Boundary detection in medical images using edge following algorithm based on intensity gradient and texture gradient features

Finding the correct boundary in noisy images is still a difficult task. This paper introduces a new edge following technique for boundary detection in noisy images. Utilization of the proposed technique is exhibited via its application to various types of medical images. Our proposed technique can d...

Full description

Saved in:
Bibliographic Details
Main Authors: Somkantha K., Theera-Umpon N., Auephanwiriyakul S.
Format: Article
Language:English
Published: 2014
Online Access:http://www.scopus.com/inward/record.url?eid=2-s2.0-79952158160&partnerID=40&md5=dbab88e8511379c002db24df27ca924f
http://www.ncbi.nlm.nih.gov/pubmed/21062676
http://cmuir.cmu.ac.th/handle/6653943832/1421
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Chiang Mai University
Language: English
id th-cmuir.6653943832-1421
record_format dspace
spelling th-cmuir.6653943832-14212014-08-29T09:29:17Z Boundary detection in medical images using edge following algorithm based on intensity gradient and texture gradient features Somkantha K. Theera-Umpon N. Auephanwiriyakul S. Finding the correct boundary in noisy images is still a difficult task. This paper introduces a new edge following technique for boundary detection in noisy images. Utilization of the proposed technique is exhibited via its application to various types of medical images. Our proposed technique can detect the boundaries of objects in noisy images using the information from the intensity gradient via the vector image model and the texture gradient via the edge map. The performance and robustness of the technique have been tested to segment objects in synthetic noisy images and medical images including prostates in ultrasound images, left ventricles in cardiac magnetic resonance (MR) images, aortas in cardiovascular MR images, and knee joints in computerized tomography images. We compare the proposed segmentation technique with the active contour models (ACM), geodesic active contour models, active contours without edges, gradient vector flow snake models, and ACMs based on vector field convolution, by using the skilled doctors' opinions as the ground truths. The results show that our technique performs very well and yields better performance than the classical contour models. The proposed method is robust and applicable on various kinds of noisy images without prior knowledge of noise properties. © 2006 IEEE. 2014-08-29T09:29:17Z 2014-08-29T09:29:17Z 2011 Article 189294 10.1109/TBME.2010.2091129 21062676 IEBEA http://www.scopus.com/inward/record.url?eid=2-s2.0-79952158160&partnerID=40&md5=dbab88e8511379c002db24df27ca924f http://www.ncbi.nlm.nih.gov/pubmed/21062676 http://cmuir.cmu.ac.th/handle/6653943832/1421 English
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
language English
description Finding the correct boundary in noisy images is still a difficult task. This paper introduces a new edge following technique for boundary detection in noisy images. Utilization of the proposed technique is exhibited via its application to various types of medical images. Our proposed technique can detect the boundaries of objects in noisy images using the information from the intensity gradient via the vector image model and the texture gradient via the edge map. The performance and robustness of the technique have been tested to segment objects in synthetic noisy images and medical images including prostates in ultrasound images, left ventricles in cardiac magnetic resonance (MR) images, aortas in cardiovascular MR images, and knee joints in computerized tomography images. We compare the proposed segmentation technique with the active contour models (ACM), geodesic active contour models, active contours without edges, gradient vector flow snake models, and ACMs based on vector field convolution, by using the skilled doctors' opinions as the ground truths. The results show that our technique performs very well and yields better performance than the classical contour models. The proposed method is robust and applicable on various kinds of noisy images without prior knowledge of noise properties. © 2006 IEEE.
format Article
author Somkantha K.
Theera-Umpon N.
Auephanwiriyakul S.
spellingShingle Somkantha K.
Theera-Umpon N.
Auephanwiriyakul S.
Boundary detection in medical images using edge following algorithm based on intensity gradient and texture gradient features
author_facet Somkantha K.
Theera-Umpon N.
Auephanwiriyakul S.
author_sort Somkantha K.
title Boundary detection in medical images using edge following algorithm based on intensity gradient and texture gradient features
title_short Boundary detection in medical images using edge following algorithm based on intensity gradient and texture gradient features
title_full Boundary detection in medical images using edge following algorithm based on intensity gradient and texture gradient features
title_fullStr Boundary detection in medical images using edge following algorithm based on intensity gradient and texture gradient features
title_full_unstemmed Boundary detection in medical images using edge following algorithm based on intensity gradient and texture gradient features
title_sort boundary detection in medical images using edge following algorithm based on intensity gradient and texture gradient features
publishDate 2014
url http://www.scopus.com/inward/record.url?eid=2-s2.0-79952158160&partnerID=40&md5=dbab88e8511379c002db24df27ca924f
http://www.ncbi.nlm.nih.gov/pubmed/21062676
http://cmuir.cmu.ac.th/handle/6653943832/1421
_version_ 1681419667409731584