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

doi:10.1007/978-3-642-28557-8_13

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 proceeding › Conference Paper › Research › peer-review

1 Citation (Scopus)

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
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	https://doi.org/10.1007/978-3-642-28557-8_13
Publication status	Published - 2012
Externally published	Yes
Event	International Workshop on Abdominal Imaging (ABDI) - Computational and Clinical Applications 2011 - Toronto, Canada Duration: 18 Sept 2011 → 18 Sept 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)
Volume	7029 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Workshop on Abdominal Imaging (ABDI) - Computational and Clinical Applications 2011
Abbreviated title	ABDI 2011
Country/Territory	Canada
City	Toronto
Period	18/09/11 → 18/09/11
Internet address	https://link.springer.com/book/10.1007/978-3-642-28557-8 (Proceedings)

Keywords

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

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10.1007/978-3-642-28557-8_13

Cite this

Mandava, R., Yeow, L. S., Chandra, B. A., Haur, O. K., Pasha, M. F., & Shuaib, I. L. (2012). Liver tumor segmentation using kernel-based FGCM and PGCM. In Abdominal Imaging: Computational and Clinical Applications - Third International Workshop, Held in Conjunction with MICCAI 2011, Revised Selected Papers (pp. 99-107). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7029 LNCS). https://doi.org/10.1007/978-3-642-28557-8_13

Mandava, Rajeswari ; Yeow, Lee Song ; Chandra, Bhavik Anil et al. / Liver tumor segmentation using kernel-based FGCM and PGCM. Abdominal Imaging: Computational and Clinical Applications - Third International Workshop, Held in Conjunction with MICCAI 2011, Revised Selected Papers. 2012. pp. 99-107 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Liver tumor segmentation using kernel-based FGCM and PGCM",

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.",

keywords = "Kernelized Fuzzy C-Mean, Liver Tumor, Multi Feature Fuzzy C-Mean, Tsallis Entropy, Tumor Segmentation",

author = "Rajeswari Mandava and Yeow, {Lee Song} and Chandra, {Bhavik Anil} and Haur, {Ong Kok} and Pasha, {Muhammad Fermi} and Shuaib, {Ibrahim Lutfi}",

note = "Copyright: Copyright 2012 Elsevier B.V., All rights reserved.; International Workshop on Abdominal Imaging (ABDI) - Computational and Clinical Applications 2011, ABDI 2011 ; Conference date: 18-09-2011 Through 18-09-2011",

year = "2012",

doi = "10.1007/978-3-642-28557-8_13",

language = "English",

isbn = "9783642285561",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Mandava, R, Yeow, LS, Chandra, BA, Haur, OK, Pasha, MF & Shuaib, IL 2012, Liver tumor segmentation using kernel-based FGCM and PGCM. in Abdominal Imaging: Computational and Clinical Applications - Third International Workshop, Held in Conjunction with MICCAI 2011, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7029 LNCS, pp. 99-107, International Workshop on Abdominal Imaging (ABDI) - Computational and Clinical Applications 2011, Toronto, Ontario, Canada, 18/09/11. https://doi.org/10.1007/978-3-642-28557-8_13

Liver tumor segmentation using kernel-based FGCM and PGCM. / Mandava, Rajeswari; Yeow, Lee Song; Chandra, Bhavik Anil et al.
Abdominal Imaging: Computational and Clinical Applications - Third International Workshop, Held in Conjunction with MICCAI 2011, Revised Selected Papers. 2012. p. 99-107 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7029 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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T1 - Liver tumor segmentation using kernel-based FGCM and PGCM

AU - Mandava, Rajeswari

AU - Yeow, Lee Song

AU - Chandra, Bhavik Anil

AU - Haur, Ong Kok

AU - Pasha, Muhammad Fermi

AU - Shuaib, Ibrahim Lutfi

N1 - Conference code: 3rd

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

KW - Kernelized Fuzzy C-Mean

KW - Liver Tumor

KW - Multi Feature Fuzzy C-Mean

KW - Tsallis Entropy

KW - Tumor Segmentation

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Abdominal Imaging

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Mandava R, Yeow LS, Chandra BA, Haur OK, Pasha MF, Shuaib IL. Liver tumor segmentation using kernel-based FGCM and PGCM. In Abdominal Imaging: Computational and Clinical Applications - Third International Workshop, Held in Conjunction with MICCAI 2011, Revised Selected Papers. 2012. p. 99-107. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-28557-8_13

Liver tumor segmentation using kernel-based FGCM and PGCM

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Keywords

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