Generalised rough intuitionistic fuzzy c‐means for magnetic resonance brain image segmentation

Namburu, Anupama; Kumar, S. Srinivas; Edara, Srinivasa Reddy

doi:10.1049/iet-ipr.2016.0891

Cited by 46 publications

(21 citation statements)

References 32 publications

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“…It can analyze and infer data to discover hidden knowledge and reveal potential laws. The rough set is a promising method, which is widely used in image processing [34,23,21]. Therefore, the authors construct fractional order adaptive function by using rough set theory.…”

Section: Xuefeng Zhang and Hui Yanmentioning

confidence: 99%

Image enhancement algorithm using adaptive fractional differential mask technique

Zhang¹,

Hui²

2019

Mathematical Foundations of Computing

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This paper addresses a novel adaptive fractional order image enhancement method. Firstly, an image segmentation algorithm is proposed, it combines Otsu algorithm and rough entropy to segment image accurately into the objet and the background. On the basis of image segmentation and the knowledge of fractional order differential, an image enhancement model is established. The rough characteristics of each average gray value are obtained by image segmentation method, through these features, we can determine the optimal fractional order of image enhancement. Then image will be enhanced using fractional order differential mask, from which fractional order is obtained adaptively. Several images are used for experiments, the proposed model is compared with other models, and the results of comparison exhibit the superiority of our algorithm in terms of image quality measures.

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Section: Xuefeng Zhang and Hui Yanmentioning

confidence: 99%

Image enhancement algorithm using adaptive fractional differential mask technique

Zhang¹,

Hui²

2019

Mathematical Foundations of Computing

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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

Information

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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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“…Medical image processing technology plays an increasingly important role in breaking the era of medicine [4]. Medical image processing technology plays an important role in teaching, scientific research and clinical diagnosis and treatment [5]. The combination of computer image processing technology and image technology has fundamentally changed the traditional diagnostic methods of medical staff.…”

Section: Introductionmentioning

confidence: 99%

“…After years of research, various segmentation methods have been proposed and applied to the field of medical image processing. Image segmentation is a process of dividing an image into several regions according to the similarity in the image region and the difference between the image regions [5]. Image segmentation is defined as: a pair of images is regarded as a large area, which is represented by geometricS.…”

Section: A the Introduction Of Image Segmentationmentioning

confidence: 99%

Application of Partial Differential Equation in Brain Image Segmentation and Registration

Tian¹,

Zhang²

2019

IEEE Access

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Due to the problem that local binary fitting model is easy to fall into local extremum, based on the hypothesis that the brightness of the image on both sides of the target boundary has contrast, an improved method of adding contrast constraints to the fitting function is proposed in this paper. The contrast-constrained model can effectively overcome the problem that energy is easily trapped in local extremum, but there is still a problem that requires the initialization curve to be close to the edge of the target. Therefore, balloon force is added to the contrast-constrained model, which enlarges the scope of external force and enhances the robustness of the model to initialization. Meanwhile, the diffusion filtering method with adaptive image structure is introduced into image registration, and the anisotropic diffusion function with the ability of feature preservation and consistency enhancement is defined as the regularization term of the model. The improved model can effectively maintain image features and achieve accurate registration of complex images. In this paper, brain image (MRI) in Mcgill database is taken as an example to verify the simulation results. The simulation results verify the reliability and validity of the partial differential equation-based algorithm. INDEX TERMS Brain image segmentation, partial differential equation (PDE), LBF model, diffusion filtering method, image registration.

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Generalised rough intuitionistic fuzzy c‐means for magnetic resonance brain image segmentation

Cited by 46 publications

References 32 publications

Image enhancement algorithm using adaptive fractional differential mask technique

Image enhancement algorithm using adaptive fractional differential mask technique

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

Application of Partial Differential Equation in Brain Image Segmentation and Registration

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