Novel Method of MRI Medical Image Segmentation Combining Watershed Algorithm and WKFCM Algorithm

2011 ◽  
Vol 121-126 ◽  
pp. 4518-4522
Author(s):  
Jin Qing Liu ◽  
Kun Chen
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Brain Mri ◽  
Feature Space ◽  
Medical Image Segmentation ◽  
Watershed Algorithm ◽  
Kernel Clustering ◽  
Mercer Kernel ◽  
Novel Method ◽  
Segmentation Problem

Aimed at the disadvantage of over-segmentation that traditional watershed algorithm segmented MRI images, a new method of MRI image segmentation was presented. First, through traditional watershed segmentation algorithm, the image was segmented into different areas, and then based on the improved kernel-clustering algorithm, we used Mercer-kernel to map average gray value of each area to high-dimensional feature space, making originally not displayed features manifested. In this way, we can achieve a more accurate clustering, and solve over-segmentation problem of watershed algorithm segmenting MRI images efficiently, thereby get better segmentation result. Experimental results show that the method of this paper can segment brain MRI images satisfactorily, and obtain clearer segmentation images.

Spatial Information Based on the Multi-Objective Programming of Fuzzy Kernel Clustering Image Segmentation Algorithm

2013 ◽  
Vol 791-793 ◽  
pp. 1337-1340
Author(s):  
Xue Zhang Zhao ◽  
Ming Qi ◽  
Yong Yi Feng
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Spatial Information ◽  
Segmentation Algorithm ◽  
Multi Objective ◽  
Multi Objective Programming ◽  
Kernel Clustering ◽  
Noise Image ◽  
The Relationship ◽  
Image Segmentation Algorithm

Fuzzy kernel clustering algorithm is a combination of unsupervised clustering and fuzzy set of the concept of image segmentation techniques, But the algorithm is sensitive to initial value, to a large extent dependent on the initial clustering center of choice, and easy to converge to local minimum values, when used in image segmentation, membership of the calculation only consider the current pixel values in the image, and did not consider the relationship between neighborhood pixels, and so on segmentation contains noise image is not ideal. This paper puts forward an improved fuzzy kernel clustering image segmentation algorithm, the multi-objective problem, change the single objective problem to increase the secondary goals concerning membership functions, Then add the constraint information space; Finally, using spatial neighborhood pixels corrected membership degree of the current pixel. The experimental results show that the algorithm effectively avoids the algorithm converges to local extremism and the stagnation of the iterative process will appear problem, significantly lower iterative times, and has good robustness and adaptability.

Image Segmentation Algorithm Combined with Regularized P-M De-Noising Model and Improved Watershed Algorithm

2020 ◽  
Vol 10 (2) ◽  
pp. 515-521 ◽  
Author(s):  
Guorui Chen
Keyword(s):  
Image Segmentation ◽  
Brain Mri ◽  
Small Region ◽  
Watershed Algorithm ◽  
Segmentation Method ◽  
Segmentation Algorithms ◽  
Segmentation Image ◽  
The Brain ◽  
Image Segmentation Algorithm ◽  
Mri Image

Aiming at the problems of noise sensitivity and unclear contour in existing MRI image segmentation algorithms, a segmentation method combining regularized P-M de-noising model and improved watershed algorithm is proposed. First, the brain MRI image is pre-processed to obtain a brain nuclear image. Then, the brain nuclear image is de-noised by a regularized P-M model. After that, the image is preliminarily segmented by the traditional watershed algorithm to extract the features of each small region. Finally, the small regions are merged by Fuzzy Clustering with Spatial Pattern (FCSP) to obtain the segmentation image with smooth edges. The experimental results show that the algorithm can accurately segment the gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) regions. The average AOM and ME of the segmentation results on the BrainWeb dataset reached 0.93 and 0.04, respectively.

