Literature DB >> 16617619

Automatic image hanging protocol for chest radiographs in PACS.

Hui Luo1, Wei Hao, David H Foos, Craig W Cornelius.   

Abstract

Chest radiography is one of the most widely used techniques in diagnostic imaging. It comprises at least one-third of all diagnostic radiographic procedures in hospitals. However, in the picture archive and communication system, images are often stored with the projection and orientation unknown or mislabeled, which causes inefficiency for radiologists' interpretation. To address this problem, an automatic hanging protocol for chest radiographs is presented. The method targets the most effective region in a chest radiograph, and extracts a set of size-, rotation-, and translation-invariant features from it. Then, a well-trained classifier is used to recognize the projection. The orientation of the radiograph is later identified by locating the neck, heart, and abdomen positions in the radiographs. Initial experiments are performed on the radiographs collected from daily routine chest exams in hospitals and show promising results. Using the presented protocol, 98.2% of all cases could be hung correctly on projection view (without protocol, 62%), and 96.1% had correct orientation (without protocol, 75%). A workflow study on the protocol also demonstrates a significant improvement in efficiency for image display.

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Year:  2006        PMID: 16617619     DOI: 10.1109/titb.2005.859872

Source DB:  PubMed          Journal:  IEEE Trans Inf Technol Biomed        ISSN: 1089-7771


  7 in total

1.  Empirical investigation of radiologists' priorities for PACS selection: an analytical hierarchy process approach.

Authors:  Vivek Joshi; Kyootai Lee; David Melson; Vamsi R Narra
Journal:  J Digit Imaging       Date:  2011-08       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.  PACS administrators' and radiologists' perspective on the importance of features for PACS selection.

Authors:  Vivek Joshi; Vamsi R Narra; Kailash Joshi; Kyootai Lee; David Melson
Journal:  J Digit Imaging       Date:  2014-08       Impact factor: 4.056

Review 4.  Artificial intelligence in paediatric radiology: Future opportunities.

Authors:  Natasha Davendralingam; Neil J Sebire; Owen J Arthurs; Susan C Shelmerdine
Journal:  Br J Radiol       Date:  2020-09-17       Impact factor: 3.039

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

Review 6.  A review of existing and potential computer user interfaces for modern radiology.

Authors:  Antoine Iannessi; Pierre-Yves Marcy; Olivier Clatz; Anne-Sophie Bertrand; Maki Sugimoto
Journal:  Insights Imaging       Date:  2018-05-16

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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