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Detection Of Clustered Microcalcifications Using Wavelets, Donald A. Mccandless
Detection Of Clustered Microcalcifications Using Wavelets, Donald A. Mccandless
Theses and Dissertations
An automated method for detecting microcalcification clusters is presented. The algorithm begins with a digitized mammogram and outputs the center coordinates of regions of interest (ROIs) that contain microcalcification clusters. The method presented uses a 12-tap Least Asymmetric Daubechies (LAD12) wavelet in a tree structured filter bank to increase the signal to noise level of microcalcifications. The signal to noise level gain achieved by the filtering allows subsequent thresholding to eliminate on average 90% of the image from further consideration without eliminating actual microcalcifications 95% of the time. A novel approach to isolating individual calcifications from background tissue through non-stationary …
Computer Aided Detection Of Microcalcifications Utilizing Texture Analysis, Ronald C. Dauk
Computer Aided Detection Of Microcalcifications Utilizing Texture Analysis, Ronald C. Dauk
Theses and Dissertations
A comparative study of texture measures for the classification of breast tissue is presented. The texture features investigated include Angular Second Moments, Power Spectrum Analysis and a novel feature, Laws Energy Ratios. The texture study was accomplished as part of the development of a Model Based Vision (MBV) system for the automatic detection of microcalcifications. An overview of the Microcalcification Detection System is presented, which applies image differencing techniques, feature selection methods, and neural networks for locating microcalcification clusters in mammograms. The Power Spectrum Analysis feature set had the best overall performance with an 83% Probability of Detection and an …
Computer-Aided Diagnosis Of Mammographic Masses, William E. Polakowski
Computer-Aided Diagnosis Of Mammographic Masses, William E. Polakowski
Theses and Dissertations
A new Model-Based Vision algorithm was developed to find possibly cancerous regions of interest (ROIs) in digitized mammograms and to correctly identify the malignant masses. This work has shown a sensitivity of 92 percent for locating malignant ROIs. The database contained 272 images (12 bit, 1OO microns) with 36 malignant and 53 benign mass images. Of the 53 biopsied benign cases, 74 percent were correctly classified. The Focus of Attention (segmentation) Module algorithm used a physiologically motivated Difference of Gaussians (DoG) filter to highlight mass-like regions in the mammogram. The Index Module labeled the regions by their hypothesized class: large …