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,...

Full description

Saved in:
Bibliographic Details
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
Tags: Add Tag
No Tags, Be the first to tag this record!
id my-uitm-ir.64309
record_format uketd_dc
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