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Segmentation of M-FISH Images for Improved Classification of Chromosomes With an Adaptive Fuzzy C-means Clustering Algorithm

An adaptive fuzzy c-means algorithm was developed and applied to the segmentation and classification of multicolor fluorescence in situ hybridization (M-FISH) images, which can be used to detect chromosomal abnormalities for cancer and genetic disease diagnosis. The algorithm improves the classical fuzzy c-means algorithm (FCM) by the use of a gain field, which models and corrects intensity inhomogeneities caused by a microscope imaging system, flairs of targets (chromosomes), and uneven hybridization of DNA. Other than directly simulating the inhomogeneousely distributed intensities over the image, the gain field regulates centers of each intensity cluster. The algorithm has been tested on an M-FISH database that we have established, which demonstrates improved performance in both segmentation and classification. When compared with other FCM clustering-based algorithms and a recently reported region-based segmentation and classification algorithm, our method gave the lowest segmentation and classification error, which will contribute to improved diagnosis of genetic diseases and cancers.