Comparison of fuzzy C means and K means clustering technique using color segmentation for prostate cancer cell images / Nuratiqah Mohd Zahari

Comparison of fuzzy C means and K means clustering technique using color segmentation for prostate cancer cell images / Nuratiqah Mohd Zahari

Segmentation of an image entails the division or separation of the image into regions of similar attribute. The most basic attribute for segmentation of an image is its color components for a color image. Clustering is one of the methods used for segmentation. The aim of the project is to investigat...

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Main Author: Mohd Zahari, Nuratiqah
Format: Thesis
Language:English
Published: 2012
Online Access:https://ir.uitm.edu.my/id/eprint/87116/1/87116.pdf
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spelling my-uitm-ir.871162024-01-29T15:17:51Z Comparison of fuzzy C means and K means clustering technique using color segmentation for prostate cancer cell images / Nuratiqah Mohd Zahari 2012 Mohd Zahari, Nuratiqah Segmentation of an image entails the division or separation of the image into regions of similar attribute. The most basic attribute for segmentation of an image is its color components for a color image. Clustering is one of the methods used for segmentation. The aim of the project is to investigate two methods of segmentation based on accuracy and efficiency. Twenty color images of prostate cancer cell are converted into L*a*b*color space and are segmented using Fuzzy C-Means clustering and K-Means clustering. Accuracy of segmentation are judged by visually inspecting the abnormal cell area, which is brown in color. Segmentation time is also measured to determine which clustering technique is faster. Results showed that Fuzzy C-Means clustering produced better segmentation results. However, K-Means clustering technique is faster compared to Fuzzy C-Means clustering. 2012 Thesis https://ir.uitm.edu.my/id/eprint/87116/ https://ir.uitm.edu.my/id/eprint/87116/1/87116.pdf text en public degree Universiti Teknologi MARA (UiTM) Faculty of Computer and Mathematical Sciences Jamil, Nursuriati
institution Universiti Teknologi MARA
collection UiTM Institutional Repository
language English
advisor Jamil, Nursuriati
description Segmentation of an image entails the division or separation of the image into regions of similar attribute. The most basic attribute for segmentation of an image is its color components for a color image. Clustering is one of the methods used for segmentation. The aim of the project is to investigate two methods of segmentation based on accuracy and efficiency. Twenty color images of prostate cancer cell are converted into L*a*b*color space and are segmented using Fuzzy C-Means clustering and K-Means clustering. Accuracy of segmentation are judged by visually inspecting the abnormal cell area, which is brown in color. Segmentation time is also measured to determine which clustering technique is faster. Results showed that Fuzzy C-Means clustering produced better segmentation results. However, K-Means clustering technique is faster compared to Fuzzy C-Means clustering.
format Thesis
qualification_level Bachelor degree
author Mohd Zahari, Nuratiqah
spellingShingle Mohd Zahari, Nuratiqah
Comparison of fuzzy C means and K means clustering technique using color segmentation for prostate cancer cell images / Nuratiqah Mohd Zahari
author_facet Mohd Zahari, Nuratiqah
author_sort Mohd Zahari, Nuratiqah
title Comparison of fuzzy C means and K means clustering technique using color segmentation for prostate cancer cell images / Nuratiqah Mohd Zahari
title_short Comparison of fuzzy C means and K means clustering technique using color segmentation for prostate cancer cell images / Nuratiqah Mohd Zahari
title_full Comparison of fuzzy C means and K means clustering technique using color segmentation for prostate cancer cell images / Nuratiqah Mohd Zahari
title_fullStr Comparison of fuzzy C means and K means clustering technique using color segmentation for prostate cancer cell images / Nuratiqah Mohd Zahari
title_full_unstemmed Comparison of fuzzy C means and K means clustering technique using color segmentation for prostate cancer cell images / Nuratiqah Mohd Zahari
title_sort comparison of fuzzy c means and k means clustering technique using color segmentation for prostate cancer cell images / nuratiqah mohd zahari
granting_institution Universiti Teknologi MARA (UiTM)
granting_department Faculty of Computer and Mathematical Sciences
publishDate 2012
url https://ir.uitm.edu.my/id/eprint/87116/1/87116.pdf
_version_ 1794192112012492800

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