Literature DB >> 21267773

Automatic quantification of spinal curvature in scoliotic radiograph using image processing.

Anitha H1, G K Prabhu.   

Abstract

Choosing the most suitable treatment for the scoliosis relies heavily on accurate and reproducible spinal curvature measurement from radiographs. Our objective is to reduce the variability in spinal curvature measurement by reducing the user intervention and bias. In order to determine the reliability of the spinal curvature measurement as it is in the clinical measurement of scoliosis a methodological survey has been carried out that concludes with inter and intra observer error variation. The proposed method list out horizontal inclination of all the vertebrae's in terms of slopes using active contour models and morphological operators. This facilitates the radiologist to decide end vertebrae and hence inter/intra observer variation is completely eliminated. Tables 1 and 2 shows the observer error variation between manual and proposed methods in terms of mean and standard deviation.

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Year:  2011        PMID: 21267773     DOI: 10.1007/s10916-011-9654-9

Source DB:  PubMed          Journal:  J Med Syst        ISSN: 0148-5598            Impact factor:   4.460


  15 in total

1.  The reliability of quantitative analysis on digital images of the scoliotic spine.

Authors:  John Cheung; Dirk J Wever; Albert G Veldhuizen; Jean P Klein; Bert Verdonck; Rutger Nijlunsing; Jan C Cool; Jim R Van Horn
Journal:  Eur Spine J       Date:  2002-07-13       Impact factor: 3.134

2.  Snakes, shapes, and gradient vector flow.

Authors:  C Xu; J L Prince
Journal:  IEEE Trans Image Process       Date:  1998       Impact factor: 10.856

Review 3.  A review of methods for quantitative evaluation of spinal curvature.

Authors:  Tomaz Vrtovec; Franjo Pernus; Bostjan Likar
Journal:  Eur Spine J       Date:  2009-02-27       Impact factor: 3.134

4.  Identifying sources of variability in scoliosis classification using a rule-based automated algorithm.

Authors:  Ian A F Stokes; David D Aronsson
Journal:  Spine (Phila Pa 1976)       Date:  2002-12-15       Impact factor: 3.468

5.  Intra- and interobserver reliability analysis of digital radiographic measurements for pediatric orthopedic parameters using a novel PACS integrated computer software program.

Authors:  Eitan Segev; Yoram Hemo; Shlomo Wientroub; Dror Ovadia; Michael Fishkin; David M Steinberg; Shlomo Hayek
Journal:  J Child Orthop       Date:  2010-05-08       Impact factor: 1.548

6.  A study of vertebral rotation.

Authors:  C L Nash; J H Moe
Journal:  J Bone Joint Surg Am       Date:  1969-03       Impact factor: 5.284

7.  Accuracy and applicability of measurement of the scoliotic angle at the frontal plane by Cobb's method, by Ferguson's method and by a new method.

Authors:  K M Diab; J A Sevastik; R Hedlund; I A Suliman
Journal:  Eur Spine J       Date:  1995       Impact factor: 3.134

8.  Variation in Cobb angle measurements in scoliosis.

Authors:  J E Pruijs; M A Hageman; W Keessen; R van der Meer; J C van Wieringen
Journal:  Skeletal Radiol       Date:  1994-10       Impact factor: 2.199

9.  The validity and reliability of measurements in spinal deformities: a critical appraisal.

Authors:  G Capasso; N Maffulli; V Testa
Journal:  Acta Orthop Belg       Date:  1992       Impact factor: 0.500

10.  Inter- and intraobserver reliability assessment of the Cobb angle: manual versus digital measurement tools.

Authors:  Michaela Gstoettner; Katrin Sekyra; Nadja Walochnik; Peter Winter; Roland Wachter; Christian M Bach
Journal:  Eur Spine J       Date:  2007-06-05       Impact factor: 3.134

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  10 in total

1.  Improving Visibility of Stereo-Radiographic Spine Reconstruction with Geometric Inferences.

Authors:  Sampath Kumar; K Prabhakar Nayak; K S Hareesha
Journal:  J Digit Imaging       Date:  2016-04       Impact factor: 4.056

2.  Reliable and reproducible classification system for scoliotic radiograph using image processing techniques.

Authors:  H Anitha; G K Prabhu; A K Karunakar
Journal:  J Med Syst       Date:  2014-09-27       Impact factor: 4.460

3.  Effective automated prediction of vertebral column pathologies based on logistic model tree with SMOTE preprocessing.

Authors:  Esra Mahsereci Karabulut; Turgay Ibrikci
Journal:  J Med Syst       Date:  2014-04-22       Impact factor: 4.460

4.  Contour and Angle-Function Based Scoliosis Monitoring: Relaxing the Requirement on Image Quality in the Measurement of Spinal Curvature.

Authors:  Pierino G Bonanni
Journal:  Int J Spine Surg       Date:  2017-06-30

Review 5.  Current development and prospects of deep learning in spine image analysis: a literature review.

Authors:  Biao Qu; Jianpeng Cao; Chen Qian; Jinyu Wu; Jianzhong Lin; Liansheng Wang; Lin Ou-Yang; Yongfa Chen; Liyue Yan; Qing Hong; Gaofeng Zheng; Xiaobo Qu
Journal:  Quant Imaging Med Surg       Date:  2022-06

Review 6.  A Review of the Methods on Cobb Angle Measurements for Spinal Curvature.

Authors:  Chen Jin; Shengru Wang; Guodong Yang; En Li; Zize Liang
Journal:  Sensors (Basel)       Date:  2022-04-24       Impact factor: 3.847

7.  Reliability and reproducibility analysis of the Cobb angle and assessing sagittal plane by computer-assisted and manual measurement tools.

Authors:  Weifei Wu; Jie Liang; Yuanli Du; Xiaoyi Tan; Xuanping Xiang; Wanhong Wang; Neng Ru; Jinbo Le
Journal:  BMC Musculoskelet Disord       Date:  2014-02-06       Impact factor: 2.362

8.  Automatic Discoid Lateral Meniscus Diagnosis from Radiographs Based on Image Processing Tools and Machine Learning.

Authors:  Xibai Li; Yan Sun; Juyang Jiao; Haoyu Wu; Chunxi Yang; Xubo Yang
Journal:  J Healthc Eng       Date:  2021-04-20       Impact factor: 2.682

Review 9.  A Survey of Methods and Technologies Used for Diagnosis of Scoliosis.

Authors:  Ilona Karpiel; Adam Ziębiński; Marek Kluszczyński; Daniel Feige
Journal:  Sensors (Basel)       Date:  2021-12-16       Impact factor: 3.576

10.  Computer-Aided Cobb Measurement Based on Automatic Detection of Vertebral Slopes Using Deep Neural Network.

Authors:  Junhua Zhang; Hongjian Li; Liang Lv; Yufeng Zhang
Journal:  Int J Biomed Imaging       Date:  2017-10-03
  10 in total

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