Abstract
Low contrast between tumor and healthy liver tissue is one of the significant and challenging features among others in the automated tumor delineation process. In this paper we propose kernel based clustering algorithms that incorporate Tsallis entropy to resolve long range interactions between tumor and healthy tissue intensities. This paper reports the algorithm and its encouraging results of evaluation with MICCAI liver Tumor Segmentation Challenge 08 (LTS08) dataset. Work in progress involves incorporating additional features and expert knowledge into clustering algorithm to improve the accuracy.
Original language | English |
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Title of host publication | Abdominal Imaging |
Subtitle of host publication | Computational and Clinical Applications - Third International Workshop, Held in Conjunction with MICCAI 2011, Revised Selected Papers |
Pages | 99-107 |
Number of pages | 9 |
DOIs | |
Publication status | Published - 2012 |
Externally published | Yes |
Event | International Workshop on Abdominal Imaging (ABDI) - Computational and Clinical Applications 2011 - Toronto, Canada Duration: 18 Sep 2011 → 18 Sep 2011 Conference number: 3rd https://link.springer.com/book/10.1007/978-3-642-28557-8 (Proceedings) |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 7029 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | International Workshop on Abdominal Imaging (ABDI) - Computational and Clinical Applications 2011 |
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Abbreviated title | ABDI 2011 |
Country/Territory | Canada |
City | Toronto |
Period | 18/09/11 → 18/09/11 |
Internet address |
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Keywords
- Kernelized Fuzzy C-Mean
- Liver Tumor
- Multi Feature Fuzzy C-Mean
- Tsallis Entropy
- Tumor Segmentation