Literature DB >> 22094308

A computerized method for automated identification of erect posteroanterior and supine anteroposterior chest radiographs.

E-Fong Kao1, Wei-Chen Lin, Jui-Sheng Hsu, Ming-Chung Chou, Twei-Shiun Jaw, Gin-Chung Liu.   

Abstract

A computerized scheme was developed for automated identification of erect posteroanterior (PA) and supine anteroposterior (AP) chest radiographs. The method was based on three features, the tilt angle of the scapula superior border, the tilt angle of the clavicle and the extent of radiolucence in lung fields, to identify the view of a chest radiograph. The three indices A(scapula), A(clavicle) and C(lung) were determined from a chest image for the three features. Linear discriminant analysis was used to classify PA and AP chest images based on the three indices. The performance of the method was evaluated by receiver operating characteristic analysis. The proposed method was evaluated using a database of 600 PA and 600 AP chest radiographs. The discriminant performances Az of A(scapula), A(clavicle) and C(lung) were 0.878 ± 0.010, 0.683 ± 0.015 and 0.962 ± 0.006, respectively. The combination of the three indices obtained an Az value of 0.979 ± 0.004. The results indicate that the combination of the three indices could yield high discriminant performance. The proposed method could provide radiologists with information about the view of chest radiographs for interpretation or could be used as a preprocessing step for analyzing chest images.

Mesh:

Year:  2011        PMID: 22094308     DOI: 10.1088/0031-9155/56/24/004

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  3 in total

1.  AI-driven deep CNN approach for multi-label pathology classification using chest X-Rays.

Authors:  Saleh Albahli; Hafiz Tayyab Rauf; Abdulelah Algosaibi; Valentina Emilia Balas
Journal:  PeerJ Comput Sci       Date:  2021-04-20

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

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

3.  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

  3 in total

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