A new segmentation method of cerebral MRI images based on the fuzzy c-means algorithm

Abderrezak, Mohamed Zaki; Chibane, Mouatez Billah; Mansour, Karim

doi:10.3906/elk-1510-37

Cited by 3 publications

(4 citation statements)

References 30 publications

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“…With this exponent operation of the Euclidean distance in its membership function, ORFCM showed its capability to neutralize the outlier's effect and construct better clusters than the original FCM. There exist several recent results in medical domain using this outlier rejection concept in fuzzy learning process successfully [38][39][40].…”

Section: Appendix Segmentation With Double-layered Outlier Rejection ...mentioning

confidence: 99%

Robust Automatic Segmentation of Inflamed Appendix from Ultrasonography with Double-Layered Outlier Rejection Fuzzy C-Means Clustering

2022

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Accurate diagnosis of acute appendicitis from abdominal ultrasound is a challenging task, since traditional sonographic diagnostic criteria for appendicitis, such as diameter, compressibility, and wall thickness, rely on complete identification or visualization of the appendix and the diagnosis is frequently operator subjective. In this paper, we propose a robust automatic segmentation method for inflamed appendix identification to mitigate abovementioned difficulties. We use outlier rejection fuzzy c-means clustering (FCM) algorithm within a double-layered learning structure to extract the target inflamed appendix area. The proposed method extracts the target appendix in 98 cases out of 100 test images, which is far better than traditional FCM, standard outlier FCM, and double-layered learning with FCM in correct extraction rate. Furthermore, we investigate the outlier rejection effect and double layered learning effect by comparing our proposed method with standard double-layered FCM and the standard outlier-rejection FCM. In this comparison, the proposed method exhibits robust segmentation results in accuracy, precision, and recall by 2.5~5.6% over two standard methods in quality with human pathologists’ marking as the ground truth.

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Section: Appendix Segmentation With Double-layered Outlier Rejection ...mentioning

confidence: 99%

Robust Automatic Segmentation of Inflamed Appendix from Ultrasonography with Double-Layered Outlier Rejection Fuzzy C-Means Clustering

2022

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“…Just as shown in the Figure 1, a neuron of PCNN could be divided into three parts: input field, modulation field and pulse generator. The definition of PCNN mathematical model could be described as (1)(2)(3)(4)(5):…”

Section: A Basic Pcnn Modelmentioning

confidence: 99%

“…where all the notations have the same meanings as indicated in (1)(2)(3)(4)(5). Compared with the SCM, the firing condition of SPCNN is U ij (n) > θ ij (n) rather than the sigmoid function in SCM.…”

Section: B Simplified Pcnnmentioning

confidence: 99%

“…The quality of image segmentation directly affects the image processing later that is considered as the central task in many research fields. So numerous segmentation algorithms have been proposed in recent decades that includes threshold based algorithm [1], region based algorithm [2], edge based algorithm [3], fuzzy theory based [4], neural network based algorithm [5].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Method for Image Segmentation Based on Simplified Pulse Coupled Neural Network and Gbest Led Gravitational Search Algorithm

Jiao

Zhang

2019

IEEE Access

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This paper proposed a novel image segmentation method based on simplified pulse coupled neural network (SPCNN), which was optimized by gbest led gravitational search algorithm (GLGSA) that combined gravitational search algorithm (GSA) with gbest agent memory ability. To evaluate the performance of GLGSA, we applied it to 23 standard benchmark functions and compared with GSA and GGSA. The results showed that the GLGSA had better performance in term of convergence and avoidance of local minima. Besides, in order to improve the accuracy of segmentation, the fitness function consisted of cross entropy parameter, edge matching, and noise control. To verify the efficiency of our method, we compared it with the state-of-the-art algorithms, such as Otsu, GA Renyi, and PSO-PCNN, using the gray nature images from the Berkeley segmentation dataset. Finally, the subjective visual analysis and quantitative analysis that included the uniformity measure, region contrast measure, structural similarity, and comprehensive evaluation were used to evaluate the segmented images. The comparison results demonstrated that our proposed method could get better segmentation results. INDEX TERMS Image segmentation, gravitational search algorithm, pulse coupled neural network, edge matching, noise control.

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VNS Metaheuristic Based on Thresholding Functions for Brain MRI Segmentation

Miledi

Dhouib

2021

International Journal of Applied Metaheuristic Computing

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Image segmentation is a very crucial step in medical image analysis which is the first and the most important task in many clinical interventions. The authors propose in this paper to apply the variable neighborhood search (VNS) metaheuristic on the problem of brain magnetic resonance images (MRI) segmentation. In fact, by reviewing the literature, they notice that when the number of classes increases the computational time of the exhaustive methods grows exponentially with the number of required classes. That's why they exploit the VNS algorithm to optimize two maximizing thresholding functions which are the between-class variance (the Otsu's function) and the entropy thresholding (the Kapur's function). Thus, two versions of the VNS metaheuristic are respectively obtained: the VNS-Otsu and the VNS-Kapur. These two novel proposed thresholding methods are tested on a set of benchmark brain MRI to show their robustness and proficiency.

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A new segmentation method of cerebral MRI images based on the fuzzy c-means algorithm

Cited by 3 publications

References 30 publications

Robust Automatic Segmentation of Inflamed Appendix from Ultrasonography with Double-Layered Outlier Rejection Fuzzy C-Means Clustering

Robust Automatic Segmentation of Inflamed Appendix from Ultrasonography with Double-Layered Outlier Rejection Fuzzy C-Means Clustering

A Novel Method for Image Segmentation Based on Simplified Pulse Coupled Neural Network and Gbest Led Gravitational Search Algorithm

VNS Metaheuristic Based on Thresholding Functions for Brain MRI Segmentation

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