Evaluation of texture analysis techniques for characterization of multimode-based liver images using SGLCM and FOS

This paper is a study of using Spatial Grey-Level Co-occurrence Matrix (SGLCM) and First-Order Statistics (FOS), for characterization of liver tissue. SGLCM and FOS are applied on three modalities of liver images, consisting of Magnetic Resonance Imaging (MRI), Ultrasound and Computed Tomography (CT...

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Main Authors: Chung, S. H., Logeswaran, R.
Format: Conference or Workshop Item
Published: 2006
Subjects:
Online Access:http://shdl.mmu.edu.my/3204/
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author Chung, S. H.
Logeswaran, R.
author_facet Chung, S. H.
Logeswaran, R.
author_sort Chung, S. H.
building MMU Institutional Repository
collection Online Access
description This paper is a study of using Spatial Grey-Level Co-occurrence Matrix (SGLCM) and First-Order Statistics (FOS), for characterization of liver tissue. SGLCM and FOS are applied on three modalities of liver images, consisting of Magnetic Resonance Imaging (MRI), Ultrasound and Computed Tomography (CT), for the diagnosis of liver diseases. The results indicate that the proposed texture analysis methodology is able to characterize cyst, fatty liver and healthy liver in clinical test images with high success rates. The study indicates viable use of SGLCM and FOS in multimode image analysis and development of a texture-based multimode computer-aided diagnostic (CAD) system for liver diseases.
first_indexed 2025-11-14T18:09:50Z
format Conference or Workshop Item
id mmu-3204
institution Multimedia University
institution_category Local University
last_indexed 2025-11-14T18:09:50Z
publishDate 2006
recordtype eprints
repository_type Digital Repository
spelling mmu-32042011-10-18T06:03:55Z http://shdl.mmu.edu.my/3204/ Evaluation of texture analysis techniques for characterization of multimode-based liver images using SGLCM and FOS Chung, S. H. Logeswaran, R. T Technology (General) QA75.5-76.95 Electronic computers. Computer science This paper is a study of using Spatial Grey-Level Co-occurrence Matrix (SGLCM) and First-Order Statistics (FOS), for characterization of liver tissue. SGLCM and FOS are applied on three modalities of liver images, consisting of Magnetic Resonance Imaging (MRI), Ultrasound and Computed Tomography (CT), for the diagnosis of liver diseases. The results indicate that the proposed texture analysis methodology is able to characterize cyst, fatty liver and healthy liver in clinical test images with high success rates. The study indicates viable use of SGLCM and FOS in multimode image analysis and development of a texture-based multimode computer-aided diagnostic (CAD) system for liver diseases. 2006-12 Conference or Workshop Item NonPeerReviewed Chung, S. H. and Logeswaran, R. (2006) Evaluation of texture analysis techniques for characterization of multimode-based liver images using SGLCM and FOS. In: 3rd Kuala Lumpur International Conference on Biomedical Engineering 2006 (BioMed 2006) , 11-14 DEC 2006 , Kuala Lumpur, MALAYSIA . http://apps.webofknowledge.com/full_record.do?product=WOS&search_mode=GeneralSearch&qid=1&SID=Y2M4cogb143I6iogn9p&page=122&doc=1220
spellingShingle T Technology (General)
QA75.5-76.95 Electronic computers. Computer science
Chung, S. H.
Logeswaran, R.
Evaluation of texture analysis techniques for characterization of multimode-based liver images using SGLCM and FOS
title Evaluation of texture analysis techniques for characterization of multimode-based liver images using SGLCM and FOS
title_full Evaluation of texture analysis techniques for characterization of multimode-based liver images using SGLCM and FOS
title_fullStr Evaluation of texture analysis techniques for characterization of multimode-based liver images using SGLCM and FOS
title_full_unstemmed Evaluation of texture analysis techniques for characterization of multimode-based liver images using SGLCM and FOS
title_short Evaluation of texture analysis techniques for characterization of multimode-based liver images using SGLCM and FOS
title_sort evaluation of texture analysis techniques for characterization of multimode-based liver images using sglcm and fos
topic T Technology (General)
QA75.5-76.95 Electronic computers. Computer science
url http://shdl.mmu.edu.my/3204/
http://shdl.mmu.edu.my/3204/