Skin Lesion Extraction Using Multiscale Morphological Local Variance Reconstruction Based Watershed Transform and Fast Fuzzy C-Means Clustering

Early identification of melanocytic skin lesions increases the survival rate for skin cancer patients. Automated melanocytic skin lesion extraction from dermoscopic images using the computer vision approach is a challenging task as the lesions present in the image can be of different colors, there m...

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Autores principales:	Ranjita Rout, Priyadarsan Parida, Youseef Alotaibi, Saleh Alghamdi, Osamah Ibrahim Khalaf
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	lesion extraction fast FCM clustering watershed transform (WT) local variance Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/242183b7c9db498685fd8aad393a49d0
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spelling	oai:doaj.org-article:242183b7c9db498685fd8aad393a49d02021-11-25T19:06:37ZSkin Lesion Extraction Using Multiscale Morphological Local Variance Reconstruction Based Watershed Transform and Fast Fuzzy C-Means Clustering10.3390/sym131120852073-8994https://doaj.org/article/242183b7c9db498685fd8aad393a49d02021-11-01T00:00:00Zhttps://www.mdpi.com/2073-8994/13/11/2085https://doaj.org/toc/2073-8994Early identification of melanocytic skin lesions increases the survival rate for skin cancer patients. Automated melanocytic skin lesion extraction from dermoscopic images using the computer vision approach is a challenging task as the lesions present in the image can be of different colors, there may be a variation of contrast near the lesion boundaries, lesions may have different sizes and shapes, etc. Therefore, lesion extraction from dermoscopic images is a fundamental step for automated melanoma identification. In this article, a watershed transform based on the fast fuzzy c-means (FCM) clustering algorithm is proposed for the extraction of melanocytic skin lesion from dermoscopic images. Initially, the proposed method removes the artifacts from the dermoscopic images and enhances the texture regions. Further, it is filtered using a Gaussian filter and a local variance filter to enhance the lesion boundary regions. Later, the watershed transform based on MMLVR (multiscale morphological local variance reconstruction) is introduced to acquire the superpixels of the image with accurate boundary regions. Finally, the fast FCM clustering technique is implemented in the superpixels of the image to attain the final lesion extraction result. The proposed method is tested in the three publicly available skin lesion image datasets, i.e., ISIC 2016, ISIC 2017 and ISIC 2018. Experimental evaluation shows that the proposed method achieves a good result.Ranjita RoutPriyadarsan ParidaYouseef AlotaibiSaleh AlghamdiOsamah Ibrahim KhalafMDPI AGarticlelesion extractionfast FCM clusteringwatershed transform (WT)local varianceMathematicsQA1-939ENSymmetry, Vol 13, Iss 2085, p 2085 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	lesion extraction fast FCM clustering watershed transform (WT) local variance Mathematics QA1-939
spellingShingle	lesion extraction fast FCM clustering watershed transform (WT) local variance Mathematics QA1-939 Ranjita Rout Priyadarsan Parida Youseef Alotaibi Saleh Alghamdi Osamah Ibrahim Khalaf Skin Lesion Extraction Using Multiscale Morphological Local Variance Reconstruction Based Watershed Transform and Fast Fuzzy C-Means Clustering
description	Early identification of melanocytic skin lesions increases the survival rate for skin cancer patients. Automated melanocytic skin lesion extraction from dermoscopic images using the computer vision approach is a challenging task as the lesions present in the image can be of different colors, there may be a variation of contrast near the lesion boundaries, lesions may have different sizes and shapes, etc. Therefore, lesion extraction from dermoscopic images is a fundamental step for automated melanoma identification. In this article, a watershed transform based on the fast fuzzy c-means (FCM) clustering algorithm is proposed for the extraction of melanocytic skin lesion from dermoscopic images. Initially, the proposed method removes the artifacts from the dermoscopic images and enhances the texture regions. Further, it is filtered using a Gaussian filter and a local variance filter to enhance the lesion boundary regions. Later, the watershed transform based on MMLVR (multiscale morphological local variance reconstruction) is introduced to acquire the superpixels of the image with accurate boundary regions. Finally, the fast FCM clustering technique is implemented in the superpixels of the image to attain the final lesion extraction result. The proposed method is tested in the three publicly available skin lesion image datasets, i.e., ISIC 2016, ISIC 2017 and ISIC 2018. Experimental evaluation shows that the proposed method achieves a good result.
format	article
author	Ranjita Rout Priyadarsan Parida Youseef Alotaibi Saleh Alghamdi Osamah Ibrahim Khalaf
author_facet	Ranjita Rout Priyadarsan Parida Youseef Alotaibi Saleh Alghamdi Osamah Ibrahim Khalaf
author_sort	Ranjita Rout
title	Skin Lesion Extraction Using Multiscale Morphological Local Variance Reconstruction Based Watershed Transform and Fast Fuzzy C-Means Clustering
title_short	Skin Lesion Extraction Using Multiscale Morphological Local Variance Reconstruction Based Watershed Transform and Fast Fuzzy C-Means Clustering
title_full	Skin Lesion Extraction Using Multiscale Morphological Local Variance Reconstruction Based Watershed Transform and Fast Fuzzy C-Means Clustering
title_fullStr	Skin Lesion Extraction Using Multiscale Morphological Local Variance Reconstruction Based Watershed Transform and Fast Fuzzy C-Means Clustering
title_full_unstemmed	Skin Lesion Extraction Using Multiscale Morphological Local Variance Reconstruction Based Watershed Transform and Fast Fuzzy C-Means Clustering
title_sort	skin lesion extraction using multiscale morphological local variance reconstruction based watershed transform and fast fuzzy c-means clustering
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/242183b7c9db498685fd8aad393a49d0
work_keys_str_mv	AT ranjitarout skinlesionextractionusingmultiscalemorphologicallocalvariancereconstructionbasedwatershedtransformandfastfuzzycmeansclustering AT priyadarsanparida skinlesionextractionusingmultiscalemorphologicallocalvariancereconstructionbasedwatershedtransformandfastfuzzycmeansclustering AT youseefalotaibi skinlesionextractionusingmultiscalemorphologicallocalvariancereconstructionbasedwatershedtransformandfastfuzzycmeansclustering AT salehalghamdi skinlesionextractionusingmultiscalemorphologicallocalvariancereconstructionbasedwatershedtransformandfastfuzzycmeansclustering AT osamahibrahimkhalaf skinlesionextractionusingmultiscalemorphologicallocalvariancereconstructionbasedwatershedtransformandfastfuzzycmeansclustering
_version_	1718410265938100224

Skin Lesion Extraction Using Multiscale Morphological Local Variance Reconstruction Based Watershed Transform and Fast Fuzzy C-Means Clustering

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