Spine Localization in X-ray Images Using Interest Point Detection
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TLDR
The goal of this work is to develop an x-ray image segmentation approach used to identify the location and the orientation of the cervical vertebrae in medical images by extracting the anterior—left—faces of vertebra contours using the Harris corner detector.Abstract:
This study was conducted to evaluate a new method used to calculate vertebra orientation in medical x-ray images. The goal of this work is to develop an x-ray image segmentation approach used to identify the location and the orientation of the cervical vertebrae in medical images. We propose a method for localization of vertebrae by extracting the anterior—left—faces of vertebra contours. This approach is based on automatic corner points of interest detection. For this task, we use the Harris corner detector. The final goal is to determine vertebral motion induced by their movement between two or several positions. The proposed system proceeds in several phases as follows: (a) image acquisition, (b) corner detection, (c) extracting of the corners belonging to vertebra left sides, (d) global estimation of the spine curvature, and (e) anterior face vertebra detection.read more
Citations
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Journal ArticleDOI
A framework of vertebra segmentation using the active shape model-based approach
TL;DR: A medical image segmentation approach based on the Active Shape Model theory for cervical vertebra detection that allows the use of two different models, that is, a global one (with several vertebrae) and a local one ( with a single vertebra).
Journal ArticleDOI
Automatic Lumbar MRI Detection and Identification Based on Deep Learning.
TL;DR: The study demonstrates that a lumbar detection network supported by deep learning can be trained successfully without annotated MRI images, and can be believed that the detection method will assist clinicians to raise working efficiency.
Proceedings ArticleDOI
Fully automatic vertebra detection in x-ray images based on multi-class SVM
TL;DR: This work develops a fully automatic approach for the vertebra detection, based on a learning method, to detect a vertebra by its anterior corners without human intervention.
Journal ArticleDOI
Vertebra identification using template matching modelmp and K-means clustering
TL;DR: An automated vertebra identification method was developed and demonstrated to be robust to noise and occlusion and presents a first step toward an automated computer-aided diagnosis system for cervical spine trauma detection.
Journal ArticleDOI
3-D Reconstruction of the Spine From Biplanar Radiographs Based on Contour Matching Using the Hough Transform
TL;DR: A method based on vertebral contour matching for estimating vertebral orientations and locations based on the generalized Hough transform technique with a deformation tolerant matching strategy is developed and evaluated, indicating its reliability.
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Evaluation of Interest Point Detectors
TL;DR: Two evaluation criteria for interest points' repeatability rate and information content are introduced and different interest point detectors are compared using these two criteria.