Literature DB >> 1520745

Orientation correction for chest images.

E Pietka1, H K Huang.   

Abstract

This report presents an automatic procedure that determines the orientation of computed radiography (CR) chest images and rotates them to a standard position to be viewed by radiologists. As an input, CR images of a normalized size of 1,000 x 1,000 or 2,000 x 2,000 pixels are used. The analysis is performed in three steps. First, the orientation of the spine within the image is determined. Then, a function searches for upper extremities and the subdiaphragm. Finally, the lungs are extracted and their areas are compared. This indicates whether the image needs to be y-axis flipped. These three steps set the value of three parameters on the basis of which the final rotation angle is determined. The procedure has been implemented in the clinics at UCLA. The rate of correctly rotated images is 95.4%.

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Mesh:

Year:  1992        PMID: 1520745     DOI: 10.1007/bf03167768

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


  3 in total

Review 1.  Infrastructure design of a picture archiving and communication system.

Authors:  H K Huang; R K Taira
Journal:  AJR Am J Roentgenol       Date:  1992-04       Impact factor: 3.959

2.  Computer-assisted phalangeal analysis in skeletal age assessment.

Authors:  E Pietka; M F McNitt-Gray; M L Kuo; H K Huang
Journal:  IEEE Trans Med Imaging       Date:  1991       Impact factor: 10.048

3.  A picture archiving and communication system module for radiology.

Authors:  R K Taira; H K Huang
Journal:  Comput Methods Programs Biomed       Date:  1989 Oct-Nov       Impact factor: 5.428

  3 in total
  9 in total

1.  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

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

Authors:  Hideo Nose; Yasushi Unno; Masayuki Koike; Junji Shiraishi
Journal:  Radiol Phys Technol       Date:  2012-04-27

3.  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

4.  A Method to Recognize Anatomical Site and Image Acquisition View in X-ray Images.

Authors:  Xiao Chang; Thomas Mazur; H Harold Li; Deshan Yang
Journal:  J Digit Imaging       Date:  2017-12       Impact factor: 4.056

5.  Angular relational signature-based chest radiograph image view classification.

Authors:  K C Santosh; Laurent Wendling
Journal:  Med Biol Eng Comput       Date:  2018-01-22       Impact factor: 2.602

6.  Displaying radiologic images on personal computers: image processing and analysis.

Authors:  T Gillespy; A H Rowberg
Journal:  J Digit Imaging       Date:  1994-05       Impact factor: 4.056

7.  Lung segmentation in digital radiographs.

Authors:  E Pietka
Journal:  J Digit Imaging       Date:  1994-05       Impact factor: 4.056

8.  Deep Transfer Learning for COVID-19 Prediction: Case Study for Limited Data Problems.

Authors:  Saleh Albahli; Waleed Albattah
Journal:  Curr Med Imaging       Date:  2021

9.  Detection of coronavirus disease from X-ray images using deep learning and transfer learning algorithms.

Authors:  Saleh Albahli; Waleed Albattah
Journal:  J Xray Sci Technol       Date:  2020       Impact factor: 1.535

  9 in total

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