Classification of multi-spectral florescence in situ hybridization images with fuzzy clustering and multiscale feature selection

Multi-color or multiplex fluorescence in situ hybridization (M-FISH) imaging is a recently developed molecular cytogenetic diagnosis technique for rapid visualization of genomic aberrations at the chromosomal level. The reliability of the technique depends primarily on the accurate pixel-wise classification. In the paper we introduce a novel approach that combines fuzzy clustering with multiscale feature selection to improve the accuracy of classifying M-FISH images. A multiscale principal component analysis (MPCA) was proposed to reduce the redundancy between multi-channel images. In comparison with conventional PCA, it offers adaptive redundancy reduction. The algorithms have been tested on an M-FISH image database, demonstrating the improvement in the classification accuracy. The increased accuracy of pixel-wise classification will improve the reliability of M-FISH imaging technique in identifying subtle and cryptic genetic aberrations for cancer diagnosis and genetic research.