A Kind of Watershed Image Segmentation Method Based on Combination Pre-Processing

2010 ◽  
Vol 121-122 ◽  
pp. 320-324
Author(s):  
Jin Xi Wang ◽  
Lin Xiang Liu ◽  
Xiu Zheng Li
Keyword(s):  
Image Segmentation ◽  
Segmentation Algorithm ◽  
Watershed Algorithm ◽  
Segmentation Method ◽  
Simple Operation ◽  
Processing Steps ◽  
Segmentation Problem ◽  
Image Segmentation Algorithm

The watershed algorithm has been widely used in image segmentation for its characteristics of accurately positioning edge, simple operation and etc. But it also has drawbacks of easy to over-segmentation and loss important outline for the character of sensitive to noise. Aiming at the problem of over-segmentation of watershed algorithm, the paper brought out an improved image segmentation algorithm based on watershed, which can limit the number of existing regions that are allowed with combination pre-processing steps, so that the over-segmentation problem can be better solved. The result of experiment also verifies the correctness and feasibility of the proposed algorithm in the paper.

scholarly journals A Novel Brain MRI Image Segmentation Method Using an Improved Multi-View Fuzzy c-Means Clustering Algorithm

2021 ◽  
Vol 15 ◽  
Author(s):  
Lei Hua ◽  
Yi Gu ◽  
Xiaoqing Gu ◽  
Jing Xue ◽  
Tongguang Ni
Keyword(s):  
Image Segmentation ◽  
Brain Tissue ◽  
Adaptive Learning ◽  
Clustering Algorithm ◽  
Brain Mri ◽  
Segmentation Method ◽  
Learning Mechanism ◽  
Fcm Algorithm ◽  
The Brain ◽  
Mri Image

Background: The brain magnetic resonance imaging (MRI) image segmentation method mainly refers to the division of brain tissue, which can be divided into tissue parts such as white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The segmentation results can provide a basis for medical image registration, 3D reconstruction, and visualization. Generally, MRI images have defects such as partial volume effects, uneven grayscale, and noise. Therefore, in practical applications, the segmentation of brain MRI images has difficulty obtaining high accuracy.Materials and Methods: The fuzzy clustering algorithm establishes the expression of the uncertainty of the sample category and can describe the ambiguity brought by the partial volume effect to the brain MRI image, so it is very suitable for brain MRI image segmentation (B-MRI-IS). The classic fuzzy c-means (FCM) algorithm is extremely sensitive to noise and offset fields. If the algorithm is used directly to segment the brain MRI image, the ideal segmentation result cannot be obtained. Accordingly, considering the defects of MRI medical images, this study uses an improved multiview FCM clustering algorithm (IMV-FCM) to improve the algorithm’s segmentation accuracy of brain images. IMV-FCM uses a view weight adaptive learning mechanism so that each view obtains the optimal weight according to its cluster contribution. The final division result is obtained through the view ensemble method. Under the view weight adaptive learning mechanism, the coordination between various views is more flexible, and each view can be adaptively learned to achieve better clustering effects.Results: The segmentation results of a large number of brain MRI images show that IMV-FCM has better segmentation performance and can accurately segment brain tissue. Compared with several related clustering algorithms, the IMV-FCM algorithm has better adaptability and better clustering performance.

Two-Step Modified Nash Equilibrium Method for Medical Image Segmentation Based on an Improved C-V Model

2018 ◽  
Vol 8 (9) ◽  
pp. 1826-1834
Author(s):  
Tian Chi Zhang ◽  
Jian Pei Zhang ◽  
Jing Zhang ◽  
Melvyn L. Smith
Keyword(s):  
Image Segmentation ◽  
Nash Equilibrium ◽  
Level Set ◽  
Medical Images ◽  
Medical Image Segmentation ◽  
Clustering Problem ◽  
Object Contour ◽  
Segmentation Methods ◽  
Maximum Similarity ◽  
Segmentation Problem

One of the most established region-based segmentation methods is the region based C-V model. This method formulates the image segmentation problem as a level set or improved level set clustering problem. However, the existing level set C-V model fails to perform well in the presence of noisy and incomplete data or when there is similarity between the objects and background, especially for clustering or segmentation tasks in medical images where objects appear vague and poorly contrasted in greyscale. In this paper, we modify the level set C-V model using a two-step modified Nash equilibrium approach. Firstly, a standard deviation using an entropy payoff approach is employed and secondly a two-step similarity clustering based approach is applied to the modified Nash equilibrium. One represents a maximum similarity within the clustered regions and the other the minimum similarity between the clusters. Finally, an improved C-V model based on a two-step modified Nash equilibrium is proposed to smooth the object contour during the image segmentation. Experiments demonstrate that the proposed method has good performance for segmenting noisy and poorly contrasting regions within medical images.

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