An Entropy Based Multi-Thresholding Method for Semi-Automatic Segmentation of Liver Tumors

Liver cancer is the fifth most commonly diagnosed cancer and the third most common cause of death from cancer worldwide; moreover the liver is the second most commonly involved organ by metastatic disease. 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 developed for the semi-automatic segmentation of liver tumors in the data sets 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 liver segmentation and once we have extracted the organ structure we segment the lesions applying an adaptive multi-thresholding system.