- Title
- Analysis and comparison of co-occurrence matrix and pixel n-gram features for mammographic images
- Creator
- Kulkarni, Pradnya; Stranieri, Andrew; Kulkarni, Sid; Ugon, Julien; Mittal, Manish
- Date
- 2015
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/162479
- Identifier
- vital:12639
- Abstract
- Mammography is a proven way of detecting breast cancer at an early stage. Various feature extraction techniques such as histograms, co-occurrence matrix, local binary patterns, Gabor filters, wavelet transforms are used for analysing mammograms. The novel pixel N-gram feature extraction technique has been inspired from the character N-gram concept of text retrieval. In this paper, we have compared the novel N-gram feature extraction technique with the co-occurrence matrix feature extraction technique. The experiments were conducted on the benchmark miniMIAS mammography database. Classification of mammograms into normal and abnormal category using N-gram features showed promising results with greater classification accuracy, sensitivity and specificity compared to classification using co-occurrence matrix features. Moreover, N-gram features computation are found to be considerably faster than co-occurrence matrix feature computation
- Relation
- International Conference on Communication and Computing p. 7-14
- Rights
- This metadata is freely available under a CCO license
- Subject
- Mammogram classification; N-grams; Co-occurrence matrix; Multilayer perceptron; Computational time
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