Haralick texture and invariant moments features for breast cancer classification

Classification of breast cancer is essential in determining the type of treatment that should be applied. Thus, a Computer Aided Diagnosis (CADx) may assist radiologists in making appropriate decision based on the classification results. In this paper, the classification is divided into two categori...

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Main Authors: Yasiran, Siti Salmah, Salleh, Shaharuddin, Mahmud, Rozi
Format: Conference or Workshop Item
Language:English
Published: AIP Publishing 2015
Online Access:http://psasir.upm.edu.my/id/eprint/57618/1/Haralick%20texture%20and%20invariant%20moments%20features%20for%20breast%20cancer%20classification.pdf
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spelling oai:psasir.upm.edu.my:57618 http://psasir.upm.edu.my/id/eprint/57618/ Haralick texture and invariant moments features for breast cancer classification Yasiran, Siti Salmah Salleh, Shaharuddin Mahmud, Rozi Classification of breast cancer is essential in determining the type of treatment that should be applied. Thus, a Computer Aided Diagnosis (CADx) may assist radiologists in making appropriate decision based on the classification results. In this paper, the classification is divided into two categories; to classify the cancer into benign and malignant (two classes) and to classify the character of the background tissue either fatty, glandular or dense (multi class). The Haralick texture features and Hu Invariants moments were proposed as the features extraction. There are three phases conducted in this study. The first phase is the pre-processing phase. This is followed by the features extraction phase where combination of moment based features with addition of four features was proposed. The final phase is the classification phase by using SVM classifiers. Results obtained shows that the accuracy of the proposed features are 90.5% and 77.5% for two classes and multi class respectively. AIP Publishing 2015 Conference or Workshop Item PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/57618/1/Haralick%20texture%20and%20invariant%20moments%20features%20for%20breast%20cancer%20classification.pdf Yasiran, Siti Salmah and Salleh, Shaharuddin and Mahmud, Rozi (2015) Haralick texture and invariant moments features for breast cancer classification. In: 23rd Malaysian National Symposium of Mathematical Sciences (SKSM23), 24-26 Nov. 2015, Johor Bahru, Malaysia. (pp. 1-6). 10.1063/1.4954535
institution UPM IR
collection UPM IR
language English
description Classification of breast cancer is essential in determining the type of treatment that should be applied. Thus, a Computer Aided Diagnosis (CADx) may assist radiologists in making appropriate decision based on the classification results. In this paper, the classification is divided into two categories; to classify the cancer into benign and malignant (two classes) and to classify the character of the background tissue either fatty, glandular or dense (multi class). The Haralick texture features and Hu Invariants moments were proposed as the features extraction. There are three phases conducted in this study. The first phase is the pre-processing phase. This is followed by the features extraction phase where combination of moment based features with addition of four features was proposed. The final phase is the classification phase by using SVM classifiers. Results obtained shows that the accuracy of the proposed features are 90.5% and 77.5% for two classes and multi class respectively.
format Conference or Workshop Item
author Yasiran, Siti Salmah
Salleh, Shaharuddin
Mahmud, Rozi
spellingShingle Yasiran, Siti Salmah
Salleh, Shaharuddin
Mahmud, Rozi
Haralick texture and invariant moments features for breast cancer classification
author_facet Yasiran, Siti Salmah
Salleh, Shaharuddin
Mahmud, Rozi
author_sort Yasiran, Siti Salmah
title Haralick texture and invariant moments features for breast cancer classification
title_short Haralick texture and invariant moments features for breast cancer classification
title_full Haralick texture and invariant moments features for breast cancer classification
title_fullStr Haralick texture and invariant moments features for breast cancer classification
title_full_unstemmed Haralick texture and invariant moments features for breast cancer classification
title_sort haralick texture and invariant moments features for breast cancer classification
publisher AIP Publishing
publishDate 2015
url http://psasir.upm.edu.my/id/eprint/57618/1/Haralick%20texture%20and%20invariant%20moments%20features%20for%20breast%20cancer%20classification.pdf
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score 12.833586