Literature DB >> 22539411

A simple method for identifying image orientation of chest radiographs by use of the center of gravity of the image.

Hideo Nose1, Yasushi Unno, Masayuki Koike, Junji Shiraishi.   

Abstract

Bedside chest radiography is a frequent X-ray examination when patients are physically incapacitated. An X-ray cassette with an imaging plate is inserted below the patient's body, and the image orientation of the radiograph is determined by the direction of insertion. Therefore, an incorrect direction of insertion would yield an incorrect image orientation for diagnosis, if no correction was performed on the resulting image data. We aimed to develop a computerized method that identifies the image orientation of chest radiographs by using the center of gravity (COG) of the images in terms of pixel values. To develop the computerized method, we used 247 chest images contained in the Japanese Society of Radiological Technology database as training cases, and 1833 bedside chest radiographs obtained in our institution for validation testing. As a result for the 247 training images, the angles which were obtained from directions between the COG of pixel values and the center of the image were distributed between 162.7° and 224.4° in a clockwise direction. We used the angle of the COG to identify the correct view orientation. The range of angles (139.1°-229.0°) for the COG in the chest image with correct image orientation was determined with a 99 % confidence interval for the angles of the COGs obtained from the training images. As a result of the validation test based on the range of angles determined, 99.7 % of the 1833 test images were identified correctly.

Entities:  

Mesh:

Year:  2012        PMID: 22539411     DOI: 10.1007/s12194-012-0155-4

Source DB:  PubMed          Journal:  Radiol Phys Technol        ISSN: 1865-0333


  9 in total

1.  Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists' detection of pulmonary nodules.

Authors:  J Shiraishi; S Katsuragawa; J Ikezoe; T Matsumoto; T Kobayashi; K Komatsu; M Matsui; H Fujita; Y Kodera; K Doi
Journal:  AJR Am J Roentgenol       Date:  2000-01       Impact factor: 3.959

Review 2.  Computer-aided diagnosis in chest radiography: a survey.

Authors:  B van Ginneken; B M ter Haar Romeny; M A Viergever
Journal:  IEEE Trans Med Imaging       Date:  2001-12       Impact factor: 10.048

3.  Determining the view of chest radiographs.

Authors:  Thomas M Lehmann; O Güld; Daniel Keysers; Henning Schubert; Michael Kohnen; Berthold B Wein
Journal:  J Digit Imaging       Date:  2003-12-15       Impact factor: 4.056

4.  Orientation correction for chest images.

Authors:  E Pietka; H K Huang
Journal:  J Digit Imaging       Date:  1992-08       Impact factor: 4.056

5.  Recognition of chest radiograph orientation for picture archiving and communications systems display using neural networks.

Authors:  J M Boone; S Seshagiri; R M Steiner
Journal:  J Digit Imaging       Date:  1992-08       Impact factor: 4.056

6.  Automatic image hanging protocol for chest radiographs in PACS.

Authors:  Hui Luo; Wei Hao; David H Foos; Craig W Cornelius
Journal:  IEEE Trans Inf Technol Biomed       Date:  2006-04

7.  Computer-aided detection (CAD) of lung nodules and small tumours on chest radiographs.

Authors:  D W De Boo; M Prokop; M Uffmann; B van Ginneken; C M Schaefer-Prokop
Journal:  Eur J Radiol       Date:  2009-09-10       Impact factor: 3.528

8.  Quantitative kinetic analysis of lung nodules using the temporal subtraction technique in dynamic chest radiographies performed with a flat panel detector.

Authors:  Yuichiro Tsuchiya; Yoshie Kodera; Rie Tanaka; Shigeru Sanada
Journal:  J Digit Imaging       Date:  2008-04-16       Impact factor: 4.056

9.  Dual energy subtraction digital radiography improves performance of a next generation computer-aided detection program.

Authors:  Jason D Balkman; Sonali Mehandru; Elena DuPont; Ronald D Novak; Robert C Gilkeson
Journal:  J Thorac Imaging       Date:  2010-02       Impact factor: 3.000

  9 in total
  1 in total

1.  Development of a method of automated extraction of biological fingerprints from chest radiographs as preprocessing of patient recognition and identification.

Authors:  Yoichiro Shimizu; Junji Morishita
Journal:  Radiol Phys Technol       Date:  2017-04-27
  1 in total

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