2003
DOI: 10.1117/12.481400
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Hierarchical segmentation of vertebrae from x-ray images

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Cited by 53 publications
(33 citation statements)
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“…Nevertheless, the results are competitive with results described in the literature. Zamora et al reported success rates of 50% with average errors below 6.4 mm in active shape model (ASM) segmentation of lumbar vertebrae in spine radiographs [20]. Smyth et al performed ASM segmentation of vertebrae in dual energy X ray absorptiometry (DXA) images [16] and obtained success rates of 94 -98%, with errors in the order of 1 mm for healthy vertebra and success rates of 85 -98% with errors in the order of 2 mm for fractured vertebrae.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Nevertheless, the results are competitive with results described in the literature. Zamora et al reported success rates of 50% with average errors below 6.4 mm in active shape model (ASM) segmentation of lumbar vertebrae in spine radiographs [20]. Smyth et al performed ASM segmentation of vertebrae in dual energy X ray absorptiometry (DXA) images [16] and obtained success rates of 94 -98%, with errors in the order of 1 mm for healthy vertebra and success rates of 85 -98% with errors in the order of 2 mm for fractured vertebrae.…”
Section: Discussionmentioning
confidence: 99%
“…Linear models of object appearance were shown to fail in many medical image segmentation tasks [18,9,4,14]. Another drawback of current deformable model approaches it that they require initialization near the final solution, and thus need manual intervention [9,16] or automatic object recognition [5,20].…”
Section: Introductionmentioning
confidence: 99%
“…A k-nearest neighbor (K-NN) method was adopted for matching the statistical model for an object to test images. Zamora et al [6] proposed a fully automated method for segmenting vertebrae using ASM. A customized Generalized Hough Transform was used to estimate the pose of the vertebrae by matching a template that represents the vertebrae of interest to a target image.…”
Section: Introductionmentioning
confidence: 99%
“…The state-of-the-art solution to the problem of vertebrae segmentation in digitized spine X-ray images is a hierarchical approach that combines three different methodologies. 17, 18 The first module is a customized generalized Hough transform (GHT) algorithm that is used to find an estimate of vertebral pose within target images. The second module is a customized version of active shape models (ASM) that is used to combine gray-level values and edge information to find vertebral boundaries.…”
Section: Introductionmentioning
confidence: 99%
“…The third module is a customized deformable model (DM) approach based on the minimization of external and internal energies which allows the capture of fine details such as vertebral corners. 18 The success of the GHT-based technique depends on three parameters: (1) gradient information, (2) representativeness of the template, and (3) reckon-ing of the votes in the accumulator structure. A clear edge image is required for obtaining gradient information accurately, and a template must adequately represent the target object to obtain the necessary votes in the accumulator.…”
Section: Introductionmentioning
confidence: 99%