Abstract
Medical image analysis is very important for the proper and efficient diagnosis of various disorders. Detection and diagnosis of various brain disorders are very challenging because of the complex shape and structure of the brain. Among the various medical imaging tools, Magnetic Resonance Imaging (MRI) gives the most precise image of the brain structure. But, these images suffer from low contrast and are also distorted by noise. So, these images need to be pre-processed such that accurate and precise information can be extracted from them for further analysis and detection of various brain disorders. In this paper, a hybrid approach has been proposed for pre-processing, wherein the contrast of the MRI image has been enhanced using the Minimum Mean Brightness Error Bi-Histogram Equalization (MMBEBHE) approach and denoising has been done using a combination of Wiener and bilateral filter. The results of the proposed approach have been analyzed by adding speckle and Gaussian noise considering Peak Signal to Noise Ratio (PSNR), Root Mean Square (RMS) Contrast, Structural Similarity Index Measure (SSIM), Signal to Noise Ratio (SNR), and Normalized Correlation (NC) as performance parameters.
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Acknowledgements
This work is part of a bilateral Indian-Bulgarian cooperation research project between Technical University of Sofia, Bulgaria and Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Sonepat, India, under the title “Contemporary Approaches for Processing and Analysis of Multidimensional Signals in Telecommunications,” financed by the Department of Science and Technology (DST), India, and the Ministry of Education and Science, Bulgaria.
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Lather, M., Singh, P. (2022). Contrast Enhancement and Noise Removal from Medical Images Using a Hybrid Technique. In: Kountchev, R., Mironov, R., Nakamatsu, K. (eds) New Approaches for Multidimensional Signal Processing. Smart Innovation, Systems and Technologies, vol 270. Springer, Singapore. https://doi.org/10.1007/978-981-16-8558-3_17
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DOI: https://doi.org/10.1007/978-981-16-8558-3_17
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