Denoising of skull stripped brain tumor MR images - Akshath, M J; Sheshadri, H S (International Journal of Computer Sciences and Engineering, 2025)
To improve the accuracy in segmenting the brain tumor from Magnetic resonance images (MR), pre-processing of raw MR images plays significant importance, is required for proper visualization and detection of the tumor part, to increase the quality of the image affected by the noise. The paper is mainly focused on skull removal and denoising techniques. Inclusion of skull may lead to misclassification of tumour tissues and might increase the time complexity. In this study T1, T1contrast, T2, Flair MR images is analysed in axial, sagittal and coronal planes. Mathematical morphology operation with histogram thresholding is performed to remove the skull region. Denoising the images with various filters and evaluation of filters in terms of mean square error (MSE), signal to noise ratio (SNR) and peak signal to noise ratio (PSNR) are considered for the study. The Algorithm developed provides better results for skull stripping and high PSNR is obtained for wiener filter reducing the Gaussian noise by preserving the edges