Improved Bias-corrected Fuzzy C-means Segmentation of Brain MRI Data (original) (raw)
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Pattern Recognition Letters, 2005
The segmentation of magnetic resonance images (MRI) is a challenging problem that has received an enormous amount of attention lately. Many researchers have applied various techniques however fuzzy c-means (FCM) based algorithms have produced better results compared to other methods. In this paper, we present a modified FCM algorithm for bias (also called intensity in-homogeneities) estimation and segmentation of MRI. Normally, the intensity inhomogeneities are attributed to imperfections in the radio-frequency coils or to the problems associated with the image acquisition. Our algorithm is formulated by modifying the objective function of the standard FCM and it has the advantage that it can be applied at an early stage in an automated data analysis before a tissue model is available. The proposed method can deal with the intensity in-homogeneities and Gaussian noise effectively. We have conducted extensive experimental and have compared our results with other reported methods. The results using simulated images and real MRI data show that our method provides better results compared to standard FCM-based algorithms and other modified FCM-based techniques.
A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data
Medical Imaging, …, 2002
In this paper, we present a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the radio-frequency coils or to problems associated with the acquisition sequences. The result is a slowly varying shading artifact over the image that can produce errors with conventional intensity-based classification. Our algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel (voxel) to be influenced by the labels in its immediate neighborhood. The neighborhood effect acts as a regularizer and biases the solution toward piecewise-homogeneous labelings. Such a regularization is useful in segmenting scans corrupted by salt and pepper noise. Experimental results on both synthetic images and MR data are given to demonstrate the effectiveness and efficiency of the proposed algorithm.
International Journal of Imaging Systems and Technology, 2016
This paper proposes a fully automated method for MR brain image segmentation into Gray Matter, White Matter and Cerebro-spinal Fluid. It is an extension of Fuzzy C Means Clustering Algorithm which overcomes its drawbacks, of sensitivity to noise and inhomogeneity. In the conventional FCM, the membership function is computed based on the Euclidean distance between the pixel and the cluster center. It does not take into consideration the spatial correlation among the neighboring pixels. This means that the membership values of adjacent pixels belonging to the same cluster may not have the same range of membership value due to the contamination of noise and hence misclassified. Hence, in the proposed method, the membership function is convolved with mean filter and thus the local spatial information is incorporated in the clustering process. The method further includes pixel re-labeling and contrast enhancement using non-linear mapping to improve the segmentation accuracy. The proposed method is applied to both simulated and real T1weighted MR brain images from BrainWeb and IBSR database. Experiments show that there is an increase in segmentation accuracy of around 30% over the conventional methods and 6% over the state of the art methods.
Bias field estimation and adaptive segmentation of MRI data using a modified fuzzy C-means algorithm
Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), 1999
In this paper, we present a novel algorithm for adaptive fuzzy segmentation of MRI data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the RF coils or some problems associated with the acquisition sequences. The result is a slowly-varying shading artifact over the image that can produce errors with conventional intensity-based classi cation. Our algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel (voxel) to be in uenced by the labels in its immediate neighborhood. The neighborhood e ect acts as a regularizer and biases the solution towards piecewise-homogeneous labelings. Such a regularization is useful in segmenting scans corrupted by salt and pepper noise. Experimental results on both synthetic images and MR data are given to demonstrate the e ectiveness and e ciency of the proposed algorithm.
IJERT-A Modified Adaptive Fuzzy C-Means Clustering Algorithm For Brain MR Image Segmentation
International Journal of Engineering Research and Technology (IJERT), 2012
https://www.ijert.org/a-modified-adaptive-fuzzy-c-means-clustering-algorithm-for-brain-mr-image-segmentation https://www.ijert.org/research/a-modified-adaptive-fuzzy-c-means-clustering-algorithm-for-brain-mr-image-segmentation-IJERTV1IS8668.pdf Fuzzy c-means (FCM) clustering has been widely used in image segmentation. However, in spite of its computational efficiency and wide spread popularity, the FCM algorithm does not take the spatial information of pixels into consideration, and hence may result in low robustness to noise and less accurate segmentation. In this paper, a modified adaptive fuzzy c-means clustering (AFCM) algorithm is presented for fuzzy segmentation of magnetic resonance (MR) images. To estimate the intensity inhomogeneity, the global intensity is introduced into the coherent local intensity clustering algorithm and takes the local and global intensity information into account. The proposed method has been successfully applied to recorded MR images with desirable results. Our results show that the proposed AFCM algorithm can effectively segment the test images and MR images. Comparisons with other FCM approaches based on number of iterations and time complexity demonstrate the superior performance of the proposed algorithm.
