Mammographic mass classification using filter response patches

Zobia Suhail, Azam Hamidinekoo, Reyer Zwiggelaar

Research output: Contribution to journalArticlepeer-review

5 Citations (SciVal)


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
Original languageEnglish
Pages (from-to)1060-1066
Number of pages7
JournalIET Computer Vision
Issue number8
Early online date13 Aug 2018
Publication statusPublished - 01 Dec 2018


  • Bayes methods
  • cancer
  • channel bank filters
  • image classification
  • image texture
  • mammography
  • medical image processing
  • object detection


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