa, Kazuhisa
    Department of Cancer Biology, Division of Mathematical Oncology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
    Characterization of Cancer and Normal Intracellular Images by the Power Law of a Fuzzy Partition Functional2013Ingår i: Image Analysis and Recognition: 10th International Conference, ICIAR 2013, Póvoa do Varzim, Portugal, June 26-28, 2013. Proceedings / [ed] Mohamed Kamel; Aurélio Campilho, Springer Berlin/Heidelberg, 2013, s. 597-604Konferensbidrag (Refereegranskat)
    Abstract [en]

    The discovery of detailed structures of spatial organelles within a single cell obtained by state-of-the-art molecular imaging technology has provided essential biological information for gaining insights into the study of complex human diseases. In particular, such information is helpful for cancer modeling and simulation. This paper presents a novel concept for characterizing the intracellular space of cancer and normal cells using the mathematical principle of power laws applied to a fuzzy partition functional for cluster validity. Experimental results and comparison with image texture analysis suggest the promising application of the proposed method.

  • 74.
    Pham, Tuan D
    et al.
    University of Aizu, Japan.
    Jain, Lakhmi C
    University of Camberra and south australia, Australia.
    Knowledge-Based Systems in Biomedicine and Computational Life Science2013 (uppl. 1)Bok (Övrigt vetenskapligt)
    Abstract [en]

    This book presents a sample of research on knowledge-based systems in biomedicine and computational life science. The contributions include: personalized stress diagnosis system, image analysis system for breast cancer diagnosis, analysis of neuronal cell images, structure prediction of protein, relationship between two mental disorders, detection of cardiac abnormalities, holistic medicine based treatment and analysis of life-science data.

  • 75.
    Pham, Tuan D
    et al.
    School of Computer Science and Engineering, Research Center for Advanced Information Science and Technology, The University of Aizu, Aizu-Wakamatsu City, Fukushima, Japan.
    Jain, Lakhmi CUniversity of Canberra, Australia; University of South Australia, Adelaide, South Australia, Australia.
    Knowledge-Based Systems in Biomedicine and Computational Life Science2012Samlingsverk (redaktörskap) (Övrigt vetenskapligt)
    Abstract [en]

    This book presents a sample of research on knowledge-based systems in biomedicine and computational life science. The contributions include: personalized stress diagnosis system, image analysis system for breast cancer diagnosis, analysis of neuronal cell images, structure prediction of protein, relationship between two mental disorders, detection of cardiac abnormalities, holistic medicine based treatment and analysis of life-science data.

  • 76.
    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, Fukushima 965-8580, Japan.
    Jang, Xiaoyi
    University of Münster, Einsteinstrasse 62, 48149 Münster, Germany.
    Ichikawa, Kazuhisa
    Division of Mathematical Oncology, The Institute of Medical Science, The University of Tokyo, 108-8639, Tokyo, Japan.
    Current Challenging Medical Image Analysis2014Ingår i: Biomedical engineering online, ISSN 1475-925X, E-ISSN 1475-925X, Vol. 13, nr 1, s. 1-2Artikel i tidskrift (Refereegranskat)
  • 77.
    Pham, Tuan D
    et al.
    School of Information Technology and Electrical Engineering, The University of New South Wales, Canberra, Australia.
    Muller, Catharina C
    Biomolecular and Physical Sciences, and Eskitis Institute for Cell and Molecular Therapies, Griffith University, Nathan, Queensland, Australia.
    Crane, Denis I
    Biomolecular and Physical Sciences, and Eskitis Institute for Cell and Molecular Therapies, Griffith University, Nathan, Queensland, Australia.
    Fuzzy scaling analysis of a mouse mutant with brain morphological changes2009Ingår i: Information Technology in Biomedicine, IEEE Transactions on, ISSN 1089-7771, Vol. 13, nr 4, s. 629-635Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Scaling behavior inherently exists in fundamental biological structures, and the measure of such an attribute can only be known at a given scale of observation. Thus, the properties of fractals and power-law scaling have become attractive for research in biology and medicine because of their potential for discovering patterns and characteristics of complex biological morphologies. Despite the successful applications of fractals for the life sciences, the quantitative measure of the scale invariance expressed by fractal dimensions is limited in more complex situations, such as for histopathological analysis of tissue changes in disease. In this paper, we introduce the concept of fuzzy scaling and its analysis of a mouse mutant with postnatal brain morphological changes.

