A transform-based fast fuzzy C-means approach for high brain MRI segmentation accuracy

• Published in: Applied soft computing Vol. 76; pp. 156 - 173
• Main Authors: Singh, Chandan, Bala, Anu
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2019
• Subjects: DCT-filtering; Discrete cosine transform; Fuzzy C-means; MRI segmentation; Segmentation accuracy; Segmentation accuracy; Fuzzy C-means; MRI segmentation; DCT-filtering; Discrete cosine transform
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2018.12.005

Segmentation of brain magnetic resonance (MR) images has a significant impact on the computer-aided diagnosis and analysis. However, due to the presence of noise in medical images, many segmentation methods suffer from limited segmentation accuracy. To reduce the effect of noise and achieve high segmentation accuracy many approaches based on the local and nonlocal information in the spatial domain have been proposed in the past. Recently, the authors have proposed a discrete cosine transform (DCT)-based local and nonlocal fuzzy C-means method (DCT-LNLFCM) which performs much better than the existing methods. However, the method is slow in speed. This paper presents a fast DCT-based nonlocal fuzzy C-means (DCT-NLFCM) segmentation method which is not only very fast than the DCT-LNLFCM, but also provides better segmentation results. The proposed method uses DCT-based MR pre-filtered image to achieve high segmentation accuracy and its histogram enables to achieve very high computation speed. Detailed experiments are conducted to establish the superiority of the proposed method over the state-of-the-art unsupervised methods. •A fast DCT-based MR image segmentation approach proposed.•The approach provides very high segmentation accuracy.•The algorithm is very fast as compared to the existing DCT-based algorithm.•The DCT-domain pre-filtered image is used to achieve high speed.•The proposed method is robust to noise.