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 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.

• Type: Conference
• Year: 2006
• Where: ICIP
• Authors: Hyohoon Choi, Kenneth R. Castleman, Alan C. Bovik
• Keywords: Actual Chromosomal Rearrangement, Adjusted Reclassification Method, Fuzzy Logic Classification, ICIP 2006, Image Processing, Prior Adjusted Reclassification, Unsupervised Classification Method