Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images

Adhikari, Sudip Kumar; Sing, Jamuna Kanta; Basu, Dipak Kumar; Nasipuri, Mita

doi:10.1016/j.asoc.2015.05.038

Cited by 156 publications

(76 citation statements)

References 29 publications

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“…A method of using Soft Computing Approaches to compute the cluster centers of FCM segmentation is proposed (Agrawal et al, ). A weighted membership function which incorporates local and global spatial information is introduced to overcome the effects of sensitivity to noise and intensity inhomogeneity is proposed (Adhikari et al, ). The Euclidean distance in the objective function of FCM is replaced with neighborhood‐weighted distance to improve robustness against noise and artifacts (Zaixin et al, ).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy C means integrated with spatial information and contrast enhancement for segmentation of MR brain images

Prakash

Kumari

2016

Int. J. Imaging Syst. Technol.

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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 T1-weighted 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.

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Section: Introductionmentioning

confidence: 99%

Fuzzy C means integrated with spatial information and contrast enhancement for segmentation of MR brain images

Prakash

Kumari

2016

Int. J. Imaging Syst. Technol.

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“…The K - means (KM) and fuzzy clustering algorithms, e.g., fuzzy K - means (FKM), have been used in a wide range of scenarios and applications, such as: digital soil pattern recognition [32], archaeology [33], indoor localization [34], discrimination of cabernet sauvignon grapevine elements [35], white blood cell segmentation [36], abnormal lung sounds diagnosis [37], intelligent sensor networks in agriculture [38], magnetic resonance image (MRI) segmentation [39,40], speaker recognition [41] and image compression by VQ [29,42,43]. …”

Section: Introductionmentioning

confidence: 99%

Accelerating Families of Fuzzy K-Means Algorithms for Vector Quantization Codebook Design

Mata

Bandeira

Neto

et al. 2016

Sensors

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The performance of signal processing systems based on vector quantization depends on codebook design. In the image compression scenario, the quality of the reconstructed images depends on the codebooks used. In this paper, alternatives are proposed for accelerating families of fuzzy K-means algorithms for codebook design. The acceleration is obtained by reducing the number of iterations of the algorithms and applying efficient nearest neighbor search techniques. Simulation results concerning image vector quantization have shown that the acceleration obtained so far does not decrease the quality of the reconstructed images. Codebook design time savings up to about 40% are obtained by the accelerated versions with respect to the original versions of the algorithms.

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“…This makes it a robust tool for noisy image segmentation. The knowledge that neighboring pixels in images highly correlate with same feature data makes the spatial relationship of neighboring pixels an interesting idea for noise elimination [30]. When the spatial neighborhood is incorporated, the membership weighting of each cluster is altered by the cluster distribution in the neighborhood.…”

Section: Fcm Algorithmmentioning

confidence: 99%

Brain MR Segmentation using a Fusion of K-Means and Spatial Fuzzy C-Means

Oluwasanmi¹,

Qin²,

Lan³

2018

dtcse

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This study presents an automatic segmentation of the brain tissues in Magnetic Resonance Image using a fusion of Spatial Fuzzy C-Means (sFCM) and K-Means Algorithms (sFCMKA). The segmentation of the standard FCM algorithm have been realized to be highly sensitive to noise and therefore fail in providing accurate results. The K-Means algorithm while having the advantage of simplified clustering results is disadvantaged for merely classifying pixels into just one cluster segment. In overcoming this problem, first, FCM algorithm incorporated with a spatial neighborhood function is presented to eliminate the effect of noise. Also, the K-Means algorithm is embedded to sort pixels in tissues which are not intertwined with another. Since they are nonoverlapping tissues, such pixels are grouped in a singular cluster without having membership to another cluster segment. sFCMKA achieves this by placing a threshold parameter on pixels having contrasting cluster result from both algorithms and then reprocessed. The efficiency of the proposed algorithm is demonstrated by evaluating the results with the input image's manual segmentation using the Jaccard similarity index, Dice coefficient and accuracy index. The results of the proposed algorithm depicts a far more admirable accuracy quality compared to existing clustering based segmentation methods.

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Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images

Cited by 156 publications

References 29 publications

Fuzzy C means integrated with spatial information and contrast enhancement for segmentation of MR brain images

Fuzzy C means integrated with spatial information and contrast enhancement for segmentation of MR brain images

Accelerating Families of Fuzzy K-Means Algorithms for Vector Quantization Codebook Design

Brain MR Segmentation using a Fusion of K-Means and Spatial Fuzzy C-Means

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