Computer-aided detection of pulmonary nodules using genetic programming


This paper describes a novel nodule detection method that enhances false positive reduction. Lung region is extracted from CT image sequence using adaptive thresholding and 18-connectedness voxel labelling. In the extracted lung region, nodule candidates are detected using adaptive multiple thresholding and rule based classifier. After that, we extract the 3D and 2D features from nodule candidates. The nodule candidates are then classified using genetic programming (GP) based classifier. In this work, a new fitness function is proposed to generate optimal adaptive classifier. We tested the proposed algorithm by using Lung Imaging Database Consortium (LIDC) database of National Cancer Institute (NCI). The classifier was trained and evaluated using two independent dataset and whole dataset. The proposed method reduced the false positives in nodule candidates and achieved 92% detection rate with 6.5 false positives per scan.

DOI: 10.1109/ICIP.2010.5652369

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@article{Choi2010ComputeraidedDO, title={Computer-aided detection of pulmonary nodules using genetic programming}, author={Wook-Jin Choi and Tae-Sun Choi}, journal={2010 IEEE International Conference on Image Processing}, year={2010}, pages={4353-4356} }