Cluster validity of Xie and Beni and the partition coefficient indexes for fuzzy c-means clustering / Nor Azrin Ahmad Mustaffa

Under Image Processing, there is Image Segmentation. Image Segmentation is a subset of an expansive field of computer vision which deals with partition an image into meaningful regions with respect to a particular application. In particular, it is used to separate regions from the rest of the image,...

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Main Author:	Ahmad Mustaffa, Nor Azrin
Format:	Thesis
Language:	English
Published:	2010
Online Access:	https://ir.uitm.edu.my/id/eprint/64309/1/64309.PDF
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spelling	my-uitm-ir.643092023-09-12T01:51:33Z Cluster validity of Xie and Beni and the partition coefficient indexes for fuzzy c-means clustering / Nor Azrin Ahmad Mustaffa 2010 Ahmad Mustaffa, Nor Azrin Under Image Processing, there is Image Segmentation. Image Segmentation is a subset of an expansive field of computer vision which deals with partition an image into meaningful regions with respect to a particular application. In particular, it is used to separate regions from the rest of the image, in order to recognize them as objects. In this project, we implement fuzzy c-means (FCM) clustering which is the technique of segmentation into mammographic images. Segmentation defines the boundary of the targeted object from its background in the images. This project focuses on suspected region that may contain breast anomalies such as masses and calcifications. These breast anomalies may be diagnosed as cancer by radiologists. Therefore, segmentation of mammographic images is an important phase in image analysis that can be further applied to other algorithms for specific tasks such as the detection and classification of breast anomalies. The implementation of FCM for the segmentation of mammographic images is by using Mat lab. FCM is widely used technique in this regard but it requires the priori specification of the number of clusters. Therefore, this project is posed as one of optimization of a fuzzy cluster validity index. There are two validity measures in the context of fuzzy clustering that are being used which are Partition Coefficient and Xie and Beni index. We use C language to write down the cluster validity indexes. 2010 Thesis https://ir.uitm.edu.my/id/eprint/64309/ https://ir.uitm.edu.my/id/eprint/64309/1/64309.PDF text en public degree Universiti Teknologi MARA (UiTM) Faculty of Computer and Mathematical Sciences Embong, Rohana
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Embong, Rohana
description	Under Image Processing, there is Image Segmentation. Image Segmentation is a subset of an expansive field of computer vision which deals with partition an image into meaningful regions with respect to a particular application. In particular, it is used to separate regions from the rest of the image, in order to recognize them as objects. In this project, we implement fuzzy c-means (FCM) clustering which is the technique of segmentation into mammographic images. Segmentation defines the boundary of the targeted object from its background in the images. This project focuses on suspected region that may contain breast anomalies such as masses and calcifications. These breast anomalies may be diagnosed as cancer by radiologists. Therefore, segmentation of mammographic images is an important phase in image analysis that can be further applied to other algorithms for specific tasks such as the detection and classification of breast anomalies. The implementation of FCM for the segmentation of mammographic images is by using Mat lab. FCM is widely used technique in this regard but it requires the priori specification of the number of clusters. Therefore, this project is posed as one of optimization of a fuzzy cluster validity index. There are two validity measures in the context of fuzzy clustering that are being used which are Partition Coefficient and Xie and Beni index. We use C language to write down the cluster validity indexes.
format	Thesis
qualification_level	Bachelor degree
author	Ahmad Mustaffa, Nor Azrin
spellingShingle	Ahmad Mustaffa, Nor Azrin Cluster validity of Xie and Beni and the partition coefficient indexes for fuzzy c-means clustering / Nor Azrin Ahmad Mustaffa
author_facet	Ahmad Mustaffa, Nor Azrin
author_sort	Ahmad Mustaffa, Nor Azrin
title	Cluster validity of Xie and Beni and the partition coefficient indexes for fuzzy c-means clustering / Nor Azrin Ahmad Mustaffa
title_short	Cluster validity of Xie and Beni and the partition coefficient indexes for fuzzy c-means clustering / Nor Azrin Ahmad Mustaffa
title_full	Cluster validity of Xie and Beni and the partition coefficient indexes for fuzzy c-means clustering / Nor Azrin Ahmad Mustaffa
title_fullStr	Cluster validity of Xie and Beni and the partition coefficient indexes for fuzzy c-means clustering / Nor Azrin Ahmad Mustaffa
title_full_unstemmed	Cluster validity of Xie and Beni and the partition coefficient indexes for fuzzy c-means clustering / Nor Azrin Ahmad Mustaffa
title_sort	cluster validity of xie and beni and the partition coefficient indexes for fuzzy c-means clustering / nor azrin ahmad mustaffa
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2010
url	https://ir.uitm.edu.my/id/eprint/64309/1/64309.PDF
_version_	1783735438082572288

Cluster validity of Xie and Beni and the partition coefficient indexes for fuzzy c-means clustering / Nor Azrin Ahmad Mustaffa

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