scholarly journals A Robust Spatial Information-Theoretic GMM Algorithm for Bias Field Estimation and Brain MRI Segmentation

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 89617-89629
Author(s):  
Yunjie Chen ◽  
Mao Cai ◽  
Xinze Zhou ◽  
Cheng Ning ◽  
Chunzheng Cao ◽  
...  
Keyword(s):  
Spatial Information ◽  
Brain Mri ◽  
Bias Field ◽  
Mri Segmentation ◽  
Information Theoretic ◽  
Field Estimation

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

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.

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

2015 ◽  
Vol 2015 ◽  
pp. 1-23 ◽  
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.

A Modified Fuzzy C-Means Algorithm Brain MR Images Segmentation with Bias Field Compensation

2013 ◽  
Vol 756-759 ◽  
pp. 1349-1355 ◽  
Author(s):  
Xiao Li Liu ◽  
Yu Ting Guo ◽  
Jun Kong ◽  
Jian Zhong Wang
Keyword(s):  
Brain Mri ◽  
Bias Field ◽  
Local Information ◽  
Mr Images ◽  
Fcm Algorithm ◽  
Different Types ◽  
Field Estimation ◽  
Non Local ◽  
Brain Mr Images ◽  
Fuzzy C Means Algorithm

Segmentation of brain magnetic resonance (MR) images is always required as a preprocessing stage in many brain analysis tasks. Nevertheless, the bias field (BF, also called intensity in-homogeneities) and noise in the MRI images always make the accurate segmentation difficult. In this paper, we present a modified FCM algorithm for bias field estimation and segmentation of brain MRI. Our method is formulated by modifying the objective function of the standard FCM algorithm. It aims to compensate for bias field and incorporate both the local and non-local information into the distance function to restrain the noise of the image. We have conducted extensive experimental and have compared our method with different types of FCM extension methods using simulated MRI images. The results show that our proposed method can deal with the bias field and noise effectively and outperforms other methods.

scholarly journals Localized FCM Clustering with Spatial Information for Medical Image Segmentation and Bias Field Estimation

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Wenchao Cui ◽  
Yi Wang ◽  
Yangyu Fan ◽  
Yan Feng ◽  
Tao Lei
Keyword(s):  
Objective Function ◽  
Spatial Information ◽  
Bias Field ◽  
Medical Image Segmentation ◽  
Membership Functions ◽  
Image Domain ◽  
Fcm Clustering ◽  
Field Estimation ◽  
Fuzzy Energy ◽  
The Impact

This paper presents a novel fuzzy energy minimization method for simultaneous segmentation and bias field estimation of medical images. We first define an objective function based on a localized fuzzyc-means (FCM) clustering for the image intensities in a neighborhood around each point. Then, this objective function is integrated with respect to the neighborhood center over the entire image domain to formulate a global fuzzy energy, which depends on membership functions, a bias field that accounts for the intensity inhomogeneity, and the constants that approximate the true intensities of the corresponding tissues. Therefore, segmentation and bias field estimation are simultaneously achieved by minimizing the global fuzzy energy. Besides, to reduce the impact of noise, the proposed algorithm incorporates spatial information into the membership function using the spatial function which is the summation of the membership functions in the neighborhood of each pixel under consideration. Experimental results on synthetic and real images are given to demonstrate the desirable performance of the proposed algorithm.

Recent advancements in Fuzzy C-means based techniques for brain MRI Segmentation

2021 ◽  
Vol 16 ◽  
Author(s):  
Ghazanfar Latif ◽  
Jaafar Alghazo ◽  
Fadi N. Sibai ◽  
D.N.F. Awang Iskandar ◽  
Adil H. Khan
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Mri ◽  
Tumor Segmentation ◽  
Brain Anatomy ◽  
Mri Segmentation ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Low Contrast ◽  
Fuzzy C Means

Background: Variations of image segmentation techniques, particularly those used for Brain MRI segmentation, vary in complexity from basic standard Fuzzy C-means (FCM) to more complex and enhanced FCM techniques. Objective: In this paper, a comprehensive review is presented on all thirteen variations of FCM segmentation techniques. In the review process, the concentration is on the use of FCM segmentation techniques for brain tumors. Brain tumor segmentation is a vital step in the process of automatically diagnosing brain tumors. Unlike segmentation of other types of images, brain tumor segmentation is a very challenging task due to the variations in brain anatomy. The low contrast of brain images further complicates this process. Early diagnosis of brain tumors is indeed beneficial to patients, doctors, and medical providers. Results: FCM segmentation works on images obtained from magnetic resonance imaging (MRI) scanners, requiring minor modifications to hospital operations to early diagnose tumors as most, if not all, hospitals rely on MRI machines for brain imaging. In this paper, we critically review and summarize FCM based techniques for brain MRI segmentation.

Quality-driven Deep Active Learning Method for 3D Brain MRI Segmentation

2021 ◽  
Author(s):  
Zhenxi Zhang ◽  
Jie Li ◽  
Chunna Tian ◽  
Zhusi Zhong ◽  
Zhicheng Jiao ◽  
...  
Keyword(s):  
Active Learning ◽  
Brain Mri ◽  
Learning Method ◽  
Mri Segmentation ◽  
Active Learning Method

Comparison of Different Image Processing Methods with Spatial Information in Clinical Brain MRI

2020 ◽  
Author(s):  
Iza Sazanita Isa ◽  
Mohamad Khairul Faizi Mat Saad ◽  
Muhammad Haris Khusairi Mohmad Kadir ◽  
Ahmad Afifi Ahmad Afandi ◽  
Noor Khairiah A. Karim ◽  
...  
Keyword(s):  
Image Processing ◽  
Spatial Information ◽  
Brain Mri ◽  
Processing Methods

scholarly journals Leveraging 3d Information In Unsupervised Brain Mri Segmentation

2021 ◽  
Author(s):  
Benjamin Lambert ◽  
Maxime Louis ◽  
Senan Doyle ◽  
Florence Forbes ◽  
Michel Dojat ◽  
...  
Keyword(s):  
Brain Mri ◽  
Mri Segmentation ◽  
3D Information

Left ventricular dysfunction is associated with cerebral grey matter injury: An in-vivo brain MRI segmentation study

2012 ◽  
Vol 321 (1-2) ◽  
pp. 111-113 ◽  
Author(s):  
Pratik Bhattacharya ◽  
Fen Bao ◽  
Megha Shah ◽  
Gautam Ramesh ◽  
Ramesh Madhavan ◽  
...  
Keyword(s):  
Left Ventricular Dysfunction ◽  
Grey Matter ◽  
Ventricular Dysfunction ◽  
Brain Mri ◽  
Left Ventricular ◽  
Mri Segmentation ◽  
Cerebral Grey Matter ◽  
Grey Matter Injury
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