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Pulmonary Nodule Detection from X-ray CT Images Based on Region Shape Analysis and Appearance-based Clustering

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Graduate School of Systems and Information Engineering, University of Tsukuba, 305-8573, Japan
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Faculty of Engineering, Information and Systems, University of Tsukuba, 305-8573, Japan
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Author to whom correspondence should be addressed.
Academic Editor: Javier Del Ser Lorente
Algorithms 2015, 8(2), 209-223; https://doi.org/10.3390/a8020209
Received: 24 December 2014 / Revised: 6 March 2015 / Accepted: 27 April 2015 / Published: 8 May 2015
In this paper, we propose a detection method of pulmonary nodules in X-ray computed tomography (CT) scans by use of three image filters and appearance-based k-means clustering. First, voxel values are suppressed in radial directions so as to eliminate extra regions in the volumes of interest (VOIs). Globular regions are enhanced by moment-of-inertia tensors where the voxel values in the VOIs are regarded as mass. Excessively enhanced voxels are reduced based on displacement between the VOI centers and the gravity points of the voxel values in the VOIs. Initial nodule candidates are determined by these filtering processings. False positives are reduced by, first, normalizing the directions of intensity distributions in the VOIs by rotating the VOIs based on the eigenvectors of the moment-of-inertia tensors, and then applying an appearance-based two-step k-means clustering technique to the rotated VOIs. The proposed method is applied to actual CT scans and experimental results are shown. View Full-Text
Keywords: chest X-ray computed tomography (CT) scans; detection of pulmonary nodules; moment-of-inertia; appearance-based k-means clustering chest X-ray computed tomography (CT) scans; detection of pulmonary nodules; moment-of-inertia; appearance-based k-means clustering
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Yanagihara, T.; Takizawa, H. Pulmonary Nodule Detection from X-ray CT Images Based on Region Shape Analysis and Appearance-based Clustering. Algorithms 2015, 8, 209-223.

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