Multi-color fluorescence in-situ hybridization (M-FISH) image analysis based on sparse representation models
There are a variety of chromosomal abnormalities such as translocation, duplication, deletion, insertion and inversion, which may cause severe diseases, e.g., cancers and birth defects. Multi-color fluorescence in-situ hybridization (M-FISH) is an imaging technique popularly used for simultaneously detecting and visualizing these complex abnormalities in a single hybridization. In spite of the advancement of fluorescence microscopy for chromosomal abnormality detection, the quality of the fluorescence images is still limited, due to the spectral overlap, uneven intensity level across multiple channels, variations of background and inhomogeneous intensity within intra-channels. Therefore, it is critical but challenging to distinguish the different types of chromosomes accurately in order to detect the chromosomal abnormalities from M-FISH images. The main contribution of this dissertation is to develop an M-FISH image analysis pipeline by taking full advantage of spatial and spectral information from M-FISH imaging. In addition, novel image analysis approaches such as the sparse representation are applied in this work. The pipeline starts with the image preprocessing to extract the background to improve the quality of the raw images by low-rank plus group lasso decomposition. Then, the image segmentation is performed by incorporating both spatial and spectral information by total variation (TV) and row-wise constraints. Finally image classification is conducted by considering the structural information of neighboring pixels with a row-wise sparse representation model. In each step, new methods and sophisticated algorithms were developed and compared with several popularly used methods, It shows that (1) the preprocessing model improves the quality of the raw images; (2) the segmentation model outperforms than both fuzzy c-means (FCM) and improved adaptive fuzzy c-means (IAFCM) models in terms of correct ratio and false rate; and (3) the classification model corrects the misclassification to improve the accuracy of chromosomal abnormalities detection, especially for the complex inter-chromosomal rearrangements.