Session

Saturday, June 24, 2017 - 14:45 to 15:45
Automated collagen proportional area extraction in liver biopsy images using a novel classification via clustering algorithm
Abstract: 
Diagnosis and staging of liver diseases are essential for the therapeutic efficacy of medication and treatment strategies. Measuring the Collagen Proportional Area (CPA) in liver biopsies recently becomes an effective tool for the assessment of fibrosis in liver tissues. State of the art image processing techniques are employed to analyze biopsy images, providing objective assessment of diseases severity. In current work a novel modification of K-means clustering is proposed for image segmentation of liver biopsies. More specifically, supervised restriction of centroids movement is utilized. In the first stage, a training set of images are employed to extract a hypercube for each class. Then, one centroid is initialized inside each hypercube and during the iterations of the clustering is allowed to move only inside the hypercube. For the evaluation of the proposed method 8 liver biopsy images are employed and classification results along with CPA values are computed for each image.
Dimosthenis C. Tsouros's picture
Dimosthenis C. Tsouros
Panagiotis N. Smyrlis's picture
Panagiotis N. Smyrlis
Nikolaos Giannakeas's picture
Nikolaos Giannakeas
Alexandros Tzallas's picture
Alexandros Tzallas
Technological Educational Institute of Epirus (GR)
Pinelopi Manousou's picture
Pinelopi Manousou
Dimitrios G. Tsalikakis's picture
Dimitrios G. Tsalikakis
Markos G. Tsipouras's picture
Markos G. Tsipouras
University of Western Macedonia