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

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

Possibilistic Intuitionistic Fuzzy c-Means Clustering Algorithm for MRI Brain Image Segmentation

2015 ◽  
Vol 24 (05) ◽  
pp. 1550016 ◽  
Author(s):  
Hanuman Verma ◽  
R. K. Agrawal
Keyword(s):  
Clustering Algorithm ◽  
Brain Images ◽  
Brain Image ◽  
Fuzzy C Means ◽  
Mri Brain ◽  
Intuitionistic Fuzzy ◽  
Fuzzy C Means Clustering ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain ◽  
Mri Brain Images

Accurate segmentation of human brain image is an essential step for clinical study of magnetic resonance imaging (MRI) images. However, vagueness and other ambiguity present between the brain tissues boundaries can lead to improper segmentation. Possibilistic fuzzy c-means (PFCM) algorithm is the hybridization of fuzzy c-means (FCM) and possibilistic c-means (PCM) algorithms which overcomes the problem of noise in the FCM algorithm and coincident clusters problem in the PCM algorithm. A major challenge posed in the PFCM algorithm for segmentation of ill-defined MRI image with noise is to take into account the ambiguity in the final localization of the feature vectors due to lack of qualitative information. This may lead to improper assignment of membership (typicality) value to their desired cluster. In this paper, we have proposed the possibilistic intuitionistic fuzzy c-means (PIFCM) algorithm for Atanassov’s intuitionistic fuzzy sets (A-IFS) which includes the advantages of the PCM, FCM algorithms and A-IFS. Real and simulated MRI brain images are segmented to show the superiority of the proposed PIFCM algorithm. The experimental results demonstrate that the proposed algorithm yields better result.

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

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.

A nonparametric method for intensity inhomogeneity correction in MRI brain images by fusion of Gaussian surfaces

2014 ◽  
Vol 9 (8) ◽  
pp. 1945-1954 ◽  
Author(s):  
Sudip Kumar Adhikari ◽  
Jamuna Kanta Sing ◽  
Dipak Kumar Basu ◽  
Mita Nasipuri ◽  
Punam Kumar Saha
Keyword(s):  
Intensity Inhomogeneity ◽  
Nonparametric Method ◽  
Brain Images ◽  
Mri Brain ◽  
Inhomogeneity Correction ◽  
Mri Brain Images
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