Feature extraction of kidney ultrasound images based on intensity histogram and gray level co-occurrence matrix
This study proposes an approach of feature extraction of kidney ultrasound images based on five intensity histogram features and nineteen gray level co-occurrence matrix (GLCM) features. Kidney ultrasound images were divided into four different groups; normal (NR), bacterial infection (BI), cystic d...
Saved in:
Main Authors: | , , |
---|---|
Format: | Book Section |
Published: |
IEEE
2012
|
Subjects: | |
Online Access: | http://eprints.utm.my/id/eprint/35778/ https://www.researchgate.net/publication/261460328_Feature_Extraction_of_Kidney_Ultrasound_Images_Based_on_Intensity_Histogram_and_Gray_Level_Co-occurrence_Matrix |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.utm.35778 |
---|---|
record_format |
eprints |
spelling |
my.utm.357782017-06-13T03:48:59Z http://eprints.utm.my/id/eprint/35778/ Feature extraction of kidney ultrasound images based on intensity histogram and gray level co-occurrence matrix Hafizah, Wan Mahani Supriyanto, Eko Yunus, Jasmy Q Science This study proposes an approach of feature extraction of kidney ultrasound images based on five intensity histogram features and nineteen gray level co-occurrence matrix (GLCM) features. Kidney ultrasound images were divided into four different groups; normal (NR), bacterial infection (BI), cystic disease (CD) and kidney stones (KS). Before feature extraction, the images were initially preprocessed for preserving pixels of interest prior to feature extraction. Preprocessing techniques including region of interest cropping, contour detection, image rotation and background removal, have been applied. Test result shows that kurtosis, mean, skewness, cluster shades and cluster prominence dominates over other parameters. After normalization, KS group has highest value of kurtosis (1.000) and lowest value of cluster shades (0.238) and mean (0.649) while NR group has highest value of mean (1.000), skewness (1.000), cluster shades (1.000) and cluster prominence (1.000). CD group has the lowest value of skewness (0.625) and BI has the lowest value of kurtosis (0.542). This shows that these features can be used to classify kidney ultrasound images into different groups for creating database of kidney ultrasound images with different pathologies. IEEE 2012 Book Section PeerReviewed Hafizah, Wan Mahani and Supriyanto, Eko and Yunus, Jasmy (2012) Feature extraction of kidney ultrasound images based on intensity histogram and gray level co-occurrence matrix. In: Proceedings - 6th Asia International Conference on Mathematical Modelling and Computer Simulation, AMS 2012. IEEE, New York, USA, pp. 115-120. ISBN 978-076954730-5 https://www.researchgate.net/publication/261460328_Feature_Extraction_of_Kidney_Ultrasound_Images_Based_on_Intensity_Histogram_and_Gray_Level_Co-occurrence_Matrix DOI:10.1109/AMS.2012.47 |
institution |
Universiti Teknologi Malaysia |
building |
UTM Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Teknologi Malaysia |
content_source |
UTM Institutional Repository |
url_provider |
http://eprints.utm.my/ |
topic |
Q Science |
spellingShingle |
Q Science Hafizah, Wan Mahani Supriyanto, Eko Yunus, Jasmy Feature extraction of kidney ultrasound images based on intensity histogram and gray level co-occurrence matrix |
description |
This study proposes an approach of feature extraction of kidney ultrasound images based on five intensity histogram features and nineteen gray level co-occurrence matrix (GLCM) features. Kidney ultrasound images were divided into four different groups; normal (NR), bacterial infection (BI), cystic disease (CD) and kidney stones (KS). Before feature extraction, the images were initially preprocessed for preserving pixels of interest prior to feature extraction. Preprocessing techniques including region of interest cropping, contour detection, image rotation and background removal, have been applied. Test result shows that kurtosis, mean, skewness, cluster shades and cluster prominence dominates over other parameters. After normalization, KS group has highest value of kurtosis (1.000) and lowest value of cluster shades (0.238) and mean (0.649) while NR group has highest value of mean (1.000), skewness (1.000), cluster shades (1.000) and cluster prominence (1.000). CD group has the lowest value of skewness (0.625) and BI has the lowest value of kurtosis (0.542). This shows that these features can be used to classify kidney ultrasound images into different groups for creating database of kidney ultrasound images with different pathologies. |
format |
Book Section |
author |
Hafizah, Wan Mahani Supriyanto, Eko Yunus, Jasmy |
author_facet |
Hafizah, Wan Mahani Supriyanto, Eko Yunus, Jasmy |
author_sort |
Hafizah, Wan Mahani |
title |
Feature extraction of kidney ultrasound images based on intensity histogram and gray level co-occurrence matrix |
title_short |
Feature extraction of kidney ultrasound images based on intensity histogram and gray level co-occurrence matrix |
title_full |
Feature extraction of kidney ultrasound images based on intensity histogram and gray level co-occurrence matrix |
title_fullStr |
Feature extraction of kidney ultrasound images based on intensity histogram and gray level co-occurrence matrix |
title_full_unstemmed |
Feature extraction of kidney ultrasound images based on intensity histogram and gray level co-occurrence matrix |
title_sort |
feature extraction of kidney ultrasound images based on intensity histogram and gray level co-occurrence matrix |
publisher |
IEEE |
publishDate |
2012 |
url |
http://eprints.utm.my/id/eprint/35778/ https://www.researchgate.net/publication/261460328_Feature_Extraction_of_Kidney_Ultrasound_Images_Based_on_Intensity_Histogram_and_Gray_Level_Co-occurrence_Matrix |
_version_ |
1643649837884768256 |
score |
13.160551 |