ABSTRACT
The main objective of this study was to develop a 3D X-ray reconstruction system of the spine and rib cage for an accurate clinical assessment of spinal deformities. The proposed system uses an explicit calibration technique and a new calibration object composed of: (1) a set of radiopaque markers embedded in a jacket worn by the patient during the X-ray exposures; (2) six control markers to define a reference vertical plane. Computer simulations were performed to evaluate the accuracy of the 3D reconstruction procedure when different kind of displacements were applied on a reference model. Clinical indices computed from the 3D X-ray reconstruction of the spine for 24 scoliotic subjects were compared to those obtained with the DLT method. The results of the evaluation study showed that the new system allows the patient to adopt a normal attitude without any constraint, compensating for its displacement between exposures.
Subject(s)
Image Processing, Computer-Assisted/instrumentation , Imaging, Three-Dimensional/instrumentation , Radiographic Image Interpretation, Computer-Assisted/instrumentation , Scoliosis/diagnostic imaging , Algorithms , Calibration , Computer Simulation , Humans , Lumbar Vertebrae/diagnostic imaging , Nonlinear Dynamics , Reference Values , Reproducibility of Results , Scoliosis/classification , Thoracic Vertebrae/diagnostic imagingABSTRACT
Multiscale representation is a methodology that is being used more and more when describing real-world structures. Scale-space representation is one formulation of multiscale representation that has received considerable interest in the literature because of its efficiency in several practical applications and the distinct properties of the Gaussian kernel that generate the scale space. Together, some of these properties make the Gaussian unique. Unfortunately, the Gaussian kernel has two practical limitations: information loss caused by the unavoidable Gaussian truncation and the prohibitive processing time due to the mask size. We propose a new kernel family derived from the Gaussian with compact supports that are able to recover the information loss while drastically reducing processing time. This family preserves a great part of the useful Gaussian properties without contradicting the uniqueness of the Gaussian kernel. The construction and analysis of the properties of the proposed kernels are presented in this paper. To assess the developed theory, an application of extracting handwritten data from noisy document images is presented, including a qualitative comparison between the results obtained by the Gaussian and the proposed kernels.
