Computer-aided tumor detection stemmed from the fuzzification of the Dempster-Shafer theory

Abstract

In decision making processes where we have to deal with epistemic uncertainties, the Dempster-Shafer theory (DST) of evidence and fuzzy logic have gained prominence as the methods of choice over traditional probabilistic methods. The DST is unfortunately known to give wrong results in situations of high conflict. While some methods have been proposed in the literature for improving the DST, such as the weighted DST which assumes that we have some information about the relative reliabilities of the classifiers, we opted to incorporate fuzzy concepts in the DST framework. This work was motivated by the desire to improve detection performance of a Computer-Aided Detection (CAD) system under development for the detection of tumors in Positron Emission Tomography (PET) images by fusing the outputs of multiple classifiers such as the SVM and LDA classifiers. A first implement based on a simple binary fusion scheme gave a result of 69% true detections with an average of 2.5 false positive detections per 3D image (FPI). These results prompted the use of the DST which resulted in 92% detection sensitivity and 25 FPI. As a way of further reducing the false detections, we chose to tackle the limitations inherent to the DST by principally applying fuzzy techniques in defining the hypotheses and experimenting with new combination rules. The best result of this modified DST approach has been a 92% true tumor detection with 12 FPI; indicating a reduction by a factor of 2 of the false detections while maintaining high sensitivity.