A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analysis of mammogram classification using a wavelet transform decomposition
Pattern Recognition Letters - Special issue: Sibgrapi 2001
Multiresolution mammogram analysis in multilevel decomposition
Pattern Recognition Letters
Fuzzy rough sets hybrid scheme for breast cancer detection
Image and Vision Computing
Computer-aided detection and diagnosis of breast cancer with mammography: recent advances
IEEE Transactions on Information Technology in Biomedicine
Computers in Biology and Medicine
Breast cancer diagnosis system based on wavelet analysis and fuzzy-neural
Expert Systems with Applications: An International Journal
Digital Mammograms Classification Using a Wavelet Based Feature Extraction Method
ICCEE '09 Proceedings of the 2009 Second International Conference on Computer and Electrical Engineering - Volume 02
Contourlet-based mammography mass classification using the SVM family
Computers in Biology and Medicine
A comparison of wavelet and curvelet for breast cancer diagnosis in digital mammogram
Computers in Biology and Medicine
Multiresolution detection of spiculated lesions in digital mammograms
IEEE Transactions on Image Processing
Contourlet-based mammography mass classification
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Hierarchical Correlation of Multi-Scale Spatial Pyramid for Similar Mammogram Retrieval
International Journal of Digital Library Systems
Computers in Biology and Medicine
International Journal of Computational Vision and Robotics
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This paper presents a method for breast cancer diagnosis in digital mammogram images. Multiresolution representations, wavelet or curvelet, are used to transform the mammogram images into a long vector of coefficients. A matrix is constructed by putting wavelet or curvelet coefficients of each image in row vector, where the number of rows is the number of images, and the number of columns is the number of coefficients. A feature extraction method is developed based on the statistical t-test method. The method is ranking the features (columns) according to its capability to differentiate the classes. Then, a dynamic threshold is applied to optimize the number of features, which can achieve the maximum classification accuracy rate. The method depends on extracting the features that can maximize the ability to discriminate between different classes. Thus, the dimensionality of data features is reduced and the classification accuracy rate is improved. Support vector machine (SVM) is used to classify between the normal and abnormal tissues and to distinguish between benign and malignant tumors. The proposed method is validated using 5-fold cross validation. The obtained classification accuracy rates demonstrate that the proposed method could contribute to the successful detection of breast cancer.