Noise adaptive FCM algorithm for segmentation of MRI brain images using local and non-local spatial information

Image segmentation is an important task in many image processing applications. Fuzzy C means algorithm has been widely used for the segmentation. There are many versions of the traditional FCM algorithm which uses the local spatial information to increase the robustness of this algorithm in presence of noise, but all these algorithms do not successfully segment the images contaminated by heavy noise. In order to solve this problem, non-local spatial information present in the image is utilized. The filtering parameter ‘h’ in the non-local information is a crucial parameter which needs to be appropriately determined, irrespective of using a single constant value of ‘h’; we can determine its value using the standard deviation of noise present in the image. The adaptive non-local information determined is termed as noise adaptive non-local spatial information. This adaptive non-local information is used in the FCM algorithm for the segmentation of MRI images. In this paper Noise adaptive FCM algorithm (NAFCM) using adaptive non-local information is proposed. Therefore the proposed algorithm utilizes adaptive non-local information making it robust in presence of noise as well as preserving the details present in the image. The efficiency of the proposed algorithm is demonstrated by validation studies on synthetic as well as simulated brain MRI images. The results of the proposed algorithm show that the proposed algorithm is robust to noise and as compared to other state of the art algorithms.

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Segmentation of MRI brain images by incorporating intensity inhomogeneity and spatial information using probabilistic fuzzy c-means clustering algorithm

2012 International Conference on Communications, Devices and Intelligent Systems (CODIS) ◽

10.1109/codis.2012.6422153 ◽

2012 ◽

Author(s):

Sudip Kumar Adhikari ◽

J. K. Sing ◽

D. K. Basu ◽

M. Nasipuri ◽

P. K. Saha

Keyword(s):

Clustering Algorithm ◽

Spatial Information ◽

Intensity Inhomogeneity ◽

Brain Images ◽

Fuzzy C Means ◽

Mri Brain ◽

Fuzzy C Means Clustering ◽

Mri Brain Images

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FCM Algorithm Based on Improved Automatic Segmentation of MRI Brain Images

Computer Science for Environmental Engineering and EcoInformatics - Communications in Computer and Information Science ◽

10.1007/978-3-642-22691-5_5 ◽

2011 ◽

pp. 24-29 ◽

Cited By ~ 2

Author(s):

Zhuang Miao ◽

Chengcheng Liu ◽

Xiaomei Lin

Keyword(s):

Automatic Segmentation ◽

Brain Images ◽

Fcm Algorithm ◽

Mri Brain ◽

Mri Brain Images

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An Extended Fuzzy C-Means Segmentation for an Efficient BTD With the Region of Interest of SCP

International Journal of Information Technology Project Management ◽

10.4018/ijitpm.2021100102 ◽

2021 ◽

Vol 12 (4) ◽

pp. 11-24

Author(s):

Subba Reddy K. ◽

Rajendra Prasad K.

Keyword(s):

Image Segmentation ◽

Spatial Information ◽

Region Of Interest ◽

Tumor Segmentation ◽

Brain Images ◽

Brain Image ◽

Fuzzy C Means ◽

Mri Brain ◽

Irregular Boundaries ◽

Mri Brain Images

Magnetic resonance imaging (MRI) is the primary source to diagnose a brain tumor or masses in the medical sciences. It is emerging to detect the tumors from the scanned MRI brain images at early stages for the best treatments. Existing image segmentation techniques, morphological, fuzzy c-means are wildly successful in the extraction region of interest (ROI) in brain image segmentation. Proper extraction of ROIs is useful for regularizing the regions of tumors from the brain image with effective binarization in the segmentation. However, the existing techniques are limiting the irregular boundaries or shapes in tumor segmentation. Thus, this paper presents the proposed work extending the FCM with the spatial correlated pixel (RSCP), known as FCM-RSCP. It overcomes the problem of irregular boundaries by assessing correlated spatial information during segmentation. Benchmarked MRI brain images are used in the experiment for demonstrating the efficiency of the proposed methodology.

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