Improved fuzzy clustering for image segmentation based on a low-rank prior

AbstractImage segmentation is a basic problem in medical image analysis and useful for disease diagnosis. However, the complexity of medical images makes image segmentation difficult. In recent decades, fuzzy clustering algorithms have been preferred due to their simplicity and efficiency. However, they are sensitive to noise. To solve this problem, many algorithms using non-local information have been proposed, which perform well but are inefficient. This paper proposes an improved fuzzy clustering algorithm utilizing nonlocal self-similarity and a low-rank prior for image segmentation. Firstly, cluster centers are initialized based on peak detection. Then, a pixel correlation model between corresponding pixels is constructed, and similar pixel sets are retrieved. To improve efficiency and robustness, the proposed algorithm uses a novel objective function combining non-local information and a low-rank prior. Experiments on synthetic images and medical images illustrate that the algorithm can improve efficiency greatly while achieving satisfactory results.

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An Automatic Magnetic Resonance Brain Image Segmentation Method Using a Multitask Weighted Fuzzy Clustering Algorithm

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2021.3705 ◽

2021 ◽

Vol 11 (8) ◽

pp. 2177-2183

Author(s):

Lei Hua ◽

Jing Xue ◽

Kaijian Xia ◽

Leyuan Zhou ◽

Pengjiang Qian ◽

...

Keyword(s):

Image Segmentation ◽

Magnetic Resonance ◽

Fuzzy Clustering ◽

Clustering Algorithm ◽

Medical Images ◽

Medical Image Analysis ◽

Brain Images ◽

Brain Image ◽

Fuzzy Clustering Algorithm ◽

Brain Image Segmentation

In clinical assisted diagnosis, it is an important way to obtain information with the help of medical images. Qualitative and quantitative analysis of brain tissue has become a research hotspot for brain diseases. Therefore, image segmentation technology is an indispensable link in medical image analysis. Due to the defects such as ambiguity, complexity, gray-scale unevenness, partial volume effect in magnetic resonance brain images, it is essential to improve the segmentation performance of classical algorithms in medical images. In this paper, multitasking and weighted fuzzy clustering algorithm are combined as a new algorithm (MT-WFCM) for MRI brain image segmentation. The proposed MT-WFCM algorithm improve the clustering performance of all tasks through common information between different magnetic resonance brain images with correlation. Besides, the difference between MT-WFCM and MT-FCM is that task weights are added to avoid negative effects between tasks in the segmentation process. According to five different comparative experiments, the MT-WFCM algorithm can mine the cooperative relationship among each task and the characteristics of each task effectively. In magnetic resonance image (MRI) segmentation, multi-task weighted fuzzy c-means clustering method can make up for the shortcomings of single-task clustering algorithm, strengthen the relationship between tasks, and get more accurate segmentation results.

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Improved fuzzy clustering algorithm with non-local information for image segmentation

Multimedia Tools and Applications ◽

10.1007/s11042-016-3399-x ◽

2016 ◽

Vol 76 (6) ◽

pp. 7869-7895 ◽

Cited By ~ 21

Author(s):

Xiaofeng Zhang ◽

Yujuan Sun ◽

Gang Wang ◽

Qiang Guo ◽

Caiming Zhang ◽

...

Keyword(s):

Image Segmentation ◽

Fuzzy Clustering ◽

Clustering Algorithm ◽

Local Information ◽

Fuzzy Clustering Algorithm ◽

Non Local

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A novel self-learning weighted fuzzy local information clustering algorithm integrating local and non-local spatial information for noise image segmentation

Applied Intelligence ◽

10.1007/s10489-021-02722-7 ◽

2021 ◽

Author(s):

Qiuyu Song ◽

Chengmao Wu ◽

Xiaoping Tian ◽

Yue Song ◽

Xiaokang Guo

Keyword(s):

Image Segmentation ◽

Fuzzy Clustering ◽

Clustering Algorithm ◽

Spatial Information ◽

Local Information ◽

Cluster Center ◽

Fuzzy Clustering Algorithm ◽

Robust Segmentation ◽

Non Local ◽

Self Learning

AbstractFuzzy clustering algorithm (FCM) can be directly used to segment images, it takes no account of the neighborhood information of the current pixel and does not have a robust segmentation noise suppression. Fuzzy Local Information C-means Clustering (FLICM) is a widely used robust segmentation algorithm, which combines spatial information with the membership degree of adjacent pixels. In order to further improve the robustness of FLICM algorithm, non-local information is embedded into FLICM algorithm and a fuzzy C-means clustering algorithm has local and non-local information (FLICMLNLI) is obtained. When calculating distance from pixel to cluster center, FLICMLNLI algorithm considers two distances from current pixel and its neighborhood pixels to cluster center. However, the algorithm gives the same weight to two different distances, which incorrectly magnifies the importance of neighborhood information in calculating the distance, resulting in unsatisfactory image segmentation effects and loss of image details. In order to solve this problem, we raise an improved self-learning weighted fuzzy algorithm, which directly obtains different weights in distance calculation through continuous iterative self-learning, then the distance metric with the weights obtained from self-learning is embedded in the objective function of the fuzzy clustering algorithm in order to improve the segmentation performance and robustness of the algorithm. A large number of experiments on different types of images show that the algorithm can not only suppress the noise but also retain the details in the image, the effect of segmenting complex noise images is better, and it provides better image segmentation results than the existing latest fuzzy clustering algorithms.

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Kernel-Based Fuzzy Local Information Clustering Algorithm Self-integrating Non-Local Information

10.21203/rs.3.rs-585259/v1 ◽

2021 ◽

Author(s):

Qiuyu Song ◽

Chengmao Wu ◽

Xiaoping Tian ◽

Yue Song ◽

Xiaokang Guo

Keyword(s):

Fuzzy Clustering ◽

Clustering Algorithm ◽

Integration Method ◽

Clustering Algorithms ◽

Noise Removal ◽

Local Information ◽

Gaussian Kernel ◽

Distance Metric ◽

High Noise ◽

Non Local

Abstract The application of fuzzy clustering algorithms in image segmentation is a hot research topic nowadays. Existing fuzzy clustering algorithms have the following three problems: (1)The parameters of spatial information constraints can$'$t be selected adaptively; (2)The image corrupted by high noise can$'$t be segmented effectively; (3)It is difficult to achieve a balance between noise removal and detail preservation. In the fuzzy clustering based on the optimization model, the choice of distance metric is very important. Since the use of Euclidean distance will lead to sensitivity to outliers and noise, it is difficult to obtain satisfactory segmentation results, which will affect the clustering performance. This paper proposes an optimization algorithm based on the kernel-based fuzzy local information clustering integrating non-local information (KFLNLI). The algorithm adopts a self-integration method to introduce local and non-local information of images, which solves the common problems of current clustering algorithm. Firstly, the self-integration method solves the problem of selecting spatial constraint parameters. The algorithm uses continuous self-learning iteration to calculate the weight coefficients; Secondly, the distance metric uses Gaussian kernel function to induce the distance to further enhance the robustness against noise and the adaptivity of processing different images; Finally, both local and non-local information are introduced to achieve a segmentation effect that can eliminate most of the noise and retain the original details of the image. Experimental results show that the algorithm is superior to existing state-of-the-art fuzzy clustering-related algorithm in the presence of high noise.

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