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Lesion Classification in Mammography Images

Bagheri Khaligh, Ali | 2012

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 43550 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Manzuri Shalmani, Mohammad Taghi
  7. Abstract:
  8. Computer-Aided Diagnosis (CAD) systems are widely used for detection of various kinds of abnormalities in mammography images. In this work, mass classification is investigated and its steps are explained in detail, for each step a main method is presented and other methods are also discussed. For mass segmentation a relatively new method based on level set and Morphological Component Analysis (MCA) is used.After this step, various kinds of features such as shape, geometrical, and textural ones are introduced. Moreover, a set of proposed features based on wavelet transformation,for this application are presented. The proposed features can describe margin and texture characterizations of a mass perfectly; furthermore, they are robust against possible inaccuracies of segmentation. For classification task, Support Vector Machines (SVMs) classifier with polynomial kernel is used and other classifiers such as Neural Networks (NN), Bayes classifier, and Symbolic Feature Vector (SFV) are also used as alternatives. Before classification, Principal Component Analysis (PCA) is used to reduce the number of features. For simulating segmentation, feature extraction, and classification steps, DDSM dataset is used. The results show that exploiting proposed features in addition to a set of selected features, along with SVM classifier have an outstanding improvement in accuracy of classification, compared to the state-of-the-art methods. They indicate about 8% accuracy improvement in classification of masses into benign and malignant classes. It is also shown that the use of proposed features have more than 10% accuracy improvement as compared to the case that they are not used.
  9. Keywords:
  10. Breast Cancer ; Classification ; Mammography ; Segmentation ; Feature Extraction

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