We present an automatic method for assessment of pectus excavatum severity based on an optical 3-D markerless shape measurement. A four-directional measurement system based on a structured light projection method is built to capture the shape of the body surface of the patients. The system setup is described and typical measurement parameters are given. The automated data analysis path is explained. Their main steps are: normalization of trunk model orientation, cutting the model into slices, analysis of each slice shape, selecting the proper slice for the assessment of pectus excavatum of the patient, and calculating its shape parameter. We develop a new shape parameter (I(3ds)) that shows high correlation with the computed tomography (CT) Haller index widely used for assessment of pectus excavatum. Clinical results and the evaluation of developed indexes are presented.

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The use of technology in sports has increased in recent years. One of the most influential of these technologies is referee support systems. Team sports such as volleyball require accurate and robust tracking systems that do not affect either the players or the court. This paper introduces the application of intrinsic and extrinsic camera calibration in a 12-camera volleyball referee system. Intrinsic parameters are calculated by using the classic pinhole model and Zhang's method. To perform extrinsic calibration in real time, the volleyball court is treated as a global calibration artifact. Calibration keypoints are defined as courtline intersections. In addition, a new keypoint detection algorithm is proposed. It enables achievement of an accurate camera pose in regard to the court. With all 12 cameras calibrated in a common coordinate system, a dynamic camera stereo pair creation is possible. Therefore, with known ball 2D image coordinates, the 3D real ball coordinates can be reconstructed and the ball trajectory can be estimated. The performance of the proposed method is tested on a synthetic data set, including 3Ds Max rendering and real data scenarios. The mean camera pose error calculated for data biased with keypoint detection errors is approximately equal to 0.013% of the measurement volume. For the real data experiment with a human hand phantom, it is possible to determine the presence of the human phantom on the basis of the ball reflection attitude.