Abstract

Chest radiography is considered as one of the most important radiological tools in pulmonary disease diagnosis. Due to the generation of low contrast images of X-ray machines, the detection of the lesions is a difficult issue and prone to error for a radiologist. Hence, a contrast enhancement algorithm is an obvious choice to enhance the contrast of the image, thus increasing the accuracy of detection of the lesions. This paper not only proposes a new algorithm for contrast enhancement of digital chest X-ray images using particle swarm optimization (PSO), but it also introduces a benchmark dataset of digital chest radiographs to justify the supremacy of our proposed algorithm over that of state-of-the-art contrast enhancement algorithms.

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