Minimum entropy segmentation applied to multi-spectral chromosome images
In the early 1990s, the state-of-the-art in commercial chromosome image acquisition was grayscale. Automated chromosome classification was based on the grayscale image and boundary information obtained during segmentation. Multi-spectral image acquisition was developed in 1990 and commercialized in the mid-1990s. One acquisition method, multiplex fluorescence in-situ hybridization (M-FISH), uses five color dyes. We propose a segmentation algorithm for M-FISH images that minimizes the entropy of classified pixels within possible chromosomes. This method is shown to correctly decompose even difficult clusters of touching and overlapping chromosomes. Finally, an example image is given to illustrate the algorithm.