Liver tumor segmentation using kernel-based FGCM and PGCM

Rajeswari Mandava, Lee Song Yeow, Bhavik Anil Chandra, Ong Kok Haur, Muhammad Fermi Pasha, Ibrahim Lutfi Shuaib

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

1 Citation (Scopus)


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 languageEnglish
Title of host publicationAbdominal Imaging
Subtitle of host publicationComputational and Clinical Applications - Third International Workshop, Held in Conjunction with MICCAI 2011, Revised Selected Papers
Number of pages9
Publication statusPublished - 2012
Externally publishedYes
EventInternational Workshop on Abdominal Imaging (ABDI) - Computational and Clinical Applications 2011 - Toronto, Canada
Duration: 18 Sep 201118 Sep 2011
Conference number: 3rd (Proceedings)

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7029 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Workshop on Abdominal Imaging (ABDI) - Computational and Clinical Applications 2011
Abbreviated titleABDI 2011
Internet address


  • Kernelized Fuzzy C-Mean
  • Liver Tumor
  • Multi Feature Fuzzy C-Mean
  • Tsallis Entropy
  • Tumor Segmentation

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