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A Wavelet-packet-based approach for breast cancer classification

Torabi, M ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1109/IEMBS.2011.6091263
  3. Abstract:
  4. In this paper, a new approach for non-invasive diagnosis of breast diseases is tested on the region of the breast without undue influence from the background and medically unnecessary parts of the images. We applied Wavelet packet analysis on the two-dimensional histogram matrices of a large number of breast images to generate the filter banks, namely sub-images. Each of 1250 resulting sub-images are used for computation of 32 two-dimensional histogram matrices. Then informative statistical features (e.g. skewness and kurtosis) are extracted from each matrix. The independent features, using 5-fold cross-validation protocol, are considered as the input sets of supervised classification. We observed that the proposed method improves the detection accuracy of Architectural Distortion disease compared to previous works and also is very effective for diagnosis of Spiculated Mass and MISC diseases
  5. Keywords:
  6. Breast diseases ; Statistical feature extraction ; Breast disease ; Non-invasive diagnosis ; Statistical features ; Supervised classification ; Wavelet Packet Analysis ; Feature extraction ; Filter banks ; Graphic methods ; Matrix algebra ; Medical imaging ; Statistical methods ; Two dimensional ; Diagnosis
  7. Source: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS ; 2011 , Pages 5100-5103 ; 1557170X (ISSN) ; 9781424441211 (ISBN)
  8. URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6091263