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Adaptive gamma correction for automatic contrast enhancement of Chest-X-ray images affected by various lung diseases

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Abstract

Lung and respiratory ailments are among the leading causes of illness and fatalities. Coronavirus disease (COVID-19), caused by the SARS-CoV-2 virus, has convinced the world that early and affordable detection improves treatment. X-ray imaging systems are inexpensive and widely available. Chest X-ray (CXR) images are inadequate due to the acquiring environment and technician skill. Hence, CXR image contrast enhancement is necessary for a correct diagnosis. Various lung diseases create variable spatial variation in CXR image contrast and brightness; hence, a single contrast enhancement procedure cannot improve it. In the proposed method CXR images are first classified into four categories depending upon their quality defined by their statistical parameters, before applying adaptive gamma correction for contrast enhancement. The performance of the proposed method is compared with existing methods on four datasets for five different types of lung diseases. The performance of the proposed algorithm is evaluated using parameters, such as Root Mean Square Contrast (RMSC) to determine the relation of contrast enhancement between the original and enhanced image, Contrast Improvement Index (CII) to measure the achieved contrast enhancement and Tenengrad which calculates the variation of intensity in the direction of maximum gradient descent. The qualitative and quantitative performance of the proposed method is found better than the existing methods for CXR images for all five lung diseases, which shows the stable performance of the proposed method and improvement in the processed images.

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Data availability

The data that support the findings of this study are openly available in Chest X-Ray Images (Pneumonia) dataset [14], Tuberculosis (TB)_Chest_Radiography_Database [15], COVID-QU-Ex Dataset [16], and NIH Clinical Center chest X-ray [17].

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Authors and Affiliations

  1. Department of Electronics and Communication, Maulana Azad National Institute of Technology, Bhopal, M.P., India

    Vivek Kumar Yadav & Jyoti Singhai

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  1. Vivek Kumar Yadav
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  2. Jyoti Singhai
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Contributions

All authors contributed to the study’s conception and design. Material preparation, data collection, and analysis were performed by Vivek Kumar Yadav, Jyoti Singhai. The first draft of the manuscript was written by Vivek Kumar Yadav and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Vivek Kumar Yadav.

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Yadav, V.K., Singhai, J. Adaptive gamma correction for automatic contrast enhancement of Chest-X-ray images affected by various lung diseases. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-023-18083-x

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