Mazen Soufi1, Hidetaka Arimura2, Katsumasa Nakamura3, Fauzia P Lestari4, Freddy Haryanto4, Taka-Aki Hirose5, Yoshiyuki Umedu5, Yoshiyuki Shioyama6, Fukai Toyofuku7. 1. Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan. 2. Faculty of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan. arimurah@med.kyushu-u.ac.jp. 3. Hamamatsu University School of Medicine, 1-20-1, Handayama, Higashi-ku, Shizuoka, 431-3192, Japan. 4. Bandung Institute of Technology, Bandung, 40116, Indonesia. 5. Kyushu University Hospital, 3-1-1, Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan. 6. Saga Heavy Ion Medical Accelerator, 415, Harakoga-machi, Tosu, 841-0071, Japan. 7. Faculty of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan.
Abstract
PURPOSE: To investigate the feasibility of differential geometry features in the detection of anatomical feature points on a patient surface in infrared-ray-based range images in image-guided radiation therapy. METHODS: The key technology was to reconstruct the patient surface in the range image, i.e., point distribution with three-dimensional coordinates, and characterize the geometrical shape at every point based on curvature features. The region of interest on the range image was extracted by using a template matching technique, and the range image was processed for reducing temporal and spatial noise. Next, a mathematical smooth surface of the patient was reconstructed from the range image by using a non-uniform rational B-splines model. The feature points were detected based on curvature features computed on the reconstructed surface. The framework was tested on range images acquired by a time-of-flight (TOF) camera and a Kinect sensor for two surface (texture) types of head phantoms A and B that had different anatomical geometries. The detection accuracy was evaluated by measuring the residual error, i.e., the mean of minimum Euclidean distances (MMED) between reference (ground truth) and detected feature points on convex and concave regions. RESULTS: The MMEDs obtained using convex feature points for range images of the translated and rotated phantom A were [Formula: see text] and [Formula: see text], respectively, using the TOF camera. For the phantom B, the MMEDs of the convex and concave feature points were [Formula: see text] and [Formula: see text] mm, respectively, using the Kinect sensor. There was a statistically significant difference in the decreased MMED for convex feature points compared with concave feature points [Formula: see text]. CONCLUSIONS: The proposed framework has demonstrated the feasibility of differential geometry features for the detection of anatomical feature points on a patient surface in range image-guided radiation therapy.
PURPOSE: To investigate the feasibility of differential geometry features in the detection of anatomical feature points on a patient surface in infrared-ray-based range images in image-guided radiation therapy. METHODS: The key technology was to reconstruct the patient surface in the range image, i.e., point distribution with three-dimensional coordinates, and characterize the geometrical shape at every point based on curvature features. The region of interest on the range image was extracted by using a template matching technique, and the range image was processed for reducing temporal and spatial noise. Next, a mathematical smooth surface of the patient was reconstructed from the range image by using a non-uniform rational B-splines model. The feature points were detected based on curvature features computed on the reconstructed surface. The framework was tested on range images acquired by a time-of-flight (TOF) camera and a Kinect sensor for two surface (texture) types of head phantoms A and B that had different anatomical geometries. The detection accuracy was evaluated by measuring the residual error, i.e., the mean of minimum Euclidean distances (MMED) between reference (ground truth) and detected feature points on convex and concave regions. RESULTS: The MMEDs obtained using convex feature points for range images of the translated and rotated phantom A were [Formula: see text] and [Formula: see text], respectively, using the TOF camera. For the phantom B, the MMEDs of the convex and concave feature points were [Formula: see text] and [Formula: see text] mm, respectively, using the Kinect sensor. There was a statistically significant difference in the decreased MMED for convex feature points compared with concave feature points [Formula: see text]. CONCLUSIONS: The proposed framework has demonstrated the feasibility of differential geometry features for the detection of anatomical feature points on a patient surface in range image-guided radiation therapy.
Authors: Sanford L Meeks; Wolfgang A Tomé; Tywla R Willoughby; Patrick A Kupelian; Thomas H Wagner; John M Buatti; Francis J Bova Journal: Semin Radiat Oncol Date: 2005-07 Impact factor: 5.934
Authors: Jean L Peng; Chihray Liu; Yu Chen; Robert J Amdur; Kenneth Vanek; Jonathan G Li Journal: J Appl Clin Med Phys Date: 2011-04-04 Impact factor: 2.102