A Modified Fuzzy C-Means Clustering with Spatial Information for Image Segmentation
International Conference on Information …
A traditional approach to segmentation of magnetic resonance (MR) images is the Fuzzy C-Means (FCM) clustering algorithm. However, the conventionally standard FCM algorithm is sensitive to noise. To overcome the above problem, a modified FCM algorithm (called MS-FCM later) for MRI brain image segmentation is presented in this paper. The algorithm is realized by incorporating the spatial neighborhood information into the standard FCM algorithm and modifying the membership weighting of each cluster. In The proposed algorithm every point of the data set has a weight in relation to every cluster. Therefore this weight permits to have a better classification especially in the case of noise data. The proposed algorithm is applied to both artificial synthesized image and real image. Segmentation results demonstrate that the presented algorithm performs more robust to noise than the standard FCM algorithm.
A Modified Fuzzy C-Means Algorithm for MRI Bias Field Estimation and Adaptive Segmentation
Lecture Notes in Computer Science, 1999
In this paper, we present a novel algorithm for adaptive fuzzy segmentation of MRI data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the RF coils or some problems associated with the acquisition sequences. The result is a slowly-varying shading artifact over the image that can produce errors with conventional intensity-based classification. Our algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel (voxel) to be influenced by the labels in its immediate neighborhood. The neighborhood effect acts as a regularizer and biases the solution towards piecewisehomogeneous labeling. Such a regularization is useful in segmenting scans corrupted by salt and pepper noise. Experimental results on both synthetic images and MR data are given to demonstrate the effectiveness and efficiency of the proposed algorithm.
Improved Fuzzy C-Means for Brain Tissue Segmentation Using T1- Weighted MRI Head Scans
IJISET - International Journal of Innovative Science, Engineering & Technology, 2016
Brain tissue segmentation of Magnetic Resonance Imaging (MRI) is an important and one of the challenging tasks in medical image processing. MRI images of brain are classified into two types: classifying tissues, anatomical structures. It comprised into different tissue classes which contain four major regions, namely Gray matter (GM), White matter (WM), Cerebrospinal fluid (CSF), and Background (BG). The present study of proposed method is an improved fuzzy c-means (FCM) clustering for tissue segmentation using T1-weighted head scans. The proposed method improved by modifying the objective function, cluster center and membership value for updating criterion. The quantitative measures of results were compared using the metrics Dice Coefficient (DC) and processing time. The DC value of proposed method attained maximum value while compared to conventional FCM. The proposed method is very efficient and faster than FCM for brain tissue segmentation from T1-weighted head scans.
Journal of medical systems, 2017
The Fuzzy C Means (FCM) and Expectation Maximization (EM) algorithms are the most prevalent methods for automatic segmentation of MR brain images into three classes Gray Matter (GM), White Matter (WM) and Cerebrospinal Fluid (CSF). The major difficulties associated with these conventional methods for MR brain image segmentation are the Intensity Non-uniformity (INU) and noise. In this paper, EM and FCM with spatial information and bias correction are proposed to overcome these effects. The spatial information is incorporated by convolving the posterior probability during E-Step of the EM algorithm with mean filter. Also, a method of pixel re-labeling is included to improve the segmentation accuracy. The proposed method is validated by extensive experiments on both simulated and real brain images from standard database. Quantitative and qualitative results depict that the method is superior to the conventional methods by around 25% and over the state-of-the art method by 8%.
Fast and accurate fuzzy C-means algorithm for MR brain image segmentation
International Journal of Imaging Systems and Technology, 2016
Fuzzy theory based intelligent techniques are widely preferred for medical applications because of high accuracy. Among the fuzzy based techniques, Fuzzy C-Means (FCM) algorithm is popular than the other approaches due to the availability of expert knowledge. But, one of the hidden facts is that the computational complexity of the FCM algorithm is significantly high. Since medical applications need to be time effective, suitable modifications must be made in this algorithm for practical feasibility. In this study, necessary changes are included in the FCM approach to make the approach time effective without compromising the segmentation efficiency. An additional data reduction approach is performed in the conventional FCM to minimize the computational complexity and the convergence rate. A comparative analysis with the conventional FCM algorithm and the proposed Fast and Accurate FCM (FAFCM) is also given to show the superior nature of the proposed approach. These techniques are analyzed in terms of segmentation efficiency and convergence rate. Experimental results show promising results for the proposed approach.