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Stationary Wavelet-Oriented Luminance Enhancement Approach for Brain Tumor Detection with Multi-modality Images

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Evolution in Computational Intelligence (FICTA 2023)

Abstract

A brain tumor is an abnormal growth of cells in the brain. However, manually detecting brain tumors is hard because it is hard to find erratically shaped tumors with only one modality and time-consuming. In this work, a novel stationary wavelet-oriented luminance enhancement (SOLE) approach to denoise the multi-modal images. Initially, medical images like MRI, CT, and PET are gathered from publicly available datasets. These multi-modality images are divided into low and high-frequency sub-images using stationary wavelet transform (SWT), which has the advantage of preserving temporal features so that information loss can be stopped. Then the low-frequency and high-frequency images are processed with distribution and denoising modules to remove the noise, respectively. The approximation coefficient is pre-processed using multi-scale retinex with gamma correction for efficaciously retrieving the noise-free image. Consequently, the remaining coefficients are pre-processed using a multi-scale Gaussian bilateral filter, and tracking wavelet denoising (TWD) algorithm in the denoising module dynamically enhanced the color detail information without human intervention so that observed image contrast and visibility are well preserved. Lastly, noise-free image is reconstructed from sub-enhanced images using Inverse-SWT to detect brain tumors. Experimental results show that the proposed algorithm has a mean error rate of 0.03 compared to the other filters. The proposed SOLE technique achieves a less running time of 0.97 s, whereas other existing techniques such as K-SVD, DRAN, 2-stage CNN, and AMF-AWF achieve the running time of 6.9, 1.63, 1.52, and 8.75 s.

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Correspondence to A. Ahilan .

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Ahilan, A., Anlin Sahaya Tinu, M., Jasmine Gnana Malar, A., Muthu Kumar, B. (2023). Stationary Wavelet-Oriented Luminance Enhancement Approach for Brain Tumor Detection with Multi-modality Images. In: Bhateja, V., Yang, XS., Ferreira, M.C., Sengar, S.S., Travieso-Gonzalez, C.M. (eds) Evolution in Computational Intelligence. FICTA 2023. Smart Innovation, Systems and Technologies, vol 370. Springer, Singapore. https://doi.org/10.1007/978-981-99-6702-5_38

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