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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Acknowledgements
We are thankful to Center of Excellence in Systems Biology and Biomedical Engineering (TEQIP II) of University of Calcutta for providing the financial support for this research and Peerless Hospitex Hospital for providing their valuable image dataset.
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Mukherjee, J., Sikdar, B., Chakrabarti, A., Kar, M., Das, S. (2018). A Novel Technique for Contrast Enhancement of Chest X-Ray Images Based on Bio-Inspired Meta-Heuristics. In: Chaki, R., Cortesi, A., Saeed, K., Chaki, N. (eds) Advanced Computing and Systems for Security. Advances in Intelligent Systems and Computing, vol 666. Springer, Singapore. https://doi.org/10.1007/978-981-10-8180-4_5
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