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Automated Pulmonary Nodule Detection System in Computed Tomography Images: A Hierarchical Block Classification Approach
Gwangju Institute of Science and Technology (GIST), School of Information and Mechatronics, 123 Cheomdan Gwagiro, Buk-Gu, Gwangju, 500-712, Korea
* Author to whom correspondence should be addressed.
Received: 8 November 2012; in revised form: 22 January 2013 / Accepted: 28 January 2013 / Published: 31 January 2013
Abstract: A computer-aided detection (CAD) system is helpful for radiologists to detect pulmonary nodules at an early stage. In this paper, we propose a novel pulmonary nodule detection method based on hierarchical block classification. The proposed CAD system consists of three steps. In the first step, input computed tomography images are split into three-dimensional block images, and we apply entropy analysis on the block images to select informative blocks. In the second step, the selected block images are segmented and adjusted for detecting nodule candidates. In the last step, we classify the nodule candidate images into nodules and non-nodules. We extract feature vectors of the objects in the selected blocks. Lastly, the support vector machine is applied to classify the extracted feature vectors. Performance of the proposed system is evaluated on the Lung Image Database Consortium database. The proposed method has reduced the false positives in the nodule candidates significantly. It achieved 95.28% sensitivity with only 2.27 false positives per scan.
Keywords: lung CT; nodule detection; CAD; block classification
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Cite This Article
MDPI and ACS Style
Choi, W.-J.; Choi, T.-S. Automated Pulmonary Nodule Detection System in Computed Tomography Images: A Hierarchical Block Classification Approach. Entropy 2013, 15, 507-523.
Choi W-J, Choi T-S. Automated Pulmonary Nodule Detection System in Computed Tomography Images: A Hierarchical Block Classification Approach. Entropy. 2013; 15(2):507-523.
Choi, Wook-Jin; Choi, Tae-Sun. 2013. "Automated Pulmonary Nodule Detection System in Computed Tomography Images: A Hierarchical Block Classification Approach." Entropy 15, no. 2: 507-523.