Orientation Sensitive Fuzzy C Means Based Fast Level Set Evolution for Segmentation of Histopathological Images to Detect Skin Cancer

Masood, A; Al-Jumaily, A

Orientation Sensitive Fuzzy C Means Based Fast Level Set Evolution for Segmentation of Histopathological Images to Detect Skin Cancer

Masood, A Al-Jumaily, A

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Publisher:: Springer International Publishing
Publication Type:: Conference Proceeding
Citation:: Hybrid Intelligent Systems, 2020, 923, pp. 501-510
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Masood, A
dc.contributor.author	Al-Jumaily, A https://orcid.org/0000-0003-0297-2463
dc.date	2018-12-13
dc.date.accessioned	2021-05-06T06:33:14Z
dc.date.available	2021-05-06T06:33:14Z
dc.date.issued	2020-01-01
dc.identifier.citation	Hybrid Intelligent Systems, 2020, 923, pp. 501-510
dc.identifier.isbn	9783030143466
dc.identifier.issn	2194-5357
dc.identifier.issn	2194-5365
dc.identifier.uri	http://hdl.handle.net/10453/148752
dc.description.abstract	Malignant Melanoma in one of the most deadly skin cancer. In latest cancer diagnosis, pathologists examine biopsies for analysing cell morphology and tissue distribution for making diagnostic assessments. However, this process is quite subjective and has considerable variability. Automated computational diagnostic tools based on quantitative measures can help the diagnosis done by pathologists. The first and foremost step in automated histopathological image analysis is to properly segment the tissue structures such as nest, irregular distribution and melanocytic cells which indicate some disorder or potential cancer. This paper presents a novel technique for automatic segmentation of histopathological skin images, without user intervention. It is based on an innovative approach for utilizing the concepts of clustering and level set evolution. Firstly, the image is pre-processed to enhance the differentiating structural details. Then a novel orientation sensitive Fuzzy C mean clustering is used to generate the initial coarse segmentation and to calculate the controlling parameters for level set evolution. Later, refined fast level set based algorithm is used to finalize the segmentation process. Experimental analysis on a database of 150 histopathological images display the accuracy of the proposed method for detecting the melanocytic areas of the image, with true detection rate of 87.66% and Dice similarity coefficient of 0.88, when segmentation results are compared with images marked by expert pathologists.
dc.language	en
dc.publisher	Springer International Publishing
dc.relation.ispartof	Hybrid Intelligent Systems
dc.relation.ispartof	International Conference on Hybrid Intelligent Systems
dc.relation.ispartofseries	Advances in Intelligent Systems and Computing
dc.relation.isbasedon	10.1007/978-3-030-14347-3_49
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Orientation Sensitive Fuzzy C Means Based Fast Level Set Evolution for Segmentation of Histopathological Images to Detect Skin Cancer
dc.type	Conference Proceeding
utslib.citation.volume	923
utslib.location.activity	Portugal
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-05-06T06:33:13Z
pubs.finish-date	2018-12-15
pubs.publication-status	Published
pubs.start-date	2018-12-13
pubs.volume	923

Abstract:

Malignant Melanoma in one of the most deadly skin cancer. In latest cancer diagnosis, pathologists examine biopsies for analysing cell morphology and tissue distribution for making diagnostic assessments. However, this process is quite subjective and has considerable variability. Automated computational diagnostic tools based on quantitative measures can help the diagnosis done by pathologists. The first and foremost step in automated histopathological image analysis is to properly segment the tissue structures such as nest, irregular distribution and melanocytic cells which indicate some disorder or potential cancer. This paper presents a novel technique for automatic segmentation of histopathological skin images, without user intervention. It is based on an innovative approach for utilizing the concepts of clustering and level set evolution. Firstly, the image is pre-processed to enhance the differentiating structural details. Then a novel orientation sensitive Fuzzy C mean clustering is used to generate the initial coarse segmentation and to calculate the controlling parameters for level set evolution. Later, refined fast level set based algorithm is used to finalize the segmentation process. Experimental analysis on a database of 150 histopathological images display the accuracy of the proposed method for detecting the melanocytic areas of the image, with true detection rate of 87.66% and Dice similarity coefficient of 0.88, when segmentation results are compared with images marked by expert pathologists.

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http://hdl.handle.net/10453/148752