Testing global histogram equalization and unsharp mask algorithms 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 combination 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 converting 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 combination 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 increased quality of second series images.Conclusions: The use of digital image processing algorithms such as HE or UM on conventional CXR images can increase quality of images.