Mammographic mass classification using filter response patches
- Author(s): Zobia Suhail 1 ; Azam Hamidinekoo 2 ; Reyer Zwiggelaar 2
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View affiliations
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Affiliations:
1:
Punjab University College of Information Technology, University of the Punjab , Lahore , Pakistan ;
2: Department of Computer Science , Aberystwyth University , Wales SY23 3DB , UK
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Affiliations:
1:
Punjab University College of Information Technology, University of the Punjab , Lahore , Pakistan ;
- Source:
Volume 12, Issue 8,
December
2018,
p.
1060 – 1066
DOI: 10.1049/iet-cvi.2018.5244 , Print ISSN 1751-9632, Online ISSN 1751-9640
Considering the importance of early diagnosis of breast cancer, a supervised patch-wise texton-based approach has been developed for the classification of mass abnormalities in mammograms. The proposed method is based on texture-based classification of masses in mammograms and does not require segmentation of the mass region. In this approach, patches from filter bank responses are utilised for generating the texton dictionary. The methodology is evaluated on the publicly available Digital Database for Screening Mammography database. Using a naive Bayes classifier, a classification accuracy of 83% with an area under the receiver operating characteristic curve of 0.89 was obtained. Experimental results demonstrated that the patch-wise texton-based approach in conjunction with the naive Bayes classifier constructs an efficient and alternative approach for automatic mammographic mass classification.
Inspec keywords: image texture; channel bank filters; image classification; medical image processing; object detection; mammography; cancer; Bayes methods
Other keywords: supervised patch-wise texton-based approach; filter response patches; mammograms; filter bank responses; automatic mammographic mass classification; mass abnormalities classification; receiver operating characteristic curve; Digital Database-for-Screening Mammography database; texture-based mass classification; early breast cancer diagnosis; texton dictionary generation; naive Bayes classifier
Subjects: Filtering methods in signal processing; Other topics in statistics; Image recognition; Biology and medical computing; X-rays and particle beams (medical uses); Patient diagnostic methods and instrumentation; Patient care and treatment; Other topics in statistics; Probability theory, stochastic processes, and statistics; Computer vision and image processing techniques; X-ray techniques: radiography and computed tomography (biomedical imaging/measurement)
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