NDLI: Segmentation of mitochondria in fluorescence micrographs by SVM

Content Provider	IEEE Xplore Digital Library
Author	Sampe, I.E. Han-Wei Dann YuhnShow Tsai Chung-Chih Lin
Copyright Year	2011
Description	Author affiliation: Department of Biomedical Engineering, Chung Yuan Christian University, Jhongli, Taiwan (Sampe, I.E.; Han-Wei Dann; YuhnShow Tsai) \|\| Department of Life Sciences and Institute of Genome, Sciences, National Yang-Ming University, Taipei, Taiwan (Chung-Chih Lin)
Abstract	Mitochondrial morphology is correlated with mitochondrial functions, neurodegenerative diseases and ageing. Mitochondrial morphology may also be important to study the etiology of type 2 diabetes due to vital role of β-cell mitochondria in insulin. We have developed a quantification system for numerical analysis of mitochondrial morphology of β-cell. However, accuracy of our previous system for certain type of image is only ∼60% due to errors in segmentation of mitochondria. A new segmentation must be developed to improve our analysis system. Support Vector Machine (SVM) is considered as good approach candidate for better segmentation because this method separates the classes in certain way to maximize the margin among them. Five manually segmented single cell mitochondria images of five typical morphological subtypes are used as training data. The new SVM-based segmentation method shows its better performance by comparing with three traditional methods, including top-hat, Mean and Otsu. The single cell fluorescence micrographs are used as test data for checking performance of our new system. Our new system can segment mitochondria accurately even mitochondria with various intensities are crowded in noisy background.
Starting Page	491
Ending Page	495
File Size	649014
Page Count	5
File Format	PDF
ISBN	9781424493517
e-ISBN	9781424493524
DOI	10.1109/BMEI.2011.6098271
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Image segmentation Accuracy Image edge detection Morphology mitochondrial morphology segmentation SVM Kernel
Content Type	Text
Resource Type	Article

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