This section evaluates the performance of a given configuration of the adaptive knowledge-based model in predicting the optimal pipeline of image processing operators used for the CAD of breast cancer. This performance is compared to that obtained by keeping the pipeline fixed. Contrast enhancement and image segmentation are the key components in a mammographic CAD system. For these key components, sections 11.3 and 11.4, respectively, have demonstrated that a knowledge-based framework is superior to the single best method in each case. Parameterized versions of these components have been engineered for individual mammogram groupings. These groupings are based on the mammographic breast density and a mechanism for its prediction. Evaluation of the performance of each parameterized version of the knowledge-based component presented in the previous sections has been performed using the target mammogram breast grouping. In this section, the complete adaptive knowledge-based model is evaluated using the predicted breast group.
Section 11.6.1 evaluates the knowledge-based contrast enhancement and segmentation components using the predicted breast type grouping using 200 abnormal mammograms from the DDSM. Following this, section 11.6.2 evaluates the complete adaptive knowledge-based model using a dataset of 400 mam-mograms. This dataset comprises 200 normal and 200 abnormal mammograms comprising 50 images of each type from each of the four breast types. Results for segmentation and following false-positive reduction are presented. Finally section 11.6.3 presents key observations.
Was this article helpful?