Literature DB >> 18273669

Spine localization in X-ray images using interest point detection.

Mohammed Benjelloun1, Saïd Mahmoudi.   

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.

Entities:  

Mesh:

Year:  2008        PMID: 18273669      PMCID: PMC3043695          DOI: 10.1007/s10278-007-9099-3

Source DB:  PubMed          Journal:  J Digit Imaging        ISSN: 0897-1889            Impact factor:   4.056


  4 in total

1.  Object localization and border detection criteria design in edge-based image segmentation: automated learning from examples.

Authors:  M Brejl; M Sonka
Journal:  IEEE Trans Med Imaging       Date:  2000-10       Impact factor: 10.048

Review 2.  Current methods in medical image segmentation.

Authors:  D L Pham; C Xu; J L Prince
Journal:  Annu Rev Biomed Eng       Date:  2000       Impact factor: 9.590

3.  Adapting Active Shape Models for 3D segmentation of tubular structures in medical images.

Authors:  Marleen de Bruijne; Bram van Ginneken; Max A Viergever; Wiro J Niessen
Journal:  Inf Process Med Imaging       Date:  2003-07

4.  Automatic estimation of orientation and position of spine in digitized X-rays using mathematical morphology.

Authors:  V P Dinesh Kumar; Tessamma Thomas
Journal:  J Digit Imaging       Date:  2005-09       Impact factor: 4.056

  4 in total
  6 in total

Review 1.  Vertebra identification using template matching modelmp and K-means clustering.

Authors:  Mohamed Amine Larhmam; Mohammed Benjelloun; Saïd Mahmoudi
Journal:  Int J Comput Assist Radiol Surg       Date:  2013-07-24       Impact factor: 2.924

2.  Automatic Lumbar MRI Detection and Identification Based on Deep Learning.

Authors:  Yujing Zhou; Yuan Liu; Qian Chen; Guohua Gu; Xiubao Sui
Journal:  J Digit Imaging       Date:  2019-06       Impact factor: 4.056

Review 3.  Self-learning computers for surgical planning and prediction of postoperative alignment.

Authors:  Renaud Lafage; Sébastien Pesenti; Virginie Lafage; Frank J Schwab
Journal:  Eur Spine J       Date:  2018-02-09       Impact factor: 3.134

4.  Automatic Lumbar Spine Tracking Based on Siamese Convolutional Network.

Authors:  Yuan Liu; Xiubao Sui; Chengwei Liu; Xiaodong Kuang; Yong Hu
Journal:  J Digit Imaging       Date:  2020-04       Impact factor: 4.056

5.  A framework of vertebra segmentation using the active shape model-based approach.

Authors:  Mohammed Benjelloun; Saïd Mahmoudi; Fabian Lecron
Journal:  Int J Biomed Imaging       Date:  2011-07-31

6.  Image Segmentation and Analysis of Flexion-Extension Radiographs of Cervical Spines.

Authors:  Eniko T Enikov; Rein Anton
Journal:  J Med Eng       Date:  2014-10-13
  6 in total

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