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This paper presents a three-dimensional (3D) reconstruction system of the human spine for the routine evaluation of musculoskeletal pathologies like idiopathic scoliosis. The main objective of this 3D reconstruction system is to offer a versatile and robust tool for the 3D analysis of spines in any healthcare centre with standard clinical setup using standard uncalibrated radiographic images. The novel system uses a self-calibration algorithm and a weak-perspective method to reconstruct the 3D coordinates of anatomical landmarks from bi-planar radiographic images of a patient's trunk. Additionally, a small planar object of known dimensions is proposed to warrant an accurately scaled model of the spine. In order to assess the validity of the 3D reconstructions yielded by the proposed system, a clinical study using 60 pairs of digitized X-rays of adolescents was conducted. The subject cohort in the study group was composed of 51 scoliotic and 9 non-scoliotic patients, with an average Cobb angle on the frontal plane of 25 degrees. For each case, a 3D reconstruction of the spine and pelvis was obtained with the previous system used at our hospital (which requires a positioning apparatus and a calibration jacket), and with the proposed method. Results show that 3D reconstructions obtained with the new system using uncalibrated X-ray images yield geometrically accurate models with insignificant differences for 2D and 3D clinical indexes commonly used in the evaluation of spinal deformities. This demonstrates the system to be a viable and accurate tool for clinical studies and biomechanical analysis purposes, with the added advantage of versatility to any clinical setup for routine follow-ups and surgical planning.

The main objective of this study was to develop a 3-D X-ray reconstruction system of the spine and rib cage for an accurate 3-D clinical assessment of spinal deformities. The system currently used at Sainte-Justine Hospital in Montreal is based on an implicit calibration technique based on a direct linear transform (DLT), using a sufficiently large rigid object incorporated in the positioning apparatus to locate any anatomical structure to be reconstructed within its bounds. During the time lapse between the two successive X-ray acquisitions required for the 3-D reconstruction, involuntary patient motion introduce errors due to the incorrect epipolar geometry inferred from the stationary object. An approach using a new calibration jacket and explicit calibration algorithm is proposed in this paper. This approach yields accurate results and compensates for involuntary motion occurring between X-ray exposures.

This paper presents a novel viewpoint selection criterion for active object recognition and pose estimation whose key advantage resides in its low computational cost with respect to current popular approaches in the literature. The proposed observation selection criterion associates high utility with observations that predictably facilitate distinction between pairs of competing hypotheses by a Bayesian classifier. Rigorous experimentation of the proposed approach was conducted on two case studies, involving synthetic and real data, respectively. The results show the proposed algorithm to perform better than a random navigation strategy in terms of the amount of data required for recognition while being much faster than a strategy based on mutual information, without compromising accuracy.