Abstract
Noise is added to an image while acquiring or transmitting an image. One of the most commonly added noises is impulse noise. This work aims to remove this impulse noise from the high-density noise in medical images. The proposed algorithm is executed in two stages for removal of noise in an image. The first stage is for removing low-density noise which is cascaded to the second stage for removing high-density noise. First order neighborhood pixels are considered for identifying noisy pixels and cascaded filter is considered for replacement of the identified noisy pixel. This algorithm has given a good result when compared to other popular algorithms and has good noise removing capabilities. Different grayscale Magnetic Resonance Imaging (MRI) brain images are tested by using this algorithm and has given better results of the various performance metrics at different noise densities.
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G, V.K., G, S.R., B, P.R. (2018). High-Density Noise Removal Algorithm for Brain Image Analysis. In: Satapathy, S., Tavares, J., Bhateja, V., Mohanty, J. (eds) Information and Decision Sciences. Advances in Intelligent Systems and Computing, vol 701. Springer, Singapore. https://doi.org/10.1007/978-981-10-7563-6_45
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DOI: https://doi.org/10.1007/978-981-10-7563-6_45
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