  • 78.
    Pham, Tuan D
    et al.
    Aizu Research Cluster for Medical Engineering and Informatics, Research Center for Advanced Information Science and Technology, The University of Aizu, Aizu-Wakamatsu, Fukushima 965-8580, Japan.
    Oyama-Higa, Mayumi
    Photoplethysmography technology and its feature visualization for cognitive stimulation assessment2015Konferensbidrag (Refereegranskat)
    Abstract [en]

    Therapeutic communication is recognized as an alternative cognitive stimulation for people with mental disorders. It is important to measure the effectiveness of such therapeutic treatments. In this paper, we present the use of photoplethysmography (PPG) technology to synchronize communication signals between the care-giver and people with dementia. To gain insights into the communication effect, the largest Lyapunov exponents are extracted from the PPG signals, which are then analyzed by multidimensional scaling to visualize the signal similarity/dissimilarity between the care-giver and participants. Experimental results show that the proposed approach is promising as a useful tool for visual assessment of the influence of the therapy over participants.

  • 79.
    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.

  • 80.
    Pham, Tuan D.
    et al.
    Bioinformatics Research Group, School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2600, Australia.
    Salvetti, Federica
    Pisa University, Via Caruso, 14-56122 Pisa, Italy.
    Wang, Bing
    University of New South Wales, Canberra, ACT 2600, Australia.
    Diani, Marco
    Pisa University, Via Caruso, 14-56122 Pisa, Italy.
    Heindel, Walter
    University of M¨unster, A Schweitzer Straße 33, D-48129 M¨unster, University of M¨unster, A Schweitzer Straße 33, D-48129 M¨unster,Germany.
    Knecht, Stefan
    University of M¨unster, A Schweitzer Straße 33, D-48129 M¨unster, Germany.
    Wersching, Heike
    University of Munster, A Schweitzer Straße 33, D-48129 M¨unster, Germany/University of Munster, Domagkstraße.
    Baune, Bernhard T
    School of Medicine, University of Adelaide, SA 5005, Australia.
    Berger, Klaus
    University of M¨unster, Domagkstraße 3,Munster, Germany.
    The hidden-Markov brain comparison and inference of white matter hyperintensities on magnetic resonance imaging (MRI)2011Ingår i: Journal of Neural Engineering, ISSN 1741-2560, E-ISSN 1741-2552, Vol. 8, nr 1, s. 1-10Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Rating and quantification of cerebral white matter hyperintensities on magnetic resonance imaging (MRI) are important tasks in various clinical and scientific settings. As manual evaluation is time consuming and imprecise, much effort has been made to automate the quantification of white matter hyperintensities. There is rarely any report that attempts to study the similarity/dissimilarity of white matter hyperintensity patterns that have different sizes, shapes and spatial localizations on the MRI. This paper proposes an original computational neuroscience framework for such a conceptual study with a standpoint that the prior knowledge about white matter hyperintensities can be accumulated and utilized to enable a reliable inference of the rating of a new white matter hyperintensity observation. This computational approach for rating inference of white matter hyperintensities, which appears to be the first study, can be utilized as a computerized rating-assisting tool and can be very economical for diagnostic evaluation of brain tissue lesions.

  • 81.
    Pham, Tuan D
    et al.
    Bioinformatics Applications Research Center; School of Information Technology, James Cook University, Townsville, QLD, Australia.
    Shim, Byung-Sub
    Bioinformatics Applications Research Center.
    A cepstral distortion measure for protein comparison and identification2005Ingår i: Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on, 2005, Vol. 9, s. 5609-5614Konferensbidrag (Refereegranskat)
    Abstract [en]

    Protein sequence comparison is the most powerful tool for the identification of novel protein structure and function. This type of inference is commonly based on the similar sequence-similar structure-similar function paradigm, and derived by sequence similarity searching on databases of protein sequences. As entire genomes have been being determined at a rapid rate, computational methods for comparing protein sequences will be more essential for probing the complexity of molecular machines. In this paper we introduce a pattern-comparison algorithm, which is based on the mathematical concept of linear-predictive-coding based cepstral distortion measure, for comparison and identification of protein sequences. Experimental results on a real data set of functionally related and functionally non-related protein sequences have shown the effectiveness of the proposed approach on both accuracy and computational efficiency.

