A new model-based vision (MBV) algorithm is developed to find regions of interest (ROI's) corresponding to masses in digitized mammograms and to classify the masses as malignant/benign. The MBV algorithm is comprised of five modules to structurally identify suspicious ROI's, eliminate false positives, and classify the remaining as malignant or benign. The focus of attention module uses a difference of Gaussians (DoG) filter to highlight suspicious regions in the mammogram. The index module uses tests to reduce the number of nonmalignant regions from 8.39 to 2.36 per full breast image. Size, shape, contrast, and Laws texture features are used to develop the prediction module's mass models. Derivative-based feature saliency techniques are used to determine the best features for classification. Nine features are chosen to define the malignant/benign models. The feature extraction module obtains these features from all suspicious ROI's. The matching module classifies the regions using a multilayer perceptron neural network architecture to obtain an overall classification accuracy of 100% for the segmented malignant masses with a false-positive rate of 1.8 per full breast image. This system has a sensitivity of 92% for locating malignant ROI's. The database contains 272 images (12 b, 100 microm) with 36 malignant and 53 benign mass images. The results demonstrate that the MBV approach provides a structured order of integrating complex stages into a system for radiologists.

This work presents a method for phoneme recognition based on a model of the human binaural auditory system. Performance of the binaural system is compared to that of an equivalent monaural system across a range of signal-to-noise ratios (SNRs) and simulated azimuth separations of speech and noise sources. Comparison of binaural and monaural phoneme recognition accuracy for both speech and noise at 0/spl deg/ suggests an effective increase in SNR of 5 dB for the binaural system. Experiments are also conducted to investigate binaural system performance for speech and noise at differing azimuth angles. Two binaural speech and noise conditions are considered: i) speech from 0/spl deg/ plus noise from 0/spl deg/; and ii) speech from 0/spl deg/ plus noise from 90/spl deg/. A comparison of relative performance differences for each binaural system with the monaural system shows the greatest increase in phoneme recognition accuracy when the speech and noise are simulated using different azimuth angles (0/spl deg/ speech plus noise from 90/spl deg/).