Effective and Efficient Dimensionality Reduction for Large-Scale and Streaming Data Preprocessing
IEEE Transactions on Knowledge and Data Engineering
Cortical Surface Shape Analysis Based on Spherical Wavelet Transformation
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Computers in Biology and Medicine
Computer Methods and Programs in Biomedicine
Predicting breast cancer survivability: a comparison of three data mining methods
Artificial Intelligence in Medicine
Computers in Biology and Medicine
A comparison of wavelet and curvelet for breast cancer diagnosis in digital mammogram
Computers in Biology and Medicine
A Wavelet-Based Mammographic Image Denoising and Enhancement with Homomorphic Filtering
Journal of Medical Systems
Classification of benign and malignant masses based on Zernike moments
Computers in Biology and Medicine
Breast Cancer Diagnosis: Analyzing Texture of Tissue Surrounding Microcalcifications
IEEE Transactions on Information Technology in Biomedicine
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The purpose of this study is to implement accurate methods of detection and classification of benign and malignant breast masses in mammograms. Our new proposed method, which can be used as a diagnostic tool, is denoted Local Seed Region Growing-Spherical Wavelet Transform (LSRG-SWT), and consists of four steps. The first step is homomorphic filtering for enhancement, and the second is detection of the region of interests (ROIs) using a Local Seed Region Growing (LSRG) algorithm, which we developed. The third step incoporates Spherical Wavelet Transform (SWT) and feature extraction. Finally the fourth step is classification, which consists of two sequential components: the 1st classification distinguishes the ROIs as either mass or non-mass and the 2nd classification distinguishes the masses as either benign or malignant using a Support Vector Machine (SVM). The mammograms used in this study were acquired from the hospital of Istanbul University (I.U.) in Turkey and the Mammographic Image Analysis Society (MIAS). The results demonstrate that the proposed scheme LSRG-SWT achieves 96% and 93.59% accuracy in mass/non-mass classification (1st component) and benign/malignant classification (2nd component) respectively when using the I.U. database with k-fold cross validation. The system achieves 94% and 91.67% accuracy in mass/non-mass classification and benign/malignant classification respectively when using the I.U. database as a training set and the MIAS database as a test set with external validation.