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.