Abstract
Nodule detection is a vital step in computer-aided diagnosis (CAD) system, for its accuracy can greatly influence the reliability of the system. Many researchers have proposed lots of effective filters to detect nodules. However, these filters have limitations, such as changing the nodules shapes and sizes. This paper proposes a modified Difference of Gaussian (DoG) detector for pulmonary nodule location. First, Keypoints that represents nodule-like regions are assigned by using the DoG detector. Second, since the DoG detector gives response to the image corners and edges, the Hessian Matrix is employed to eliminate those Keypoints located at the image corners or close to the edges. Experimental results showed the performance of the DoG detector and the modified DoG Detector.
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