Improved fuzzy entropy clustering algorithm for MRI brain image segmentation

Verma, Hanuman; Agrawal, R. K.; Kumar, Naveen

doi:10.1002/ima.22104

Cited by 21 publications

(8 citation statements)

References 23 publications

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“…The resulting hybrid FCM algorithm, combined with particle swarm optimization during the centroid vector initialization step, segments the tissues and identifies the edema and tumor affected regions in the brain. However, Verma et al (2014) presented improved fuzzy entropy clustering (IFEC) algorithm to segment brain MR images, characterized by noisy data. A new fuzzy factor, which incorporates both local spatial and gray-level information, was introduced.…”

Section: Related Workmentioning

confidence: 99%

Gamma Knife treatment planning: MR brain tumor segmentation and volume measurement based on unsupervised Fuzzy C-Means clustering

Militello

Rundo

Vitabile

et al. 2015

Int. J. Imaging Syst. Technol.

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Nowadays, radiation treatment is beginning to intensively use MRI thanks to its greater ability to discriminate healthy and diseased soft-tissues. Leksell Gamma Knife® is a radio-surgical device, used to treat different brain lesions, which are often inaccessible for conventional surgery, such as benign or malignant tumors. Currently, the target to be treated with radiation therapy is contoured with slice-by-slice manual segmentation on MR datasets. This approach makes the segmentation procedure time consuming and operator-dependent. The repeatability of the tumor boundary delineation may be ensured only by using automatic or semiautomatic methods, supporting clinicians in the treatment planning phase. This article proposes a semiautomatic segmentation method, based on the unsupervised Fuzzy C-Means clustering algorithm. Our approach helps segment the target and automatically calculates the lesion volume. To evaluate the performance of the proposed approach, segmentation tests on 15 MR datasets were performed, using both area-based and distance-based metrics, obtaining the following average values: Similarity Index=95.59%, Jaccard Index=91.86%, Sensitivity=97.39%, Specificity=94.30%, Mean Absolute Distance=0.246[pixels], Maximum Distance=1.050[pixels], and Hausdorff Distance=1.365[pixels]

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Section: Related Workmentioning

confidence: 99%

Gamma Knife treatment planning: MR brain tumor segmentation and volume measurement based on unsupervised Fuzzy C-Means clustering

Militello

Rundo

Vitabile

et al. 2015

Int. J. Imaging Syst. Technol.

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“…As the employment of FCM enhances the performance of EWT technique for the mental task classification, it would be better to explore some other fuzzy-based clustering which has been explored in image segmentation [ 46 ]. It will also be interesting to explore whether the FEWT would work in other type of BCI such as motor imagery and multi-mental task classification.…”

Section: Discussionmentioning

confidence: 99%

Fuzzy clustering-based feature extraction method for mental task classification

Gupta

Kumar

2016

Brain Inf.

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A brain computer interface (BCI) is a communication system by which a person can send messages or requests for basic necessities without using peripheral nerves and muscles. Response to mental task-based BCI is one of the privileged areas of investigation. Electroencephalography (EEG) signals are used to represent the brain activities in the BCI domain. For any mental task classification model, the performance of the learning model depends on the extraction of features from EEG signal. In literature, wavelet transform and empirical mode decomposition are two popular feature extraction methods used to analyze a signal having non-linear and non-stationary property. By adopting the virtue of both techniques, a theoretical adaptive filter-based method to decompose non-linear and non-stationary signal has been proposed known as empirical wavelet transform (EWT) in recent past. EWT does not work well for the signals having overlapped in frequency and time domain and failed to provide good features for further classification. In this work, Fuzzy c-means algorithm is utilized along with EWT to handle this problem. It has been observed from the experimental results that EWT along with fuzzy clustering outperforms in comparison to EWT for the EEG-based response to mental task problem. Further, in case of mental task classification, the ratio of samples to features is very small. To handle the problem of small ratio of samples to features, in this paper, we have also utilized three well-known multivariate feature selection methods viz. Bhattacharyya distance (BD), ratio of scatter matrices (SR), and linear regression (LR). The results of experiment demonstrate that the performance of mental task classification has improved considerably by aforesaid methods. Ranking method and Friedman’s statistical test are also performed to rank and compare different combinations of feature extraction methods and feature selection methods which endorse the efficacy of the proposed approach.

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“…All the aforementioned algorithms make the Shannon entropy as the entropy regularization term in the objective function to achieve the optimal effect of the FCM. However, the usage of the Shannon entropy is significantly limited, given its inability to consider the influence of cluster size and cluster correlation on segmentation results [24]. When the cluster scale is largely different (clusters with large pixel values coexisting with other clusters with small number of pixels), the algorithm can easily segment the edge pixels of the larger-scale cluster into the smaller-scale cluster.…”

Section: Introductionmentioning

confidence: 99%

Multispectral Image Segmentation Based on a Fuzzy Clustering Algorithm Combined with Tsallis Entropy and a Gaussian Mixture Model

Chen

et al. 2019

Remote Sensing

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Accurate multispectral image segmentation is essential in remote sensing research. Traditional fuzzy clustering algorithms used to segment multispectral images have several disadvantages, including: (1) they usually only consider the pixels’ grayscale information and ignore the interaction between pixels; and, (2) they are sensitive to noise and outliers. To overcome these constraints, this study proposes a multispectral image segmentation algorithm based on fuzzy clustering combined with the Tsallis entropy and Gaussian mixture model. The algorithm uses the fuzzy Tsallis entropy as regularization item for fuzzy C-means (FCM) and improves dissimilarity measure using the negative logarithm of the Gaussian Mixture Model (GMM). The Hidden Markov Random Field (HMRF) is introduced to define prior probability of neighborhood relationship, which is used as weights of the Gaussian components. The Lagrange multiplier method is used to solve the segmentation model. To evaluate the proposed segmentation algorithm, simulated and real multispectral images were segmented using the proposed algorithm and two other algorithms for comparison (i.e., Tsallis Fuzzy C-means (TFCM), Kullback–Leibler Gaussian Fuzzy C-means (KLG-FCM)). The study found that the modified algorithm can accelerate the convergence speed, reduce the effect of noise and outliers, and accurately segment simulated images with small gray level differences with an overall accuracy of more than 98.2%. Therefore, the algorithm can be used as a feasible and effective alternative in multispectral image segmentation, particularly for those with small color differences.

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Improved fuzzy entropy clustering algorithm for MRI brain image segmentation

Cited by 21 publications

References 23 publications

Gamma Knife treatment planning: MR brain tumor segmentation and volume measurement based on unsupervised Fuzzy C-Means clustering

Gamma Knife treatment planning: MR brain tumor segmentation and volume measurement based on unsupervised Fuzzy C-Means clustering

Fuzzy clustering-based feature extraction method for mental task classification

Multispectral Image Segmentation Based on a Fuzzy Clustering Algorithm Combined with Tsallis Entropy and a Gaussian Mixture Model

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