Testing global histogram equalization and unsharp mask algorithms for processing conventional chest X-ray images

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.