Literature DB >> 25261171

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

H Anitha1, G K Prabhu, A K Karunakar.   

Abstract

Scoliosis classification is useful for guiding the treatment and testing the clinical outcome. State-of-the-art classification procedures are inherently unreliable and non-reproducible due to technical and human judgmental error. In the current diagnostic system each examiner will have diagrammatic summary of classification procedure, number of scoliosis curves, apex level, etc. It is very difficult to define the required anatomical parameters in the noisy radiographs. The classification system demands automatic image understanding system. The proposed automated classification procedures extracts the anatomical features using image processing and applies classification procedures based on computer assisted algorithms. The reliability and reproducibility of the proposed computerized image understanding system are compared with manual and computer assisted system using Kappa values.

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Year:  2014        PMID: 25261171     DOI: 10.1007/s10916-014-0124-z

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


  10 in total

1.  A new operative classification of idiopathic scoliosis: a peking union medical college method.

Authors:  Guixing Qiu; Jianguo Zhang; Yipeng Wang; Hongguang Xu; Jia Zhang; Xisheng Weng; Jin Lin; Yu Zhao; Jianxiong Shen; Xinyu Yang; Keith D K Luk; Duosai Lu; William W Lu
Journal:  Spine (Phila Pa 1976)       Date:  2005-06-15       Impact factor: 3.468

2.  Reliability assessment of Cobb angle measurements using manual and digital methods.

Authors:  Michelle C Tanure; Alan P Pinheiro; Anamaria S Oliveira
Journal:  Spine J       Date:  2010-04-01       Impact factor: 4.166

3.  Computer-aided assessment of scoliosis on posteroanterior radiographs.

Authors:  Junhua Zhang; Edmond Lou; Douglas L Hill; James V Raso; Yuanyuan Wang; Lawrence H Le; Xinling Shi
Journal:  Med Biol Eng Comput       Date:  2009-12-10       Impact factor: 2.602

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

Authors:  Anitha H; G K Prabhu
Journal:  J Med Syst       Date:  2011-01-26       Impact factor: 4.460

6.  The selection of fusion levels in thoracic idiopathic scoliosis.

Authors:  H A King; J H Moe; D S Bradford; R B Winter
Journal:  J Bone Joint Surg Am       Date:  1983-12       Impact factor: 5.284

7.  A specific scoliosis classification correlating with brace treatment: description and reliability.

Authors:  Manuel D Rigo; Mónica Villagrasa; Dino Gallo
Journal:  Scoliosis       Date:  2010-01-27

8.  Intraobserver and interobserver reliability of the classification of thoracic adolescent idiopathic scoliosis.

Authors:  L G Lenke; R R Betz; K H Bridwell; D H Clements; J Harms; T G Lowe; H L Shufflebarger
Journal:  J Bone Joint Surg Am       Date:  1998-08       Impact factor: 5.284

9.  Reliability analysis for manual adolescent idiopathic scoliosis measurements.

Authors:  Timothy R Kuklo; Benjamin K Potter; David W Polly; Michael F O'Brien; Teresa M Schroeder; Lawrence G Lenke
Journal:  Spine (Phila Pa 1976)       Date:  2005-02-15       Impact factor: 3.468

10.  Computer-assisted algorithms improve reliability of King classification and Cobb angle measurement of scoliosis.

Authors:  Ian A F Stokes; David D Aronsson
Journal:  Spine (Phila Pa 1976)       Date:  2006-03-15       Impact factor: 3.468

  10 in total
  1 in total

Review 1.  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

  1 in total

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