  • 82.
    Pham, Tuan D
    et al.
    School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, Australia .
    To, Cuong C
    School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, Australia.
    Wang, Honghui
    Clinical Center, National Institutes of Health, Bethesda, USA.
    Zhou, Xiaobo
    Center for Biotechnology and Informatics, The Methodist Hospital Research Institute, Weill Cornell Medical College, Houston, USA.
    Analysis of Major Adverse Cardiac Events with Entropy-Based Complexity2010Ingår i: Information Technologies in Biomedicine: Volume 2 / [ed] Ewa Pietka and Jacek Kawa, Springer Berlin/Heidelberg, 2010, s. 261-272Kapitel i bok, del av antologi (Refereegranskat)
    Abstract [en]

    Major adverse cardiac events (MACE) are referred to as unsuspected heart attacks that include death, myocardial infarction and target lesion revascularization. Feature extraction and classification methods for such cardiac events are useful tools that can be applied for biomarker discovery to allow preventive treatment and healthy-life maintenance. In this study we present an entropy-based analysis of the complexity of MACE-related mass spectrometry signals, and an effective model for classifying MACE and control complexity-based features. In particular, the geostatistical entropy is analytically rigorous and can provide better information about the predictability of this type of MACE data than other entropy-based methods for complexity analysis of biosignals. Information on the complexity of this type of time-series data can expand our knowledge about the dynamical behavior of a cardiac model and be useful as a novel feature for early prediction.

  • 83.
    Pham, Tuan D
    et al.
    Bioinformatics Applications Research Centre and Information Technology Discipline, School of Mathematics, Physics, and Information Technology, James Cook University, Townsville, Australia.
    Tran, Dat T
    School of Information Sciences and Engineering, University of Canberra, ACT 2601, Australia .
    Zhou, Xiaobo
    HCNR-Center for Bioinformatics and Brigham and Womens Hospital, Harvard Medical School, Boston, MA 02215, USA .
    Fuzzy information fusion of classification models for high-throughput image screening of cancer cells in time-lapse microscopy2007Ingår i: Journal of Knowledge-based & Intelligent Engineering Systems, ISSN 1327-2314, E-ISSN 1875-8827, International Journal of Knowledge-based and Intelligent Engineering Systems, Vol. 11, nr 4, s. 237-246Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Bioimaging at molecular and cellular levels requires specific image analysis methods to help life scientists develop methodologies and hypotheses in biology and biomedicine. In particular, this is true when dealing with microscopic images of cells and vessels. To facilitate the automation of cell screening, we have developed methods based on vector quantization and Markov model for classification of cellular phases using time-lapse fluorescence microscopic image sequences. Because of ambiguity inherently existing in the labeling of cell-phase feature vectors, we proposed to use relaxation labeling technique to reduce uncertainty among cell-phase models having overlapping properties. To further improve the classification rate we applied a fuzzy fusion strategy for combining individual results obtained from multiple classifiers. Our proposed image-classification methods can be useful for the task of high-content cell-cycle screening which is essential for biomedical research in the study of structures and functions of cells and molecules.

  • 84.
    Pham, Tuan D
    et al.
    Aizu Research Cluster for Medical Engineering and Informatics The University of Aizu, Aizu-Wakamatsu, Fukushima 965-8580, Japan.
    Tsunoyama, Taichiro
    Teikyo University, Tokyo 173-8606, Japan.
    Thang, Truong C
    Aizu Research Cluster for Medical Engineering and Informatics The University of Aizu, Aizu-Wakamatsu, Fukushima 965-8580, Japan.
    Fujita, Takashi
    Teikyo University, Tokyo 173-8606, Japan.
    Sakamoto, Takanori
    Teikyo University, Tokyo 173-8606, Japan.
    Image classification of bowel abnormalities and ischemia2014Konferensbidrag (Refereegranskat)
    Abstract [en]

    Intestinal abnormalities and ischemia are medical conditions in which inflammation and injury of the intestine are caused by inadequate blood supply. Developments of computerized systems for the automated identification of these types of complex gastrointestinal disorders are rarely reported. In this paper, we introduce a mapping model of spatial uncertainty in computed tomography images for feature extraction, which can be effectively applied for diagnostic detection. Experimental results obtained from the analysis of clinical data suggest the usefulness of the proposed uncertainty mapping model.

