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...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
AIP Publishing
2015
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| Online Access: | http://psasir.upm.edu.my/id/eprint/57618/ http://psasir.upm.edu.my/id/eprint/57618/1/Haralick%20texture%20and%20invariant%20moments%20features%20for%20breast%20cancer%20classification.pdf |
| _version_ | 1848853416764243968 |
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| author | Yasiran, Siti Salmah Salleh, Shaharuddin Mahmud, Rozi |
| author_facet | Yasiran, Siti Salmah Salleh, Shaharuddin Mahmud, Rozi |
| author_sort | Yasiran, Siti Salmah |
| building | UPM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T10:53:38Z |
| format | Conference or Workshop Item |
| id | upm-57618 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T10:53:38Z |
| publishDate | 2015 |
| publisher | AIP Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-576182017-10-24T07:52:07Z 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 |
| spellingShingle | Yasiran, Siti Salmah Salleh, Shaharuddin Mahmud, Rozi Haralick texture and invariant moments features for breast cancer classification |
| title | 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_short | Haralick texture and invariant moments features for breast cancer classification |
| title_sort | haralick texture and invariant moments features for breast cancer classification |
| url | http://psasir.upm.edu.my/id/eprint/57618/ http://psasir.upm.edu.my/id/eprint/57618/ http://psasir.upm.edu.my/id/eprint/57618/1/Haralick%20texture%20and%20invariant%20moments%20features%20for%20breast%20cancer%20classification.pdf |