Robust fuzzy c‐means clustering algorithm using non‐parametric Bayesian estimation in wavelet transform domain for noisy MR brain image segmentation

Chetih, Nabil; Messali, Zoubeida; Serir, Amina; Ramou, N.

doi:10.1049/iet-ipr.2017.0399

Cited by 28 publications

(19 citation statements)

References 44 publications

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“…The fuzzy level set algorithm employs FCM clustering with spatial restrictions to define the approximate contours of interest in an MR brain image. The new level set function is given by

ϕ_{0} ((), x, y) = - 4 ε ((), 0.5 - C_{k})

where ϕ 0 ( x , y ) is the initial contour and ε is a constant regulating the Dirac function.…”

Section: Methodsmentioning

confidence: 99%

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Brightness preserving optimized weighted bi‐histogram equalization algorithm and its application to MR brain image segmentation

Veluchamy

Mayathevar

Subramani

2019

Int J Imaging Syst Tech

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Medical image segmentation is crucial for neuroscience research and computer‐aided diagnosis. However, intensity inhomogeneity and existence of noise in magnetic resonance images lead to incorrect segmentation. In this article, an effective method called enhanced fuzzy level set algorithm is presented to segment the white matter, gray matter, and cerebrospinal fluid automatically in contrast‐enhanced brain images. In this method, first, exposure threshold is computed to divide the input histogram into two sub‐histograms of different gray levels. The input histogram is clipped using a mean gray level to control the excessive enhancement rate. Then, these two sub‐histograms are modified and equalized independently to get a better contrast enhanced image. Finally, an enhanced fuzzy level set algorithm is employed to facilitate image segmentation. The extensive experimental results proved the outstanding performance of the proposed algorithm compared with other existing methods. The results conform its effectiveness for MR brain image segmentation.

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“…The fuzzy level set algorithm employs FCM clustering with spatial restrictions to define the approximate contours of interest in an MR brain image. The new level set function is given by

ϕ_{0} ((), x, y) = - 4 ε ((), 0.5 - C_{k})

where ϕ 0 ( x , y ) is the initial contour and ε is a constant regulating the Dirac function.…”

Section: Methodsmentioning

confidence: 99%

“…The fuzzy level set algorithm employs FCM 26,27 clustering with spatial restrictions to define the approximate contours of interest in an MR brain image. The new level set function is given by…”

Section: Mr Brain Image Segmentationmentioning

confidence: 99%

Brightness preserving optimized weighted bi‐histogram equalization algorithm and its application to MR brain image segmentation

Veluchamy

Mayathevar

Subramani

2019

Int J Imaging Syst Tech

View full text Add to dashboard Cite

show abstract

“…Hence, it becomes an essential tool for neurologists to perform brain tumor diagnosis (Chetih et al. 2018 ). Nowadays, there is a rapid increase in brain tumor cases.…”

Section: Introductionmentioning

confidence: 99%

Three-class brain tumor classification using deep dense inception residual network

et al. 2021

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Three-class brain tumor classification becomes a contemporary research task due to the distinct characteristics of tumors. The existing proposals employ deep neural networks for the three-class classification. However, achieving high accuracy is still an endless challenge in brain image classification. We have proposed a deep dense inception residual network for three-class brain tumor classification. We have customized the output layer of Inception ResNet v2 with a deep dense network and a softmax layer. The deep dense network has improved the classification accuracy of the proposed model. The proposed model has been evaluated using key performance metrics on a publicly available brain tumor image dataset having 3064 images. Our proposed model outperforms the existing model with a mean accuracy of 99.69%. Further, similar performance has been obtained on noisy data.

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“…At present, there are many segmentation methods for brain MR image, such as threshold method [2], region method [3], random field method [4], neural network method [5] and clustering method [6][7][8][9]. As one of the main techniques of unsupervised machine learning, clustering method, especially fuzzy c-means (FCM) clustering method [10], has been widely used in brain MR image segmentation.…”

Section: Introductionmentioning

confidence: 99%

A Modified Robust FCM Model with Spatial Constraints for Brain MR Image Segmentation

Song

Zhang

2019

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In brain magnetic resonance (MR) images, image quality is often degraded due to the influence of noise and outliers, which brings some difficulties for doctors to segment and extract brain tissue accurately. In this paper, a modified robust fuzzy c-means (MRFCM) algorithm for brain MR image segmentation is proposed. According to the gray level information of the pixels in the local neighborhood, the deviation values of each adjacent pixel are calculated in kernel space based on their median value, and the normalized adaptive weighted measure of each pixel is obtained. Both impulse noise and Gaussian noise in the image can be effectively suppressed, and the detail and edge information of the brain MR image can be better preserved. At the same time, the gray histogram is used to replace single pixel during the clustering process. The results of segmentation of MRFCM are compared with the state-of-the-art algorithms based on fuzzy clustering, and the proposed algorithm has the stronger anti-noise property, better robustness to various noises and higher segmentation accuracy.

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Robust fuzzy c‐means clustering algorithm using non‐parametric Bayesian estimation in wavelet transform domain for noisy MR brain image segmentation

Cited by 28 publications

References 44 publications

Brightness preserving optimized weighted bi‐histogram equalization algorithm and its application to MR brain image segmentation

Brightness preserving optimized weighted bi‐histogram equalization algorithm and its application to MR brain image segmentation

Three-class brain tumor classification using deep dense inception residual network

A Modified Robust FCM Model with Spatial Constraints for Brain MR Image Segmentation

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