Testing Global Histogram Equalization and Unsharp Mask Algo-rithms for Processing Conventional Chest X-Ray Images.


A Mohammadi 1 , J Aghazadeh 2 , AA Ghate 1 , SB Moosavi-toomatari 3 , * , N Se-pehrvand 4 , SE Moosavi-toomatari 5 , M Mohammad Ghasemi-rad 3

1 Associate Professor, Department of Radiology,

2 Assistant Professor, Department of Neurosurgery, Imam Khomeini Training Hospital, Urmia University of Medical Sciences, Urmia, Iran,

3 Medical Doctor, Students Research Committee, Urmia University of Medi-cal Sciences, Urmia, Iran,

4 Medical Doctor, National Institute of Health Research, Tehran University of Medical Sciences, Tehran, Iran,

5 Medical Intern, Students Research Com-mittee, Tabriz University of Medical Sciences, Tabriz, Iran.

How to Cite: Mohammadi A, Aghazadeh J, Ghate A, Moosavi-toomatari S, Se-pehrvand N, et al. Testing Global Histogram Equalization and Unsharp Mask Algo-rithms for Processing Conventional Chest X-Ray Images., Shiraz E-Med J. Online ahead of Print ; 12(4):172-8.


Shiraz E-Medical Journal: 12 (4); 172-8
Published Online: October 1, 2011
Article Type: Research Article
Received: April 3, 2011
Accepted: June 25, 2011


Introduction: Imaging methods are progressing in a rapidly manner, but the problem which we, as the health providers always encounter with is the expensive costs of different devices and our limited budget to provide them.

Aims: The aim of this study is to evaluate the usefulness of Histogram Equalization (HE) and Unsharp Mask (UM) on the conventional CXR images.

Methods and Material: In Urmia University of Medical Sciences, we designed a windows-based computer program that contains histogram equalization (HE), unsharp mask (UM) and com-bination of HE and UM algorithms with adjusted parameters to process conventional chest x-ray (CXR) images. Two series of CXR images including 49 images without major pulmonary disorder and 45 images with pulmonary parenchymal disorders were selected. After convert-ing them to digital format, images were processed with HE, UM and combination of HE and UM techniques. In each series, original and processed images were saved in 4 databases. Two board-certified general radiologists (with 6 and 5 years experience) analyzed images. Saved images were displayed to radiologists randomly and separately. Quality of each image was saved as a scale from 1 (very low quality) to 5 (excellent). We used a variance-based statistical technique to analyze quality.

Statistical analysis used: To compare the quality of each algorithm (GHE, UM and combina-tion of GHE and UM), a variance-based statistical analysis was done.

Results: In the first series images, HE and combination of HE and UM algorithms increased quality of images, but UM technique was not suitable, solely. Also, all three techniques in-creased quality of second series images.

Conclusions: The use of digital image processing algorithms such as HE or UM on conven-tional CXR images can increase quality of images.

Full Text

Full text is available in PDF

© 2011, Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.