Reduction of false positives by extracting fuzzy rules from data for polyp detection in CTC scans
Musib M. Siddique, Yalin Zheng, Xiaoyun Yang, Gareth Beddoe · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2008
This paper presents an adaptive neural network based Fuzzy Inference System (ANFIS) to reduce the false positive (FP) rate of detected colonic polyps in Computed Tomography Colonography (CTC) scans. Extracted fuzzy rules establish linguistically interpretable relationships in the data that are easy to understand, validate, and extend. The system takes several features identified from regions extracted by a segmentation algorithm and decides whether the regions are true polyps. In the training phase, subtractive clustering is used to down-sample the negative regions in order to get balanced data. The rule extraction method is based on estimating clusters in the data using the subtractive clustering algorithm; each cluster obtained corresponds to a fuzzy rule that maps a region in the input space to an output class. After the number of rules and initial rule parameters are obtained by cluster estimation, the rule parameters are optimized using a hybrid learning algorithm which is a combination of least-squares estimation with back propagation. The evolved Sugeno-type FIS has been tested on a total of 129 scans with 99 polyps of sizes 5-15 mm by experienced radiologists. The results indicate that for 93% detection sensitivity (on polyps), the evolved FIS method is able to remove 88% of FPs generated by the segmentation algorithm leaving 7.5 FP per scan. The high sensitivity rate of our results show the promise of neuro-fuzzy classifiers as an aid for interpreting CTC examinations.