MRI Segmentation of the Human Brain: Challenges, Methods, and Applications

Computational and Mathematical Methods in Medicine ◽

10.1155/2015/450341 ◽

2015 ◽

Vol 2015 ◽

pp. 1-23 ◽

Cited By ~ 202

Author(s):

Ivana Despotović ◽

Bart Goossens ◽

Wilfried Philips

Keyword(s):

Image Segmentation ◽

Medical Image Analysis ◽

Brain Mri ◽

Bias Field ◽

Mri Segmentation ◽

Anatomical Structures ◽

Segmentation Methods ◽

Brain Changes ◽

The Brain ◽

Segmentation Problem

Image segmentation is one of the most important tasks in medical image analysis and is often the first and the most critical step in many clinical applications. In brain MRI analysis, image segmentation is commonly used for measuring and visualizing the brain’s anatomical structures, for analyzing brain changes, for delineating pathological regions, and for surgical planning and image-guided interventions. In the last few decades, various segmentation techniques of different accuracy and degree of complexity have been developed and reported in the literature. In this paper we review the most popular methods commonly used for brain MRI segmentation. We highlight differences between them and discuss their capabilities, advantages, and limitations. To address the complexity and challenges of the brain MRI segmentation problem, we first introduce the basic concepts of image segmentation. Then, we explain different MRI preprocessing steps including image registration, bias field correction, and removal of nonbrain tissue. Finally, after reviewing different brain MRI segmentation methods, we discuss the validation problem in brain MRI segmentation.

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Digital Medical Image Segmentation Using Fuzzy C-Means Clustering

UHD Journal of Science and Technology ◽

10.21928/uhdjst.v4n1y2020.pp51-58 ◽

2020 ◽

Vol 4 (1) ◽

pp. 51 ◽

Cited By ~ 1

Author(s):

Bakhtyar Ahmed Mohammed ◽

Muzhir Shaban Al-Ani

Keyword(s):

Image Segmentation ◽

Medical Image ◽

Medical Image Analysis ◽

Brain Mri ◽

Accurate Method ◽

Medical Image Segmentation ◽

Fuzzy C Means ◽

Fcm Clustering ◽

The Brain ◽

Digital Medical Image

In the modern globe, digital medical image processing is a major branch to study in the fields of medical and information technology. Every medical field relies on digital medical imaging in diagnosis for most of their cases. One of the major components of medical image analysis is medical image segmentation. Medical image segmentation participates in the diagnosis process, and it aids the processes of other medical image components to increase the accuracy. In unsupervised methods, fuzzy c-means (FCM) clustering is the most accurate method for image segmentation, and it can be smooth and bear desirable outcomes. The intention of this study is to establish a strong systematic way to segment complicate medical image cases depend on the proposed method to share in the decision-making process. This study mentions medical image modalities and illustrates the steps of the FCM clustering method mathematically with example. It segments magnetic resonance imaging (MRI) of the brain to separate tumor inside the brain MRI according to four statuses.

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

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2020.2899 ◽

2020 ◽

Vol 10 (2) ◽

pp. 515-521 ◽

Cited By ~ 1

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.

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An Introspective Performance Analysis of Threshold-based Segmentation Techniques on Digital Mammograms

YMER Digital ◽

10.37896/ymer20.11/16 ◽

2021 ◽

Vol 20 (11) ◽

pp. 176-195

Author(s):

A Nithya ◽

◽

P Shanmugavadivu ◽

Keyword(s):

Image Segmentation ◽

Medical Image Analysis ◽

Region Of Interest ◽

Computation Time ◽

Vital Role ◽

Cancer Prognosis ◽

Processing Step ◽

Segmentation Methods ◽

Reliable Source ◽

Mammogram Images

Image segmentation, as a pre-processing step, plays a vital role in medical image analysis. The variants of threshold-based image segmentation methods are proved to offer feasible and optimal solutions to extract the region of interest (RoI), from medical images. Digital mammograms are used as a reliable source of breast cancer prognosis and diagnosis. Thresholding is a simple and effective strategy that finds applications in image processing and analysis. This research aimed to analyze the performance behaviour of a few threshold-based segmentation methods with respect to the intensity distribution of the input mammograms. For this analytical research, six automated thresholding segmentation techniques were chosen: Kapur, Otsu’s, Isoentropic, Ridler & Calvard’s, Kittler & Illingworth's, and Yen. The performance and behaviour of those methods were validated on the digital mammogram images of mini-MIAS database featured with Fatty (F), Fatty-Glandular (G), and Dense-Glandular (D) breast tissues. Those methods were analyzed on two metrics viz., Region Non-Uniformity (RNU) and computation time. The results of this research confirm that Ridler & Calvard’s method gives the best segmentation results for Dense-Glandular, Isoentropic method gives better segmentation results for Fatty and Yen method works well on the Fatty-Glandular normal mammogram images.

