Literature DB >> 12148738

Development of a computerized method for identifying the posteroanterior and lateral views of chest radiographs by use of a template matching technique.

Hidetaka Arimura1, Shigehiko Katsuragawa, Qiang Li, Takayuki Ishida, Kunio Doi.   

Abstract

In picture archiving and communications systems (PACS) or digital archiving systems, the information on the posteroanterior (PA) and lateral views for chest radiographs is often not recorded or is recorded incorrectly. However, it is necessary to identify the PA or lateral view correctly and automatically for quantitative analysis of chest images for computer-aided diagnosis. Our purpose in this study was to develop a computerized method for correctly identifying either PA or lateral views of chest radiographs. Our approach is to examine the similarity of a chest image with templates that represent the average chest images of the PA or lateral view for various types of patients. By use of a template matching technique with nine template images for patients of different size in two steps, correlation values were obtained for determining whether a chest image is either a PA or a lateral view. The templates for PA and lateral views were prepared from 447 PA and 200 lateral chest images. For a validation test, this scheme was applied to 1,000 test images consisting of 500 PA and 500 lateral chest radiographs, which are different from training cases. In the first step, 924 (92.4%) of the cases were correctly identified by comparison of the correlation values obtained with the three templates for medium-size patients. In the second step, the correlation values with the six templates for small and large patients were compared, and all of the remaining unidentifiable cases were identified correctly.

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Mesh:

Year:  2002        PMID: 12148738     DOI: 10.1118/1.1487426

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  7 in total

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Authors:  Yoichiro Shimizu; Junji Morishita
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2.  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

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

Authors:  K C Santosh; Laurent Wendling
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Authors:  Satoshi Yoshidome; Hidetaka Arimura; Katsumasa Nakamura; Yoshiyuki Shioyama; Kazushige Atsumi; Yasuhiko Nakamura; Hideki Yoshikawa; Kei Nishikawa; Hideki Hirata
Journal:  Biomed Res Int       Date:  2015-01-05       Impact factor: 3.411

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

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

6.  Computerized estimation of patient setup errors in portal images based on localized pelvic templates for prostate cancer radiotherapy.

Authors:  Hidetaka Arimura; Wataru Itano; Yoshiyuki Shioyama; Norimasa Matsushita; Taiki Magome; Tadamasa Yoshitake; Shigeo Anai; Katsumasa Nakamura; Satoshi Yoshidome; Akihiko Yamagami; Hiroshi Honda; Masafumi Ohki; Fukai Toyofuku; Hideki Hirata
Journal:  J Radiat Res       Date:  2012-07-26       Impact factor: 2.724

7.  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

  7 in total

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