Improved Segmentation of MRI Brain Images by Denoising and Contrast Enhancement

• Published in: Indian journal of science and technology Vol. 8; no. 22; p. 1
• Main Authors: Sasirekha, N, Kashwan, K R
• Format: Journal Article
• Language: English
• Published: 01.09.2015
• Subjects: Algorithms; Image contrast; Image quality; Magnetic resonance imaging; Noise; Noise reduction; Segmentation; Tumours
• ISSN: 0974-6846; 0974-5645
• DOI: 10.17485/ijst/2015/v8i22/73050

The Rician noise in Magnetic Resonance Image (MRI) degrades the image quality and thus, accuracy in segmentation is reduced and localization of tumour may not be precise. In this article, a robust approach is proposed which estimates and removes the Rician noise of 2D MRI for improving segmentation and detection of tumours. First, a robust Rician noise estimation algorithm is employed to identify all the pixels with high Rician noise. Second, a bilateral filter based denoising algorithm is employed to filter image in the wavelet domain. Successively a bilateral filter parameter optimization method is adopted, which uses the noise, contrast and frequency components in MRI to select suitable filter parameters for Bilateral Filter. The algorithm is tested both in synthetic and real-time clinical images of tumour affected human brain. The simulation tests show that the denoising and contrast enhancement improves the segmentation of images. The performance of the proposed approach is improved by 29% in segmentation of synthetic images compared to the existing similar techniques. Similarly, an improvement of 22% in segmentation is observed for real-time images.