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 pr...
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University of Baghdad, College of Science for Women
2021-12-01
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| Series: | مجلة بغداد للعلوم |
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| Online Access: | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/4727 |
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| author | Ekhlas Falih Naser Suhiar Mohammed Zeki |
| author_facet | Ekhlas Falih Naser Suhiar Mohammed Zeki |
| author_sort | Ekhlas Falih Naser |
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| 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 |
| id | doaj-art-ff5341c1c21a4b81ba2d5da40ff99429 |
| institution | Kabale University |
| issn | 2078-8665 2411-7986 |
| language | English |
| publishDate | 2021-12-01 |
| publisher | University of Baghdad, College of Science for Women |
| record_format | Article |
| series | مجلة بغداد للعلوم |
| spelling | doaj-art-ff5341c1c21a4b81ba2d5da40ff994292025-08-20T03:58:04ZengUniversity of Baghdad, College of Science for Womenمجلة بغداد للعلوم2078-86652411-79862021-12-0118410.21123/bsj.2021.18.4.1294Using Fuzzy Clustering to Detect the Tumor Area in Stomach Medical ImagesEkhlas Falih Naser 0Suhiar Mohammed Zeki1Department of Computer Sciences, University of Technology, Baghdad, IraqDepartment of Computer Sciences, University of Technology, Baghdad, IraqAlthough 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.https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/4727Stomach, FCM, tumor, Seven Moments, Manhattan distance. |
| spellingShingle | Ekhlas Falih Naser Suhiar Mohammed Zeki Using Fuzzy Clustering to Detect the Tumor Area in Stomach Medical Images مجلة بغداد للعلوم Stomach, FCM, tumor, Seven Moments, Manhattan distance. |
| title | 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_short | 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 |
| topic | Stomach, FCM, tumor, Seven Moments, Manhattan distance. |
| url | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/4727 |
| work_keys_str_mv | AT ekhlasfalihnaser usingfuzzyclusteringtodetectthetumorareainstomachmedicalimages AT suhiarmohammedzeki usingfuzzyclusteringtodetectthetumorareainstomachmedicalimages |