An entropy based multi-thresholding method for semi-automatic segmentation of liver tumors
Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/1430
New: Prefer using the following doi: https://doi.org/10.54294/xd06v0
Liver cancer is the fifth most commonly diagnosed cancer and the third most common cause of death from cancer worldwide. A precise analysis of the lesions would help in the staging of the tumor and in the evaluation of the possible applicable therapies. In this paper we present the workflow we have developed for the semi-automatic segmentation of liver tumors in the datasets provided for the MICCAI Liver Tumor Segmentation contest. Since we wanted to develop a system that could be as automatic as possible and to follow the segmentation process in every single step starting from the image loading to the lesion extraction, we decided to subdivide the workflow in two main steps: first we focus on the segmentation of the liver and once we have extracted the organ structure we segment the lesions applying an adaptive multi-thresholding system.