  • 85.
    Pham, Tuan D
    et al.
    Aizu Res. Cluster for Med. Eng. & Inf., Univ. of Aizu, Aizuwakamatsu, Japan.
    Vo, Dzung
    Aizuwakamatsu, Fukushima 965-8580, Japan.
    Nguyen-Thanh, Nhan
    The University of Aizu, Aizuwakamatsu, Fukushima 965-8580, Japan.
    Thang, Truong Cong
    The University of Aizu, Aizuwakamatsu, Fukushima 965-8580, Japan.
    Ichikawa, Kazuhisa
    The University of Tokyo, Tokyo 108-8639, Japan.
    How Complex Is Cancer Intracellular Signaling Space in FIB-SEM Images?2012Konferensbidrag (Refereegranskat)
    Abstract [en]

    How cell regulates its intracellular features to optimize their signaling pathways is still far from understanding. Recent advancement in microscopy imaging of the structure of cell organelles enables biomedical researchers to study cell morphology in great detail to discover the pathogeneses of diseases by information obtained at molecular level. A particular interest is to quantify the complexity of the spatial content of the intracellular space captured by the combination of focused ion beam (FIB) and scanning electron microscope (SEM) imaging systems. Such quantitative measure of the complexity of organelles is expected to be a useful tool for benchmarking biological simulations of cancers and controlling disease-specific drug effects. In this paper, for the first time nonlinear dynamical models are utilized to investigate the structural characteristics of intracellular space using FIB-SEM technology to quantify the complex architecture of cell organelles.

  • 86.
    Pham, Tuan D
    et al.
    Aizu Research Cluster for Medical Engineering and Informatics, Center for Advanced Information Science and Technology, The University of Aizu, Aizuwakamatsu, Fukushima, Japan.
    Vo, Dzung
    Computer Communications Laboratory, School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, Fukushima, Japan.
    Nguyen-Thanh, Nhan
    Aizu Research Cluster for Medical Engineering and Informatics, Center for Advanced Information Science and Technology, The University of Aizu, Aizuwakamatsu, Fukushima, Japan.
    Thang, Truong Cong
    Aizu Research Cluster for Medical Engineering and Informatics, Center for Advanced Information Science and Technology, Computer Communications Laboratory, School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, Fukushima, Japan.
    Ichikawa, Kazuhisa
    Department of Cancer Biology, Division of Mathematical Oncology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
    How Complex Is Cancer Intracellular Signaling Space in FIB-SEM Images?2012Ingår i: Sixth UKSim/AMSS European Symposium on Computer Modeling and Simulation (EMS), 2012, IEEE Computer Society, 2012, s. 139-142Konferensbidrag (Refereegranskat)
    Abstract [en]

    How cell regulates its intracellular features to optimize their signaling pathways is still far from understanding. Recent advancement in microscopy imaging of the structure of cell organelles enables biomedical researchers to study cell morphology in great detail to discover the pathogeneses of diseases by information obtained at molecular level. A particular interest is to quantify the complexity of the spatial content of the intracellular space captured by the combination of focused ion beam (FIB) and scanning electron microscope (SEM) imaging systems. Such quantitative measure of the complexity of organelles is expected to be a useful tool for benchmarking biological simulations of cancers and controlling disease-specific drug effects. In this paper, for the first time nonlinear dynamical models are utilized to investigate the structural characteristics of intracellular space using FIB-SEM technology to quantify the complex architecture of cell organelles.

  • 87.
    Pham, Tuan D
    et al.
    School of Computing, University of Canberra, Canberra, Australia.
    Wagner, M
    School of Computing, University of Canberra, Canberra, Australia.
    Fuzzy kriging filter for image restoration1999Konferensbidrag (Refereegranskat)
    Abstract [en]

    Kriging and fuzzy sets are combined as a spatial filter for smoothing gray-scale images degraded by Gaussian white noise. Application of fuzzy sets allows a gradual transition between two boundaries of semi-variance levels as a criterion for smoothing the pixel values. Results which are obtained by the fuzzy kriging filter are smoother and still preserved edges compared with those by the adaptive Wiener filter

  • 88.
    Pham, Tuan D
    et al.
    Sch. of Comput., Univ. of Canberra, ACT, Australia.
    Wagner, Michael
    Sch. of Comput., Univ. of Canberra, ACT, Australia.
    Information based speaker verification2000Konferensbidrag (Refereegranskat)
    Abstract [en]

    We discuss the conceptual and computational frameworks of information theory for decision making in speaker verification. The proposed approach departs from other conventional scoring models for speaker verification as the first approach takes into account the quantity of `surprise' or information content. We compare the new approach with a widely used log-likelihood normalization method for speaker verification. Experimental results on a commercial speech corpus validates the theoretical foundation of the proposed method. Furthermore, we introduce the unique entropic measure of uncertainty in the verification scoring

