M-fish image analysis with improved adaptive fuzzy c-means clustering based segmentation and sparse representation classification

Image segmentation and classification are two important steps in multicolor fluorescence in-situ hybridization (M-FISH) image analysis. In this paper we first developed an improved adaptive fuzzy c-means (IAFCM) clustering algorithm for the segmentation of the DAPI channel of M-FISH images to extract chromosome regions. Then we employed a sparse representation based classification (SRC) algorithm for the classification of chromosomes. The developed image segmentation and classification methods have been tested on a comprehensive M-FISH image database that we established. When comparing with other M-FISH image classifiers such as fuzzy c-means clustering algorithms and adaptive fuzzy c-means clustering algorithms that we proposed earlier, the current SRC method with proper models gave the lowest classification error. In addition, IAFCM improves the classical fuzzy c-means algorithm (FCM) by using a gain field that models and corrects intensity inhomogeneities caused by microscope imaging system, flairs of targets (chromosomes) and uneven hybridization of DNA. Experiments showed that IAFCM improved accuracy in the segmentation of chromosome region, leading to better classification of chromosomes, which will contribute to improved diagnosis of genetic diseases and cancers.