Segmentation of liver tumor using HMRF-EM algorithm with Bootstrap resampling

Abstract

Volume measurement of liver tumor is an important task for surgical planning and cancer following-up. The computation of this volume requires an efficient liver tumor segmentation method. This work deals with liver tumor segmentation from computed tomography (CT) images. We are interested by HMRF-EM classification method. This method considers the spatial information given by voxel neighbors. A Bootstrap resampling, based on selecting randomly an optimal representative set of voxels, is proposed to accelerate the classification process. In order to extract correctly the tumor region, a post-treatment based on morphological operators and active contours method is needed. The entire approach was evaluated on two clinical data sets with manually generated ground truth segmentation by radiologists and has presented promising results.