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Multi-Atlas Brain MRI Segmentation with Multiway Cut

10.54294/62rn24 ◽

2013 ◽

Author(s):

Duygu Sarikaya ◽

Liang Zhao ◽

Jason J. Corso

Keyword(s):

Brain Mri ◽

Brain Structures ◽

Mri Segmentation ◽

Weighted Voting ◽

Brain Images ◽

Anatomical Structures ◽

Test Dataset ◽

Multiway Cut ◽

The Subject

Characterization of anatomical structure of the brain and efficient algorithms for automatically analyzing brain MRI have gained an increasing interest in recent years. In this paper, we propose an algorithm that automatically segments the anatomical structures of magnetic resonance human brain images. Our method uses the prior knowledge of labels given by experts to statistically investigate the spatial correspondences of brain structures in subject images. We create a multi-atlas by registering the training images to the subject image and then propagating corresponding labels to the graph of the image. Label fusion then combines these multiple labels of atlases into one label at each voxel with intensity similarity based weighted voting. Finally we cluster the graph using multiway cut in order to achieve the final 3D segmentation of the subject image. The promising evaluation results of our segmentation method on the MRBrainS13 Test Dataset shows the efficiency and robustness of our algorithm.

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

Frontiers in Neuroscience ◽

10.3389/fnins.2021.662674 ◽

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.

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A Robust Brain MRI Segmentation and Bias Field Correction Method Integrating Local Contextual Information into a Clustering Model

Applied Sciences ◽

10.3390/app9071332 ◽

2019 ◽

Vol 9 (7) ◽

pp. 1332

Author(s):

Zhe Zhang ◽

Jianhua Song

Keyword(s):

Contextual Information ◽

Brain Mri ◽

Brain Magnetic Resonance Imaging ◽

Bias Field ◽

Correction Method ◽

Intensity Inhomogeneity ◽

Mri Segmentation ◽

Robust Clustering ◽

Clustering Model ◽

Central Pixel

The segmentation results of brain magnetic resonance imaging (MRI) have important guiding significance for subsequent clinical diagnosis and treatment. However, brain MRI segmentation is a complex and challenging problem due to the inevitable noise or intensity inhomogeneity. A novel robust clustering with local contextual information (RC_LCI) model was used in this study which accurately segmented brain MRI corrupted by noise and intensity inhomogeneity. For pixels in the neighborhood of the central pixel, a weighting scheme combining local contextual information was used to generate the corresponding anisotropic weight to update the current central pixel and thus remove noisy pixels. Then, a multiplicative framework consisting of the product of a real image and a bias field could effectively segment brain MRI and estimate the bias field. Further, a linear combination of basis functions was introduced to guarantee the bias field properties. Therefore, compared with state-of-the-art models, the segmentation result obtained by RC_LCI was increased by 0.195 0.125 in terms of the Jaccard similarity coefficient. Both visual experimental results and quantitative evaluation demonstrate the superiority of RC_LCI on real and synthetic images.

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ADAPTIVE IMAGE SEGMENTATION USING ARTIFICIAL CO-EVOLVING TRIBES

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001407005922 ◽

2007 ◽

Vol 21 (07) ◽

pp. 1171-1193

Author(s):

YUN WEN CHEN ◽

YAN QIU CHEN

Keyword(s):

Image Segmentation ◽

Visual Perception ◽

Artificial Life ◽

Experimental Results ◽

The Other ◽

Evolution Process ◽

Human Visual Perception ◽

Segmentation Methods ◽

Scale Control ◽

Segmentation Problem

Deriving from the artificial life theory, this paper proposes an artificial co-evolving tribes model and applies it to solve the image segmentation problem. During the evolution process, the individuals in this model making up the tribes effect communication cooperatively from one agent to the other in order to increase the homogeneity of the ensemble of the image regions they represent. Two remarkable properties, that is, the monotone contraction and the conservation of the system are proved. Stability and scale control of the proposed method are carefully analyzed. Experimental results are presented and compared with two latest segmentation methods, both quantitatively and visually. We also discuss the results matching with human visual perception.

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

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2018.2521 ◽

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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