Using k-means clustering to classify microscopic colon images

This study reports on the performance of k-means clustering technique in classifying microscopic images of colonic tissue. Prior to the applications of the k-means clustering algorithm, the images were classified by a human expert according to 3 categories: normal, adenomatous polyp, and adenocarcin...

Full description

Saved in:

Bibliographic Details
Main Authors:	Gan Lim, Laurence A., Naguib, Raouf N. G., Avila, Jose Maria C.
Format:	text
Published:	Animo Repository 2008
Subjects:	Colon (Anatomy)—Cancer—Imaging Computer Engineering
Online Access:	https://animorepository.dlsu.edu.ph/faculty_research/6041
Tags:	Add Tag No Tags, Be the first to tag this record!
Institution:	De La Salle University

id	oai:animorepository.dlsu.edu.ph:faculty_research-6677
record_format	eprints
spelling	oai:animorepository.dlsu.edu.ph:faculty_research-66772022-06-09T05:53:18Z Using k-means clustering to classify microscopic colon images Gan Lim, Laurence A. Naguib, Raouf N. G. Avila, Jose Maria C. This study reports on the performance of k-means clustering technique in classifying microscopic images of colonic tissue. Prior to the applications of the k-means clustering algorithm, the images were classified by a human expert according to 3 categories: normal, adenomatous polyp, and adenocarcinoma or cancerous. The images were selected in order to produce 25 images for each category, totaling 75 images in all. The image properties used were texture quantities derived from grey-level co-occurrence matrices (GLCM). Results showed classification accuracies of 69%, 62%, and 40% for the adenomatous polyp, normal, and cancerous cases, respectively. 2008-01-01T08:00:00Z text https://animorepository.dlsu.edu.ph/faculty_research/6041 Faculty Research Work Animo Repository Colon (Anatomy)—Cancer—Imaging Computer Engineering
institution	De La Salle University
building	De La Salle University Library
continent	Asia
country	Philippines Philippines
content_provider	De La Salle University Library
collection	DLSU Institutional Repository
topic	Colon (Anatomy)—Cancer—Imaging Computer Engineering
spellingShingle	Colon (Anatomy)—Cancer—Imaging Computer Engineering Gan Lim, Laurence A. Naguib, Raouf N. G. Avila, Jose Maria C. Using k-means clustering to classify microscopic colon images
description	This study reports on the performance of k-means clustering technique in classifying microscopic images of colonic tissue. Prior to the applications of the k-means clustering algorithm, the images were classified by a human expert according to 3 categories: normal, adenomatous polyp, and adenocarcinoma or cancerous. The images were selected in order to produce 25 images for each category, totaling 75 images in all. The image properties used were texture quantities derived from grey-level co-occurrence matrices (GLCM). Results showed classification accuracies of 69%, 62%, and 40% for the adenomatous polyp, normal, and cancerous cases, respectively.
format	text
author	Gan Lim, Laurence A. Naguib, Raouf N. G. Avila, Jose Maria C.
author_facet	Gan Lim, Laurence A. Naguib, Raouf N. G. Avila, Jose Maria C.
author_sort	Gan Lim, Laurence A.
title	Using k-means clustering to classify microscopic colon images
title_short	Using k-means clustering to classify microscopic colon images
title_full	Using k-means clustering to classify microscopic colon images
title_fullStr	Using k-means clustering to classify microscopic colon images
title_full_unstemmed	Using k-means clustering to classify microscopic colon images
title_sort	using k-means clustering to classify microscopic colon images
publisher	Animo Repository
publishDate	2008
url	https://animorepository.dlsu.edu.ph/faculty_research/6041
_version_	1767196405061910528

Using k-means clustering to classify microscopic colon images

Similar Items