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

Abstract

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.