Using Fuzzy Clustering to Detect the Tumor Area in Stomach Medical Images

Although the number of stomach tumor patients reduced obviously during last decades in western countries, but this illness is still one of the main causes of death in developing countries. The aim of this research is to detect the area of a tumor in a stomach images based on fuzzy clustering. The p...

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Autores principales:	Ekhlas Falih Naser, Suhiar Mohammed Zeki
Formato:	article
Lenguaje:	AR EN
Publicado:	College of Science for Women, University of Baghdad 2021
Materias:	Stomach, FCM, tumor, Seven Moments, Manhattan distance. Science Q
Acceso en línea:	https://doaj.org/article/ff5341c1c21a4b81ba2d5da40ff99429
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spelling	oai:doaj.org-article:ff5341c1c21a4b81ba2d5da40ff994292021-12-04T16:12:57ZUsing Fuzzy Clustering to Detect the Tumor Area in Stomach Medical Images10.21123/bsj.2021.18.4.12942078-86652411-7986https://doaj.org/article/ff5341c1c21a4b81ba2d5da40ff994292021-12-01T00:00:00Zhttps://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/4727https://doaj.org/toc/2078-8665https://doaj.org/toc/2411-7986 Although the number of stomach tumor patients reduced obviously during last decades in western countries, but this illness is still one of the main causes of death in developing countries. The aim of this research is to detect the area of a tumor in a stomach images based on fuzzy clustering. The proposed methodology consists of three stages. The stomach images are divided into four quarters and then features elicited from each quarter in the first stage by utilizing seven moments invariant. Fuzzy C-Mean clustering (FCM) was employed in the second stage for each quarter to collect the features of each quarter into clusters. Manhattan distance was calculated in the third stage among all clusters' centers in all quarters to disclosure of the quarter that contains a tumor based on the centroid value of the cluster in this quarter, which is far from the centers of the remaining quarters. From the calculations conducted on several images' quarters, the experimental outcomes show that the centroid value of the cluster in each quarter was greater than 0.9 if this quarter did not contain a tumor while the value of the centroid value for the cluster containing a tumor was less than 0.4.For examples, in a quarter no.1 for STOMACH_1 medical image, the centroid value of the cluster was 0.973 while the value of the cluster centroid in quarter no.3 was 0.280. For this reason the tumor area was found in quarter no.(3) of the medical image STOMACH_1. Also, the centroid value of the cluster in a quarter no.2 was 0.948 for STOMACH_2 while, the value of the cluster centroid in quarter no.4 was 0.397. For this reason the tumor area was found in a quarter no.4 of the medical image STOMACH_2. Ekhlas Falih Naser Suhiar Mohammed ZekiCollege of Science for Women, University of BaghdadarticleStomach, FCM, tumor, Seven Moments, Manhattan distance.ScienceQARENBaghdad Science Journal, Vol 18, Iss 4 (2021)
institution	DOAJ
collection	DOAJ
language	AR EN
topic	Stomach, FCM, tumor, Seven Moments, Manhattan distance. Science Q
spellingShingle	Stomach, FCM, tumor, Seven Moments, Manhattan distance. Science Q Ekhlas Falih Naser Suhiar Mohammed Zeki Using Fuzzy Clustering to Detect the Tumor Area in Stomach Medical Images
description	Although the number of stomach tumor patients reduced obviously during last decades in western countries, but this illness is still one of the main causes of death in developing countries. The aim of this research is to detect the area of a tumor in a stomach images based on fuzzy clustering. The proposed methodology consists of three stages. The stomach images are divided into four quarters and then features elicited from each quarter in the first stage by utilizing seven moments invariant. Fuzzy C-Mean clustering (FCM) was employed in the second stage for each quarter to collect the features of each quarter into clusters. Manhattan distance was calculated in the third stage among all clusters' centers in all quarters to disclosure of the quarter that contains a tumor based on the centroid value of the cluster in this quarter, which is far from the centers of the remaining quarters. From the calculations conducted on several images' quarters, the experimental outcomes show that the centroid value of the cluster in each quarter was greater than 0.9 if this quarter did not contain a tumor while the value of the centroid value for the cluster containing a tumor was less than 0.4.For examples, in a quarter no.1 for STOMACH_1 medical image, the centroid value of the cluster was 0.973 while the value of the cluster centroid in quarter no.3 was 0.280. For this reason the tumor area was found in quarter no.(3) of the medical image STOMACH_1. Also, the centroid value of the cluster in a quarter no.2 was 0.948 for STOMACH_2 while, the value of the cluster centroid in quarter no.4 was 0.397. For this reason the tumor area was found in a quarter no.4 of the medical image STOMACH_2.
format	article
author	Ekhlas Falih Naser Suhiar Mohammed Zeki
author_facet	Ekhlas Falih Naser Suhiar Mohammed Zeki
author_sort	Ekhlas Falih Naser
title	Using Fuzzy Clustering to Detect the Tumor Area in Stomach Medical Images
title_short	Using Fuzzy Clustering to Detect the Tumor Area in Stomach Medical Images
title_full	Using Fuzzy Clustering to Detect the Tumor Area in Stomach Medical Images
title_fullStr	Using Fuzzy Clustering to Detect the Tumor Area in Stomach Medical Images
title_full_unstemmed	Using Fuzzy Clustering to Detect the Tumor Area in Stomach Medical Images
title_sort	using fuzzy clustering to detect the tumor area in stomach medical images
publisher	College of Science for Women, University of Baghdad
publishDate	2021
url	https://doaj.org/article/ff5341c1c21a4b81ba2d5da40ff99429
work_keys_str_mv	AT ekhlasfalihnaser usingfuzzyclusteringtodetectthetumorareainstomachmedicalimages AT suhiarmohammedzeki usingfuzzyclusteringtodetectthetumorareainstomachmedicalimages
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Using Fuzzy Clustering to Detect the Tumor Area in Stomach Medical Images

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