Identifying abnormalities in computed tomography brain images using symmetrical features

W. M. Diyana, Wan Mimi Diyana Wan Zaki, CunRui Kong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

This paper proposes an automated method to identify abnormalities by exploiting symmetrical property features in Computed Tomography (CT) brain images. This method consists of two main steps; symmetrical axis detection and rule based abnormalities detection. Based on the principle axis theorem, any tilted intracranial is firstly corrected before symmetrical axis is generated. Then, segmented CT brains intracranial are divided into left half and right half used to produce possible feature vectors. Size (area) and location (centroid) of the abnormalities are chosen as main features for the development of the rule based abnormalities detection system. This experimental work uses twenty abnormal and eighty normal CT brain images and performance of proposed method is evaluated in term of sensitivity and specificity. It shows that the proposed automated method using symmetrical features proved to be efficient and accurate, and gives reliable results for every CT brain image tested.

Original languageEnglish
Title of host publicationProceedings of the 2009 International Conference on Electrical Engineering and Informatics, ICEEI 2009
Pages88-92
Number of pages5
Volume1
DOIs
Publication statusPublished - 2009
Event2009 International Conference on Electrical Engineering and Informatics, ICEEI 2009 - Selangor
Duration: 5 Aug 20097 Aug 2009

Other

Other2009 International Conference on Electrical Engineering and Informatics, ICEEI 2009
CitySelangor
Period5/8/097/8/09

Fingerprint

Tomography
Brain

Keywords

  • Automated detection
  • Brain abnormalities
  • CT brain images
  • Principle axis
  • Symmetrical features

ASJC Scopus subject areas

  • Information Systems
  • Software
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

Cite this

Diyana, W. M., Wan Zaki, W. M. D., & Kong, C. (2009). Identifying abnormalities in computed tomography brain images using symmetrical features. In Proceedings of the 2009 International Conference on Electrical Engineering and Informatics, ICEEI 2009 (Vol. 1, pp. 88-92). [5254809] https://doi.org/10.1109/ICEEI.2009.5254809

Identifying abnormalities in computed tomography brain images using symmetrical features. / Diyana, W. M.; Wan Zaki, Wan Mimi Diyana; Kong, CunRui.

Proceedings of the 2009 International Conference on Electrical Engineering and Informatics, ICEEI 2009. Vol. 1 2009. p. 88-92 5254809.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Diyana, WM, Wan Zaki, WMD & Kong, C 2009, Identifying abnormalities in computed tomography brain images using symmetrical features. in Proceedings of the 2009 International Conference on Electrical Engineering and Informatics, ICEEI 2009. vol. 1, 5254809, pp. 88-92, 2009 International Conference on Electrical Engineering and Informatics, ICEEI 2009, Selangor, 5/8/09. https://doi.org/10.1109/ICEEI.2009.5254809
Diyana WM, Wan Zaki WMD, Kong C. Identifying abnormalities in computed tomography brain images using symmetrical features. In Proceedings of the 2009 International Conference on Electrical Engineering and Informatics, ICEEI 2009. Vol. 1. 2009. p. 88-92. 5254809 https://doi.org/10.1109/ICEEI.2009.5254809
Diyana, W. M. ; Wan Zaki, Wan Mimi Diyana ; Kong, CunRui. / Identifying abnormalities in computed tomography brain images using symmetrical features. Proceedings of the 2009 International Conference on Electrical Engineering and Informatics, ICEEI 2009. Vol. 1 2009. pp. 88-92
@inproceedings{45c0d51c635141268d05f979824bcf3f,
title = "Identifying abnormalities in computed tomography brain images using symmetrical features",
abstract = "This paper proposes an automated method to identify abnormalities by exploiting symmetrical property features in Computed Tomography (CT) brain images. This method consists of two main steps; symmetrical axis detection and rule based abnormalities detection. Based on the principle axis theorem, any tilted intracranial is firstly corrected before symmetrical axis is generated. Then, segmented CT brains intracranial are divided into left half and right half used to produce possible feature vectors. Size (area) and location (centroid) of the abnormalities are chosen as main features for the development of the rule based abnormalities detection system. This experimental work uses twenty abnormal and eighty normal CT brain images and performance of proposed method is evaluated in term of sensitivity and specificity. It shows that the proposed automated method using symmetrical features proved to be efficient and accurate, and gives reliable results for every CT brain image tested.",
keywords = "Automated detection, Brain abnormalities, CT brain images, Principle axis, Symmetrical features",
author = "Diyana, {W. M.} and {Wan Zaki}, {Wan Mimi Diyana} and CunRui Kong",
year = "2009",
doi = "10.1109/ICEEI.2009.5254809",
language = "English",
isbn = "9781424449132",
volume = "1",
pages = "88--92",
booktitle = "Proceedings of the 2009 International Conference on Electrical Engineering and Informatics, ICEEI 2009",

}

TY - GEN

T1 - Identifying abnormalities in computed tomography brain images using symmetrical features

AU - Diyana, W. M.

AU - Wan Zaki, Wan Mimi Diyana

AU - Kong, CunRui

PY - 2009

Y1 - 2009

N2 - This paper proposes an automated method to identify abnormalities by exploiting symmetrical property features in Computed Tomography (CT) brain images. This method consists of two main steps; symmetrical axis detection and rule based abnormalities detection. Based on the principle axis theorem, any tilted intracranial is firstly corrected before symmetrical axis is generated. Then, segmented CT brains intracranial are divided into left half and right half used to produce possible feature vectors. Size (area) and location (centroid) of the abnormalities are chosen as main features for the development of the rule based abnormalities detection system. This experimental work uses twenty abnormal and eighty normal CT brain images and performance of proposed method is evaluated in term of sensitivity and specificity. It shows that the proposed automated method using symmetrical features proved to be efficient and accurate, and gives reliable results for every CT brain image tested.

AB - This paper proposes an automated method to identify abnormalities by exploiting symmetrical property features in Computed Tomography (CT) brain images. This method consists of two main steps; symmetrical axis detection and rule based abnormalities detection. Based on the principle axis theorem, any tilted intracranial is firstly corrected before symmetrical axis is generated. Then, segmented CT brains intracranial are divided into left half and right half used to produce possible feature vectors. Size (area) and location (centroid) of the abnormalities are chosen as main features for the development of the rule based abnormalities detection system. This experimental work uses twenty abnormal and eighty normal CT brain images and performance of proposed method is evaluated in term of sensitivity and specificity. It shows that the proposed automated method using symmetrical features proved to be efficient and accurate, and gives reliable results for every CT brain image tested.

KW - Automated detection

KW - Brain abnormalities

KW - CT brain images

KW - Principle axis

KW - Symmetrical features

UR - http://www.scopus.com/inward/record.url?scp=70449629550&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70449629550&partnerID=8YFLogxK

U2 - 10.1109/ICEEI.2009.5254809

DO - 10.1109/ICEEI.2009.5254809

M3 - Conference contribution

SN - 9781424449132

VL - 1

SP - 88

EP - 92

BT - Proceedings of the 2009 International Conference on Electrical Engineering and Informatics, ICEEI 2009

ER -