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.

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.

Optimized Fuzzy Clustering Algorithms for Brain MRI Image Segmentation Based on Local Gaussian Probability and Anisotropic Weight Models

2018 ◽  
Vol 32 (09) ◽  
pp. 1857005 ◽  
Author(s):  
Ting Zhang
Keyword(s):  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Clinical Medicine ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Segmentation Algorithm ◽  
Fuzzy C Means ◽  
Anisotropic Weight ◽  
Mri Image

Brain Magnetic Resonance Imaging (MRI) image segmentation is one of the critical technologies of clinical medicine, and is the basis of three-dimensional reconstruction and downstream analysis between normal tissues and diseased tissues. However, there are various limitations in brain MRI images, such as gray irregularities, noise, and low contrast, reducing the accuracy of the brain MRI images segmentation. In this paper, we propose two optimization solutions for the fuzzy clustering algorithm based on local Gaussian probability fuzzy C-means (LGP-FCM) model and anisotropic weight fuzzy C-means (AW-FCM) model and apply it in brain MRI image segmentation. An FCM clustering algorithm is proposed based on AW-FCM. By introducing the new neighborhood weight calculation method, each point has the weight of anisotropy, effectively overcomes the influence of noise on the image segmentation. In addition, the LGP model is introduced in the objective function of fuzzy clustering, and a fuzzy clustering segmentation algorithm based on LGP-FCM is proposed. A clustering segmentation algorithm of adaptive scale fuzzy LGP model is proposed. The neighborhood scale corresponding to each pixel in the image is automatically estimated, which improves the robustness of the model and achieves the purpose of precise segmentation. Extensive experimental results demonstrate that the proposed LGP-FCM algorithm outperforms comparison algorithms in terms of sensitivity, specificity and accuracy. LGP-FCM can effectively segment the target regions from brain MRI images.

White Patch Detection in Brain MRI Image Using Evolutionary Clustering Algorithm

2016 ◽  
pp. 323-339
Author(s):  
Pradeep Kumar Mallick ◽  
Mihir Narayan Mohanty ◽  
S. Saravana Kumar
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Brain Mri ◽  
Medical Diagnostics ◽  
Vital Role ◽  
Evolutionary Clustering ◽  
Edge Detectors ◽  
Ct Brain ◽  
Edge Based ◽  
The Brain

Though image segmentation is a fundamental task in image analysis; it plays a vital role in the area of image processing. Its value increases in case of medical diagnostics through medical images like X-ray, PET, CT and MRI. In this paper, an attempt is taken to analyse a CT brain image. It has been segmented for a particular patch in the brain CT image that may be one of the tumours in the brain. The purpose of segmentation is to partition an image into meaningful regions with respect to a particular application. Image segmentation is a method of separating the image from the background, read the contents and isolating it.In this paper both the concept of clustering and thresholding technique with edge based segmentation methods like sobel, prewitt edge detectors is applied. Then the result is optimized using GA for efficient minimization of the objective function and for improved classification of clusters. Further the segmented result is passed through a Gaussian filter to obtain a smoothed image.

Label fusion method based on sparse patch representation for the brain MRI image segmentation

2017 ◽  
Vol 11 (7) ◽  
pp. 502-511 ◽  
Author(s):  
Hong Liu ◽  
Meng Yan ◽  
Enmin Song ◽  
Yuejing Qian ◽  
Xiangyang Xu ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Brain Mri ◽  
Fusion Method ◽  
Label Fusion ◽  
The Brain ◽  
Mri Image

scholarly journals Predicting malignant and benign brain tumor using image processing

2018 ◽  
Vol 7 (2.31) ◽  
pp. 199
Author(s):  
Rishabh Saxena ◽  
Aakriti Johri ◽  
Vikas Deep ◽  
Purushottam Sharma
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Brain Tumor ◽  
Human Brain ◽  
Brain Mri ◽  
Prediction System ◽  
Image Mining ◽  
Mining Algorithms ◽  
The Brain ◽  
Mri Image

Brain is the most important and versatile organ of the human body. One of the most deadly diseases that damage the brain is the accumulation of unwanted and deadly cells near the curvature of brain known as brain tumor. There are two types of brain tumor namely malignant and benign. Malignant is a cancerous tumor and benign is a non cancerous tumor. Primarily brain tumor grows in the brain tissue. The project uses MATLAB to develop a prediction system which uses original hospital brain MRI to predict the brain tumor. Project uses digital image processing to predict the brain tumor. The use of certain image mining algorithms helps in predicting the correct spot and area of brain tumor by image segmentation. The procedure starts with uploading MRI image of human brain, forward by the pre-processing of the image.  

A novel image segmentation method based on fast density clustering algorithm

2018 ◽  
Vol 73 ◽  
pp. 92-110 ◽  
Author(s):  
Jinyin Chen ◽  
Haibin Zheng ◽  
Xiang Lin ◽  
Yangyang Wu ◽  
Mengmeng Su
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Segmentation Method ◽  
Density Clustering
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