Customized Hough transform for robust segmentation of cervical vertebrae from X-ray images

Tezmol, A.; Sari‐Sarraf, Hamed; Mitra, Sunanda; Long, Rodney; Gururajan, Arunkumar

doi:10.1109/iai.2002.999922

Cited by 35 publications

(20 citation statements)

References 6 publications

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“…Techniques exploiting the Hough Transform [12,26,30] are another example. These methods use a large set of templates to capture the great variability in the shape of the vertebrae.…”

Section: Introductionmentioning

confidence: 99%

X-ray image segmentation for vertebral mobility analysis

Benjelloun

Mahmoudi

2008

Int J CARS

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Objective The goal of this work is to extract the parameters determining vertebral motion and its variation during flexion-extension movements using a computer vision tool for estimating and analyzing vertebral mobility. Materials and MethodsTo compute vertebral body motion parameters we propose a comparative study between two segmentation methods proposed and applied to lateral X-ray images of the cervical spine. The two vertebra contour detection methods include (1) a discrete dynamic contour model (DDCM) and (2) a template matching process associated with a polar signature system. These two methods not only enable vertebra segmentation but also extract parameters that can be used to evaluate vertebral mobility. Lateral cervical spine views including 100 views in flexion, extension and neutral orientations were available for evaluation. Vertebral body motion was evaluated by human observers and using automatic methods. Results The results provided by the automated approaches were consistent with manual measures obtained by 15 human observers. Conclusion The automated techniques provide acceptable results for the assessment of vertebral body mobility in flexion and extension on lateral views of the cervical spine. Electronic supplementary materialThe online version of this article (

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“…Techniques exploiting the Hough Transform [12,26,30] are another example. These methods use a large set of templates to capture the great variability in the shape of the vertebrae.…”

Section: Introductionmentioning

confidence: 99%

X-ray image segmentation for vertebral mobility analysis

Benjelloun

Mahmoudi

2008

Int J CARS

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“…to determine the characteristic curves assumed to lie in the spine region 12Y14 and (2) a template-matching-based method. 15,16 The first category includes a method proposed by Zamora et al 13 based on line integrals of image gray scale to determine approximate spine axis location. They reported an orientation error in their algorithm of less than 15-for 34 of the cases in a test set of 40 cervical spine images.…”

Section: Introductionmentioning

confidence: 99%

“…In the template-matching method, a customized implementation of the generalized Hough transform (GHT) is used for the object localization. 15 The GHT was applied to the test set of 50 images using the mean template obtained from the landmark points (LMP) marked by expert radiologists. The results reported claim an average 72.06 points out of 80 LMP inside the bounding box and an average orientation error of 4.16-.…”

Section: Introductionmentioning

confidence: 99%

Automatic Estimation of Orientation and Position of Spine in Digitized X-rays using Mathematical Morphology

Kumar

Thomas

2005

J Digit Imaging

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In this paper, we propose a method for automatic determination of position and orientation of spine in digitized spine X-rays using mathematical morphology. As the X-ray images are usually highly smeared, vertebrae segmentation is a complex process. The image is first coarsely segmented to obtain the location and orientation information of the spine. The state-of-the-art technique is based on the deformation model of a template, and as the vertebrae shape usually shows variation from case to case, accurate representation using a template is a difficult process. The proposed method makes use of the vertebrae morphometry and gray-scale profile of the spine. The top-hat transformation-based method is proposed to enhance the ridge points in the posterior boundary of the spine. For cases containing external objects such as ornaments, H-Maxima transform is used for segmentation and removal of these objects. The Radon transform is then used to estimate the location and orientation of the line joining the ridge point clusters appearing on the boundary of the vertebra body. The method was validated for 100 cervical spine X-ray images, and in all cases, the error in orientation was within the accepted tolerable limit of 15-. The average error was found to be 4.6-. A point on the posterior boundary was located with an accuracy of þ À5.2 mm. The accurate information about location and orientation of the spine is necessary for fine-grained segmentation of the vertebrae using techniques such as active shape modeling. Accurate vertebrae segmentation is needed in successful feature extraction for applications such as content-based image retrieval of biomedical images.

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“…Tezmol et al [4] proposed a Hough transform based method to find the global position, orientation and size of the cervical vertebral column in an xray image. Their method applies a brute force search using a template for all possible orientations, positions and sizes and votes in a four dimensional Hough space.…”

Section: Introductionmentioning

confidence: 99%

Cervical vertebral corner detection using haar-like features and modified hough forest

Arif

Asad

Knapp

et al. 2015

2015 International Conference on Image Processing Theory, Tools and Applications (IPTA)

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract-The neck (cervical spine) is a flexible part of the human body and is particularly vulnerable to injury. Patients suspected of cervical spine injuries are often imaged using lateral view radiographs. Incorrect diagnosis based on these images may lead to serious long-term consequences. Our overarching goal is to develop a computer-aided detection system to help an emergency room physician correctly diagnose a patient's injury. In this paper, we present a method to localize the corners of cervical vertebrae in a set of 90 lateral cervical radiographs. Haar-like features are computed using intensity and gradient image patches, each of which votes for possible corner position using a modified Hough forest regression technique. Votes are aggregated using two dimensional kernel density estimation, to find the location of the corner. Our method demonstrates promising results, identifying corners with an average median error of 2.08 mm. Permanent repository link

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Customized Hough transform for robust segmentation of cervical vertebrae from X-ray images

Cited by 35 publications

References 6 publications

X-ray image segmentation for vertebral mobility analysis

X-ray image segmentation for vertebral mobility analysis

Automatic Estimation of Orientation and Position of Spine in Digitized X-rays using Mathematical Morphology

Cervical vertebral corner detection using haar-like features and modified hough forest

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