Computer assisted detection systems (CAD) in mammography incorporate two critical stages: (i) prescreening to localize suspicious regions and (ii) detailed analysis of the regions for false positive reduction. In this work, we present a new technique for automatic detection of suspicious masses for prescreening mammograms. The hypothesis of the proposed technique is that malignant masses manifestate as superimposed concentric layers. Morphological characterization of these layers can form the foundation of an automated scheme for detection of suspicious masses. The study was based on fifty nine screening mammograms from the digital database of screening mammography (DDSM). Overall, the proposed scheme performs at 85.7% sensitivity with an average of 0.53 false positives per image. The scheme targets malignant masses and, thus it can provide a second opinion to radiologists without sending benign masses to unnecessary biopsy.
This study analyzes the performance of a computer aided detection (CAD) scheme for mass detection in mammography. We investigate the trained parameters of the detection scheme before any further testing. We use an extended version of a previously reported mass detection scheme. We analyze the detection parameters by using linear canonical discriminants (LCD) and compare results with logistic regression and multi layer perceptron Neural Network models. Preliminary results suggest that regression and multi layer perceptron Neural Network showed the best Receiver Operator Characteristics (ROC). The LCD analysis predictive function showed that the trained CAD scheme performance can maintain 99.08% sensitivity (108/109) with false positive rate (FPI) of 8 per image with ROC Az= 0.74±0.01. The regression and the multi layer perceptron Neural Network ROC analysis showed stronger backbone for the CAD algorithm viewing that the extended CAD scheme can operate at 96% sensitivity with 5.6 FPI per image. These preliminary results suggest that further logic to reduce FPI is needed for the CAD algorithm to be more predictive.
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