Segmentation and Fuzzy-Logic Classification of M-FISH Chromosome Images

Multicolor fluorescence in-situ hybridization (m-fish) technique provides color karyotyping that allows simultaneous analysis of numerical and structural abnormalities of whole human chromosomes. Currently available m-fish systems exhibit misclassifications of multiple pixel regions that are often l...

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Bibliographic Details
Main Authors: Choi, H., Castleman, K. R., Bovik, A. C.
Format: Conference Proceeding
Language:English
Subjects:
Biological cells
Chromosome
Classification
Fluorescence
Fuzzy logic
Humans
Image color analysis
Image edge detection
Image segmentation
M-FISH
Maximum likelihood detection
Optical interferometry
Pixel
Segmentation
Unsupervised
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Description
Summary:Multicolor fluorescence in-situ hybridization (m-fish) technique provides color karyotyping that allows simultaneous analysis of numerical and structural abnormalities of whole human chromosomes. Currently available m-fish systems exhibit misclassifications of multiple pixel regions that are often larger than the actual chromosomal rearrangement. This paper presents a novel unsupervised classification method based on fuzzy logic classification and a prior adjusted reclassification method. Utilizing the chromosome boundaries, the initial classification results improved significantly after the prior adjusted reclassification while keeping the translocations intact. This paper also presents a new segmentation method that combines both spectral and edge information. Ten m-fish images from a publicly available database were used to test our methods. The segmentation accuracy was more than 98% on average.
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2006.312360