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A double thresholding method for cancer stem cell detection
Bioinformatics Research Group School of Engineering and Information Technology The University of New South Wales Canberra, ACT 2600, Australia .
Bioinformatics Research Group School of Engineering and Information Technology The University of New South Wales Canberra, ACT 2600, Australia .ORCID iD: 0000-0002-4255-5130
The Methodist Hospital Research Institute Cornell University Houston, TX 77030, USA.
2011 (English)Conference paper (Refereed)Text
Abstract [en]

Image analysis of cancer cells is important for cancer diagnosis and therapy, because it recognized as the most efficient and effective way to observe its proliferation. For the purpose of adaptive and accurate cancer cell image segmentation, a double threshold segmentation method is proposed in this paper. Based on a single gray-value histogram of the RGB color space, a double threshold, the key parameters of threshold segmentation can be fixed by a fitted-curve of the RGB component histogram. As reasonable thresholds confirmed, binary segmentation dependent on two thresholds, will be put into practice and result in binary image. With the post-processing of mathematical morphology and division of whole image, the better segmentation result can be finally achieved. By the comparison with other advanced segmentation methods such as level set and active contour, the proposed double thresholding has been found as the simplest strategy with shortest processing time as well as highest accuracy. The proposed method can be effectively used in the detection and recognition of cancer stem cells in images.

Place, publisher, year, edition, pages
2011. 695-699 p.
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:liu:diva-127896ISBN: 978-953-184-159-7ISBN: 978-1-4577-0841-1OAI: oai:DiVA.org:liu-127896DiVA: diva2:928801
Conference
7th International Symposium on Image and Signal Processing and Analysis (ISPA 2011). 4-6 September 2011, Dubrovnik
Available from: 2016-05-16 Created: 2016-05-13 Last updated: 2016-05-30

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Pham, Tuan D.
Medical Image Processing

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