Accession Number : ADA306044

Title :   Computer-Aided Diagnosis of Mammographic Masses.

Descriptive Note : Master's thesis,

Corporate Author : AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH

Personal Author(s) : Polakowski, William E.

PDF Url : ADA306044

Report Date : DEC 1995

Pagination or Media Count : 114

Abstract : 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 or medium mass. Then it used size, shape, and contrast tests to reduce the number of non-malignant regions from 8.4 to 2.8 per image. Size, shape, contrast, and Laws texture features were used to develop the Prediction Module's mass model. Statistical and derivative-based feature saliency techniques were used to determine the best features. Nine features were chosen to define the model. Using this model, the Matching Module classified the regions using a multilayer perceptron neural network architecture trained with an imbalanced training set weight update algorithm to obtain an overall classification accuracy of 100 percent for the segmented malignant masses with a false-positive rate of 1.8/image.

Descriptors :   *NEURAL NETS, *DIAGNOSIS(MEDICINE), *COMPUTER AIDED DIAGNOSIS, *MEDICAL COMPUTER APPLICATIONS, *BREAST CANCER, TEST AND EVALUATION, ALGORITHMS, CONTRAST, MASS, COMPUTER ARCHITECTURE, MODULAR CONSTRUCTION, REGIONS, WEIGHT, BALANCE, COMPUTER APPLICATIONS, INDEXES, MEDICAL SERVICES, MATCHING, TEXTURE, SEGMENTED, DOGS, CANCER.

Subject Categories : Medicine and Medical Research
      Cybernetics

Distribution Statement : APPROVED FOR PUBLIC RELEASE