An entropy based multi-thresholding method for semi-automatic segmentation of liver tumors

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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.

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Categories: Anisotropic blurring filters, Classification, CMake, Component Analysis and Discriminants, Data, Data Representation, Edge Detection, Feature extraction, Filtering, Generic Programming, Image, Images, Information Theory, IO, Level sets, Mathematical Morphology, Mathematics, Neighborhood filters, Optimization, Parameter Techniques, PointSet, Programming, Region growing, Segmentation, Thresholding, Watersheds
Keywords: Segmentation, Liver tumors, Multi-thresholding, De-noising
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