Cover Image

A robust technique of brain MRI classification using color features and k-nearest neighbor algorithm

The analysis of MRI images is a manual process carried by experts which need to be automated to accurately classify the normal and abnormal images. We have proposed a reduced, three stage model having per-processing feature extraction and classification steps. in pre-processing the noise has been r...

Full description

Saved in:
Bibliographic Details
Main Authors: Fayaz, Muhammad, Shah, Abdul Salam, Wahid, Fazli, Shah, Asadullah
Format: Article
Language:English
Published: Science & Engineering Research Support soCiety 2016
Subjects:
T10.5 Communication of technical information
Online Access:http://irep.iium.edu.my/52576/1/Abdul-salam-oct-2016-paper-A-Robust-Technique-of-Brain-MRI-Classification-using-Color-Features-and-K-Nearest-Neighbors-Algorithm.pdf
http://irep.iium.edu.my/52576/
http://www.sersc.org/journals/IJSIP/vol9_no10/2.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.iium.irep.52576
record_format dspace
spelling my.iium.irep.525762017-04-05T04:43:32Z http://irep.iium.edu.my/52576/ A robust technique of brain MRI classification using color features and k-nearest neighbor algorithm Fayaz, Muhammad Shah, Abdul Salam Wahid, Fazli Shah, Asadullah T10.5 Communication of technical information The analysis of MRI images is a manual process carried by experts which need to be automated to accurately classify the normal and abnormal images. We have proposed a reduced, three stage model having per-processing feature extraction and classification steps. in pre-processing the noise has been removed from gray scale images using a median filter, and than gray sclae images have been converted to color (RGB) images. In feature extraction, red, green and blue channels from each channel of RGB has been extracted because they are so much informative and easier to process. The first three color moments mean, variance, and skewness are calculated for eaqch red, green and blue channel images. The feature extraction in the feature extraction stage are classified into normal and abnormal with K-Nearest Neighbor (K, NN). This method is applied to 100 images ( 70 normal , and 30 abnormal). The proposed method gives 98.00% training and 95% test accuracy with databases of normal images and 100% training and 90% test accuracy with abnormal images. The average computation time for each image was 0.06s. Science & Engineering Research Support soCiety 2016 Article REM application/pdf en http://irep.iium.edu.my/52576/1/Abdul-salam-oct-2016-paper-A-Robust-Technique-of-Brain-MRI-Classification-using-Color-Features-and-K-Nearest-Neighbors-Algorithm.pdf Fayaz, Muhammad and Shah, Abdul Salam and Wahid, Fazli and Shah, Asadullah (2016) A robust technique of brain MRI classification using color features and k-nearest neighbor algorithm. International Journal of Signal Processing, Image Processing and Pattern Recognition, 9 (10). pp. 11-20. ISSN 2005-4254 http://www.sersc.org/journals/IJSIP/vol9_no10/2.pdf 10.14257/ijsip.2016.9.10.02
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic T10.5 Communication of technical information
spellingShingle T10.5 Communication of technical information
Fayaz, Muhammad
Shah, Abdul Salam
Wahid, Fazli
Shah, Asadullah
A robust technique of brain MRI classification using color features and k-nearest neighbor algorithm
description The analysis of MRI images is a manual process carried by experts which need to be automated to accurately classify the normal and abnormal images. We have proposed a reduced, three stage model having per-processing feature extraction and classification steps. in pre-processing the noise has been removed from gray scale images using a median filter, and than gray sclae images have been converted to color (RGB) images. In feature extraction, red, green and blue channels from each channel of RGB has been extracted because they are so much informative and easier to process. The first three color moments mean, variance, and skewness are calculated for eaqch red, green and blue channel images. The feature extraction in the feature extraction stage are classified into normal and abnormal with K-Nearest Neighbor (K, NN). This method is applied to 100 images ( 70 normal , and 30 abnormal). The proposed method gives 98.00% training and 95% test accuracy with databases of normal images and 100% training and 90% test accuracy with abnormal images. The average computation time for each image was 0.06s.
format Article
author Fayaz, Muhammad
Shah, Abdul Salam
Wahid, Fazli
Shah, Asadullah
author_facet Fayaz, Muhammad
Shah, Abdul Salam
Wahid, Fazli
Shah, Asadullah
author_sort Fayaz, Muhammad
title A robust technique of brain MRI classification using color features and k-nearest neighbor algorithm
title_short A robust technique of brain MRI classification using color features and k-nearest neighbor algorithm
title_full A robust technique of brain MRI classification using color features and k-nearest neighbor algorithm
title_fullStr A robust technique of brain MRI classification using color features and k-nearest neighbor algorithm
title_full_unstemmed A robust technique of brain MRI classification using color features and k-nearest neighbor algorithm
title_sort robust technique of brain mri classification using color features and k-nearest neighbor algorithm
publisher Science & Engineering Research Support soCiety
publishDate 2016
url http://irep.iium.edu.my/52576/1/Abdul-salam-oct-2016-paper-A-Robust-Technique-of-Brain-MRI-Classification-using-Color-Features-and-K-Nearest-Neighbors-Algorithm.pdf
http://irep.iium.edu.my/52576/
http://www.sersc.org/journals/IJSIP/vol9_no10/2.pdf
_version_ 1643614187437424640
score 13.211869

Similar Items