A novel classification via clustering algorithm for fibrosis assessment in liver biopsies

The therapeutic efficacy of medication and treatment strategies is based on the diagnosis and staging of liver diseases. According to recent researches, the Collagen Proportional Area (CPA) is a reliable metric to assess fibrosis in liver tissues. Several image processing techniques are used for the analysis of liver biopsy images, providing objective assessment for the severity of the disease. In current work a novel classification via clustering algorithm is proposed, based on K-means, which is used for image segmentation of liver biopsies. More specifically, supervised learning is employed to insert constraints on centroids movement. Furthermore, feature weighting is utilized for the classification process. At first, a hypercube is extracted and feature weights are computed, for each class, using a training set of liver biopsy images. Classification via clustering follows, initializing a centroid for each class within the respective hypercube, which, during the iterations of the clustering, is allowed to move only inside the hypercube. The weighted Euclidean distance is used as similarity criterion to the clusters. 93 liver biopsy images are employed to evaluate the proposed approach. The classification results along with CPA values are computed.