Improving Diagnostic Viewing of Region of Interest in Lung Computed Tomography Image Using Unsharp Masking and Singular Value Decomposition

Computed Tomography (CT) technology has been widely used for detecting and diagnosing lung disease. To improve the visibility of essential features in a CT image, reducing noise and blur, sharpening features, and increasing contrast are necessary. In this study, we propose a method to improve these features and gain better characteristics of region of interest in lung CT images for a right diagnosis. Processing of the proposed method consists of non-local means filter for removing noise, unsharp masking for deblurring and sharpening image, and finally singular value decomposition for enhancing contrast of the region of interest. The method was evaluated in terms of improvement in contrast based on contrast improvement ratio in lung CT images. The results clearly demonstrated that our method reached a higher contrast improvement ratio compared to histogram equalization, unsharp masking, contrast-limited adaptive histogram equalization, and singular value equalization methods and helped to increase the clarity of relevant details without distorting the images.