An Optimized Approach for Prostate Image Segmentation Using K-Means Clustering Algorithm with Elbow Method

Prostate cancer disease is one of the common types that cause men’s prostate damage all over the world. Prostate-specific membrane antigen (PSMA) expressed by type-II is an extremely attractive style for imaging-based diagnosis of prostate cancer. Clinically, photodynamic therapy (PDT) is used as no...

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Autores principales:	Rachid Sammouda, Ali El-Zaart
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/2c15ec7e00e04808999f1ddebcd5e96c
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spelling	oai:doaj.org-article:2c15ec7e00e04808999f1ddebcd5e96c2021-11-29T00:56:53ZAn Optimized Approach for Prostate Image Segmentation Using K-Means Clustering Algorithm with Elbow Method1687-527310.1155/2021/4553832https://doaj.org/article/2c15ec7e00e04808999f1ddebcd5e96c2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/4553832https://doaj.org/toc/1687-5273Prostate cancer disease is one of the common types that cause men’s prostate damage all over the world. Prostate-specific membrane antigen (PSMA) expressed by type-II is an extremely attractive style for imaging-based diagnosis of prostate cancer. Clinically, photodynamic therapy (PDT) is used as noninvasive therapy in treatment of several cancers and some other diseases. This paper aims to segment or cluster and analyze pixels of histological and near-infrared (NIR) prostate cancer images acquired by PSMA-targeting PDT low weight molecular agents. Such agents can provide image guidance to resection of the prostate tumors and permit for the subsequent PDT in order to remove remaining or noneradicable cancer cells. The color prostate image segmentation is accomplished using an optimized image segmentation approach. The optimized approach combines the k-means clustering algorithm with elbow method that can give better clustering of pixels through automatically determining the best number of clusters. Clusters’ statistics and ratio results of pixels in the segmented images show the applicability of the proposed approach for giving the optimum number of clusters for prostate cancer analysis and diagnosis.Rachid SammoudaAli El-ZaartHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7Neurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENComputational Intelligence and Neuroscience, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Rachid Sammouda Ali El-Zaart An Optimized Approach for Prostate Image Segmentation Using K-Means Clustering Algorithm with Elbow Method
description	Prostate cancer disease is one of the common types that cause men’s prostate damage all over the world. Prostate-specific membrane antigen (PSMA) expressed by type-II is an extremely attractive style for imaging-based diagnosis of prostate cancer. Clinically, photodynamic therapy (PDT) is used as noninvasive therapy in treatment of several cancers and some other diseases. This paper aims to segment or cluster and analyze pixels of histological and near-infrared (NIR) prostate cancer images acquired by PSMA-targeting PDT low weight molecular agents. Such agents can provide image guidance to resection of the prostate tumors and permit for the subsequent PDT in order to remove remaining or noneradicable cancer cells. The color prostate image segmentation is accomplished using an optimized image segmentation approach. The optimized approach combines the k-means clustering algorithm with elbow method that can give better clustering of pixels through automatically determining the best number of clusters. Clusters’ statistics and ratio results of pixels in the segmented images show the applicability of the proposed approach for giving the optimum number of clusters for prostate cancer analysis and diagnosis.
format	article
author	Rachid Sammouda Ali El-Zaart
author_facet	Rachid Sammouda Ali El-Zaart
author_sort	Rachid Sammouda
title	An Optimized Approach for Prostate Image Segmentation Using K-Means Clustering Algorithm with Elbow Method
title_short	An Optimized Approach for Prostate Image Segmentation Using K-Means Clustering Algorithm with Elbow Method
title_full	An Optimized Approach for Prostate Image Segmentation Using K-Means Clustering Algorithm with Elbow Method
title_fullStr	An Optimized Approach for Prostate Image Segmentation Using K-Means Clustering Algorithm with Elbow Method
title_full_unstemmed	An Optimized Approach for Prostate Image Segmentation Using K-Means Clustering Algorithm with Elbow Method
title_sort	optimized approach for prostate image segmentation using k-means clustering algorithm with elbow method
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/2c15ec7e00e04808999f1ddebcd5e96c
work_keys_str_mv	AT rachidsammouda anoptimizedapproachforprostateimagesegmentationusingkmeansclusteringalgorithmwithelbowmethod AT alielzaart anoptimizedapproachforprostateimagesegmentationusingkmeansclusteringalgorithmwithelbowmethod AT rachidsammouda optimizedapproachforprostateimagesegmentationusingkmeansclusteringalgorithmwithelbowmethod AT alielzaart optimizedapproachforprostateimagesegmentationusingkmeansclusteringalgorithmwithelbowmethod
_version_	1718407634769412096

An Optimized Approach for Prostate Image Segmentation Using K-Means Clustering Algorithm with Elbow Method

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