Nodule detection on chest helical CT scans by using a genetic algorithm

Abstract

The purpose of the study is to apply a genetic algorithm (GA) template matching method to detect lung nodules in chest helical X ray CT (computed tomography) images. We combined GA and template matching to search the positions of nodules and to calculate adaptation scales of individuals on GA, respectively. We used four simulated nodules created by Gaussian distribution, whose sizes were different to each other, as reference patterns in the GA template matching. The GA selected an adequate reference image from four images and searched adequate positions to template matching. We used cross correlation as similarity of template matching and as adaptation scales of individuals on GA. It was possible to detect 23 nodules from 45 that did not touch the lung walls, without consideration of their sizes. It was also possible to detect all nodules that touched the lung walls by using conventional template matching along lung walls. The total detection rate was approximately 67%. The number of false positives per slice was over 10. To improve the detection performance and to decrease the number of false positives, we are now working on considering operators and their parameters of GA.