Wavelet-Based Texture Classification of Tissues in Computed Tomography
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
Automated detection of masses in mammograms by local adaptive thresholding
Computers in Biology and Medicine
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IEEE Transactions on Image Processing
Contourlet-based mammography mass classification
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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Computers in Biology and Medicine
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
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Expert Systems with Applications: An International Journal
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This paper is concerned with the design and development of an automatic mass classification of mammograms. The proposed method consists of three stages. In the first stage, preprocessing is performed to remove the pectoral muscles and to segment regions of interest. In the next stage contourlet transform is employed as a feature extractor to obtain the contourlet coefficients. This stage is completed by feature selection based on the genetic algorithm, resulting in a more compact and discriminative texture feature set. This improves the accuracy and robustness of the subsequent classifiers. In the final stage, classification is performed based on successive enhancement learning (SEL) weighted SVM, support vector-based fuzzy neural network (SVFNN), and kernel SVM. The proposed approach is applied to the Mammograms Image Analysis Society dataset (MIAS) and classification accuracies of 96.6%, 91.5% and 82.1% are determined over an efficient computational time by SEL weighted SVM, SVFNN and kernel SVM, respectively. Experimental results illustrate that the contourlet-based feature extraction in conjunction with the state-of-art classifiers construct a powerful, efficient and practical approach for automatic mass classification of mammograms.