Non‐local‐based spatially constrained hierarchical fuzzy
            <i>C</i>
            ‐means method for brain magnetic resonance imaging segmentation

Chen, Yunjie; Li, Jian; Zhang, Hui; Zheng, Yuhui; Wu, Q. M. Jonathan

doi:10.1049/iet-ipr.2016.0271

Cited by 21 publications

(8 citation statements)

References 37 publications

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“…The hierarchy strategy used in the method increases the complexity of process. Robust-Learning FCM clustering algorithm was proposed by Miin-Shen Yang and Yessica Nataliani [2], in which the computational time is high due to more number of iterations. An automated segmentation of tumor region using optimization and clustering techniques for three (T1, T2 & Flair) image modality was developed by Vishnuvarthanan Govindaraj et al [3], requires improvement in segmentation accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Amalgamation of Clustering and Meta-heuristic Optimization Techniques for Automated MR Brain Analysis

2019

IJITEE

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Interest in computer-assisted image analysis in increasing among the radiologist as it provides them the additional information to take decision and also for better disease diagnosis. Traditionally, MR image is manually examined by medical practitioner through naked eye for the detection and diagnosis of tumor location, size, and intensity; these are difficult and not sufficient for accurate analysis and treatment. For this purpose, there is need for additional automated analysis system for accurate detection of normal and abnormal tumor region. This paper introduces the new semi-automated image processing method to identify the brain tumor region in Magnetic Resonance Image (MRI) using c means clustering technique along with meta-heuristic optimization, based on Jaya optimization algorithm. The resultant performance of the proposed algorithm (FCM +JA) is examined with the help of key analyzing parameters, MSE-Mean Square Error, PSNR-Peak Signal to Noise Ratio, DOI-Dice Overlap Index and CPU memory utilization. The experimental results of this method show better and enhanced tumor region display in reduced computation time.

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Section: Literature Reviewmentioning

confidence: 99%

Amalgamation of Clustering and Meta-heuristic Optimization Techniques for Automated MR Brain Analysis

2019

IJITEE

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“…According to the principle of entropy increase, maximum entropy seeks an optimal threshold to maximize the sum of the entropy of the background and the foreground [4]. Other segmentation methods also calculate the optimal segmentation threshold based on underlying features such as texture, hue, and shape [5][6][7][8][9][10][11][12][13][14][15][16][17]. These algorithms rely heavily on the color information of the image; therefore, before segmentation, the images are usually pre-processed to mitigate the illumination effect [18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

A Marker-Controlled Watershed Algorithm for the Intelligent Picking of Long Jujubes in Trees

Dai

Liu

Wang

et al. 2022

Forests

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Vision is the most important way for an unmanned picking or plant protection robot to navigate an external environment. To achieve intelligent picking or plant protection, it is essential to obtain target location information. A new marker-controlled watershed (MCW-D) algorithm is proposed for object segmentation. By analyzing the shortcomings of the watershed algorithm and the characteristics of objects, the proposed MCW-D method mainly solves three problems. First, it reduces the influence of shadow and other factors on image color information. Based on histogram specification, secondary mapping is used to reduce the effects of lighting. Second, marker images are selected. All points with markers need to be located in the target object. The hue feature of long jujubes and trees is used as the marker image. Third, a mask image is acquired, which requires a clear boundary between the target and the background. An adaptive angle rotation based on an energy-driven approach is designed to find large differences between the target and the background. In a natural environment, the proposed MCW-D method respectively achieves segmentation accuracies of 94.7% and 93.2% on a jujube dataset and a tree dataset, which exceed the accuracies of widely used machine learning methods. These results promote the development of the forest and fruit economies.

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“…FCM with spatial constraint and its variants greatly improved the antinoise performance compared with FCM, but when the noise is very serious in the image, the performance of the algorithm may be worse. Therefore, the nonlocal spatial information was often used and incorporated into the distance metric of FCM in recent years [ 13 – 16 ]. Zhao [ 14 ] brought in a nonlocal adaptive regularization term in its energy function, and the control factor is adaptive determined to adjust the balance of the objective function.…”

Section: Introductionmentioning

confidence: 99%

Brain Tissue Segmentation and Bias Field Correction of MR Image Based on Spatially Coherent FCM with Nonlocal Constraints

Song

Zhang

2019

Computational and Mathematical Methods in Medicine

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Influenced by poor radio frequency field uniformity and gradient-driven eddy currents, intensity inhomogeneity (or bias field) and noise appear in brain magnetic resonance (MR) image. However, some traditional fuzzy c-means clustering algorithms with local spatial constraints often cannot obtain satisfactory segmentation performance. Therefore, an objective function based on spatial coherence for brain MR image segmentation and intensity inhomogeneity correction simultaneously is constructed in this paper. First, a novel similarity measure including local neighboring information is designed to improve the separability of MR data in Gaussian kernel mapping space without image smoothing, and the similarity measure incorporates the spatial distance and grayscale difference between cluster centroid and its neighborhood pixels. Second, the objective function with an adaptive nonlocal spatial regularization term is drawn upon to compensate the drawback of the local spatial information. Meanwhile, bias field information is also embedded into the similarity measure of clustering algorithm. From the comparison between the proposed algorithm and the state-of-the-art methods, our model is more robust to noise in the brain magnetic resonance image, and the bias field is also effectively estimated.

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Non‐local‐based spatially constrained hierarchical fuzzy C ‐means method for brain magnetic resonance imaging segmentation

Cited by 21 publications

References 37 publications

Amalgamation of Clustering and Meta-heuristic Optimization Techniques for Automated MR Brain Analysis

Amalgamation of Clustering and Meta-heuristic Optimization Techniques for Automated MR Brain Analysis

A Marker-Controlled Watershed Algorithm for the Intelligent Picking of Long Jujubes in Trees

Brain Tissue Segmentation and Bias Field Correction of MR Image Based on Spatially Coherent FCM with Nonlocal Constraints

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