  • 89.
    Pham, Tuan D
    et al.
    Systems Engineering Division, School of Engineering, Cardiff University, Cardiff CF24 OYF, UK.
    Wang, Z
    Systems Engineering Division, School of Engineering, Cardiff University, Cardiff CF24 OYF, UK.
    Yang, M
    Systems Engineering Division, School of Engineering, Cardiff University, Cardiff CF24 OYF, UK.
    Packianather, M S
    Systems Engineering Division, School of Engineering, Cardiff University, Cardiff CF24 OYF, UK.
    Statistical Analysis of Signal-to-Noise Ratios in Fringe Pattern Matching2002Ingår i: IEEE 6th International Conference on Signal Processing Proceedings, Vol. 1, s. 636-639Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The paper presents a statistical analysis of signal-to-noise ratios (SNRs) in fringe pattern matching. It shows theoretically that the SNR of interference fringes can be significantly improved by fringe pattern matching or mean square difference calculation based on statistical analysis. Computer simulation and experimental results have confirmed that the high accuracy of fringe pattern matching is due to the significant SNR improvements achieved.

  • 90.
    Pham, Tuan D
    et al.
    School of Computing and Information Technology, Nathan Campus, Griffith University QLD 4111, Australia.
    Yang, Jinsong
    School of Computing and Information Technology, Nathan Campus, Griffith University QLD 4111, Australia.
    Logo Detection in Document Images with Complex Backgrounds2005Ingår i: Computer-Aided Intelligent Recognition Techniques and Applications / [ed] Muhammad Sarfraz, 2005, s. 89-98Kapitel i bok, del av antologi (Refereegranskat)
  • 91.
    Pham, Tuan D
    et al.
    Bioinformatics Applications Research Center, School of Mathematics, Physics, and Information Technology, James Cook University, Townsville, Australia.
    Zhou, Xiaobo
    HCNR Center for Bioinformatics, Harvard Medical School, Boston, USA .
    A novel image feature for nuclear-phase classification in high content screening2007Ingår i: Advances in Mass Data Analysis of Signals and Images in Medicine, Biotechnology and Chemistry: International Conferences MDA 2006/2007, Leipzig, Germany, July 18, 2007. Selected Papers / [ed] Petra Perner and Ovidio Salvetti, Springer Berlin/Heidelberg, 2007, s. 84-93Kapitel i bok, del av antologi (Refereegranskat)
    Abstract [en]

    Cellular imaging is an exciting area of research in computational life sciences, which provides an essential tool for the study of diseases at the cellular level. In particular, to faciliate the usefulness of cellular imaging for high content screening, image analysis and classification need to be automated. In fact the task of image classification is an important component for any computerized imaging system which aims to automate the screening of high-content, high-throughput fluorescent images of mitotic cells. It can help biomedical and biological researchers to speed up the analysis of mitotic data at dynamic ranges for various applications including the study of the complexity of cell processes, and the screening of novel anti-mitotic drugs as potential cancer therapeutic agents. We propose in this paper a novel image feature based on a spatial linear predictive model. This type of image feature can be effectively used for vector-quantization based classification of nuclear phases. We used a dataset of HeLa cells line to evaluate and compare the proposed method on the classification of nuclear phases. Experimental results obtained from the new feature are found to be superior to some recently published results using the same dataset.

  • 92.
    Pham, Tuan D
    et al.
    Bioinformatics Applications Research Centre, James Cook University, Australia.
    Zhou, Xiaobo
    Bioinformatics Applications Research Centre, James Cook University, Australia.
    Computational Models For Life Sciences (CMLS '07)2007Konferensbidrag (Refereegranskat)
    Abstract [en]

    This conference proceedings text features research papers that address novel applications of computer, physical, engineering and mathematical models for solving modern challenging problems in life sciences. All the papers, presented at the Computational Models for Life Sciences conference held in 2007, have been peer-reviewed. They cover a huge range of topics, including image analysis, computer vision, and pattern analysis and classification, among many others.

  • 93.
    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.

  • 94.
    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.

  • 95.
    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)
  • 96.
    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.

  • 97.
    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. 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.
    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.

  • 98.
    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.

    Publikationen är tillgänglig i fulltext från 2019-11-27 12:00
  • 99.
    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.

  • 100.
    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.

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