Texture and medical image classification using Wavelet Completed Local Ternary Count (WCLTC) texture descriptor

A Completed Local Ternary Count (CLTC) was proposed by integrating the Local Ternary Pattern (LTP) with the Completed Local Binary Count (CLBC) to overcome the noise sensitivity and the improve the discriminative property. Moreover, the discriminative property of the proposed CLTC is improved by com...

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Main Authors:	Fatimah, A. Alkareem, Mohammed, Mohammed Falah, Rassem, Taha H.
Format:	Conference or Workshop Item
Language:	English English
Published:	2018
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/22464/ http://umpir.ump.edu.my/id/eprint/22464/1/63.%20Texture%20and%20medical%20image%20classification%20using%20wavelet%20completed.pdf http://umpir.ump.edu.my/id/eprint/22464/2/63.1%20Texture%20and%20medical%20image%20classification%20using%20wavelet%20completed.pdf

id	ump-22464
recordtype	eprints
spelling	ump-224642018-12-13T02:42:09Z http://umpir.ump.edu.my/id/eprint/22464/ Texture and medical image classification using Wavelet Completed Local Ternary Count (WCLTC) texture descriptor Fatimah, A. Alkareem Mohammed, Mohammed Falah Rassem, Taha H. QA76 Computer software A Completed Local Ternary Count (CLTC) was proposed by integrating the Local Ternary Pattern (LTP) with the Completed Local Binary Count (CLBC) to overcome the noise sensitivity and the improve the discriminative property. Moreover, the discriminative property of the proposed CLTC is improved by combining it with Redundant Discrete Wavelet Transform (RDWT) to construct a Wavelet Completed Local Ternary Count (WCLTC). As a result, more accurate capturing of the local texture within RDWT domain can be done and the exact measure of local texture can be used. The proposed WCLTC is used for image classification task in terms of texture and medical images. Two benchmark texture datasets which are CURTEX and OuTex while three medical image databases which are 2D Hela, VIRUS Texture and BR datasets are used to evaluate the WCLTC performance. The experimental results show an impressive classification accuracy with some of these datasets. The WCLTC performance outperformed the previous descriptors in many cases. The WCLTC achieve highest classification accuracy reach 96.91%, 97.04%, and 72.89% with 2D Hela, CURTEX and Virus datasets, respectively. 2018 Conference or Workshop Item NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/22464/1/63.%20Texture%20and%20medical%20image%20classification%20using%20wavelet%20completed.pdf pdf en http://umpir.ump.edu.my/id/eprint/22464/2/63.1%20Texture%20and%20medical%20image%20classification%20using%20wavelet%20completed.pdf Fatimah, A. Alkareem and Mohammed, Mohammed Falah and Rassem, Taha H. (2018) Texture and medical image classification using Wavelet Completed Local Ternary Count (WCLTC) texture descriptor. In: 8th Conference On Innovative Computing Technology (INTECH 2018), 15 - 17 Aug 2018 , Luton, UK. pp. 1-8.. (Unpublished)
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Fatimah, A. Alkareem Mohammed, Mohammed Falah Rassem, Taha H. Texture and medical image classification using Wavelet Completed Local Ternary Count (WCLTC) texture descriptor
description	A Completed Local Ternary Count (CLTC) was proposed by integrating the Local Ternary Pattern (LTP) with the Completed Local Binary Count (CLBC) to overcome the noise sensitivity and the improve the discriminative property. Moreover, the discriminative property of the proposed CLTC is improved by combining it with Redundant Discrete Wavelet Transform (RDWT) to construct a Wavelet Completed Local Ternary Count (WCLTC). As a result, more accurate capturing of the local texture within RDWT domain can be done and the exact measure of local texture can be used. The proposed WCLTC is used for image classification task in terms of texture and medical images. Two benchmark texture datasets which are CURTEX and OuTex while three medical image databases which are 2D Hela, VIRUS Texture and BR datasets are used to evaluate the WCLTC performance. The experimental results show an impressive classification accuracy with some of these datasets. The WCLTC performance outperformed the previous descriptors in many cases. The WCLTC achieve highest classification accuracy reach 96.91%, 97.04%, and 72.89% with 2D Hela, CURTEX and Virus datasets, respectively.
format	Conference or Workshop Item
author	Fatimah, A. Alkareem Mohammed, Mohammed Falah Rassem, Taha H.
author_facet	Fatimah, A. Alkareem Mohammed, Mohammed Falah Rassem, Taha H.
author_sort	Fatimah, A. Alkareem
title	Texture and medical image classification using Wavelet Completed Local Ternary Count (WCLTC) texture descriptor
title_short	Texture and medical image classification using Wavelet Completed Local Ternary Count (WCLTC) texture descriptor
title_full	Texture and medical image classification using Wavelet Completed Local Ternary Count (WCLTC) texture descriptor
title_fullStr	Texture and medical image classification using Wavelet Completed Local Ternary Count (WCLTC) texture descriptor
title_full_unstemmed	Texture and medical image classification using Wavelet Completed Local Ternary Count (WCLTC) texture descriptor
title_sort	texture and medical image classification using wavelet completed local ternary count (wcltc) texture descriptor
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/22464/ http://umpir.ump.edu.my/id/eprint/22464/1/63.%20Texture%20and%20medical%20image%20classification%20using%20wavelet%20completed.pdf http://umpir.ump.edu.my/id/eprint/22464/2/63.1%20Texture%20and%20medical%20image%20classification%20using%20wavelet%20completed.pdf
first_indexed	2023-09-18T22:33:27Z
last_indexed	2023-09-18T22:33:27Z
_version_	1777416439018291200

Texture and medical image classification using Wavelet Completed Local Ternary Count (WCLTC) texture descriptor

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