Brain MRI Segmentation Research Articles

Brain MR image segmentation using NAMS in pseudo-color

Image segmentation plays a crucial role in various biomedical applications. In general, the segmentation of brain Magnetic Resonance (MR) images is mainly used to represent the image with several homogeneous regions instead of pixels for surgical analyzing and planning. This paper proposes a new approach for segmenting MR brain images by using pseudo-color based segmentation with Non-symmetry and Anti-packing Model with Squares (NAMS). First of all, the NAMS model is presented. The model can represent the image with sub-patterns to keep the image content and largely reduce the data redundancy. Second, the key idea is proposed that convert the original gray-scale brain MR image into a pseudo-colored image and then segment the pseudo-colored image with NAMS model. The pseudo-colored image can enhance the color contrast in different tissues in brain MR images, which can improve the precision of segmentation as well as directly visual perceptional distinction. Experimental results indicate that compared with other brain MR image segmentation methods, the proposed NAMS based pseudo-color segmentation method performs more excellent in not only segmenting precisely but also saving storage.

A novel method based on independent component analysis for brain MR image tissue classification into CSF, WM and GM for atrophy detection in Alzheimer’s disease

Brain Magnetic Resonance Image (MRI) plays a vital role in diagnosis of diseases like Brain Tumor, Alzheimer, Multiple Sclerosis, Schizophrenia and other White Matter Lesions. In most of the cases accurate segmentation of Brain MRI into tissue types like Cerebro-Spinal Fluid (CSF), White Matter (WM) and Grey Matter (GM) is of interest. The diagnostic accuracy of expert and non-expert Radiologists can be improved with accurate and automated tissue segmentation and classification system. Such system can also be used for trainees to understand the individual tissue distribution in MRI scans. In this paper, we propose a novel automated tissue segmentation and classification method based on Independent Component Analysis (ICA) with Band Expansion Process (BEP) and Support Vector Machine (SVM) classifier which with input as T1, T2 and Proton Density (PD) scans of patient, provides output as CSF, WM and GM indicating the possible atrophy in brain which can help in diagnosis of Alzheimer’s disease (AD). The objective of this work is to test the effectiveness of ICA with different input images generated using BEP for accurate brain tissue segmentation by validating results with different quality metrics. The novel method for generating input images for ICA has been implemented and segmented tissues are used for atrophy detection. The BEP + ICA + Thresholding + ‘SVM trained with Grey Level Co-occurrence Matrix (GLCM) based texture features’ is giving 100% tissue classification accuracy for test samples under consideration.

Novel active contour model based on multi-variate local Gaussian distribution for local segmentation of MR brain images

Active contour model (ACM) has been one of the most widely utilized methods in magnetic resonance (MR) brain image segmentation because of its ability of capturing topology changes. However, most of the existing ACMs only consider single-slice information in MR brain image data, i.e., the information used in ACMs based segmentation method is extracted only from one slice of MR brain image, which cannot take full advantage of the adjacent slice images’ information, and cannot satisfy the local segmentation of MR brain images. In this paper, a novel ACM is proposed to solve the problem discussed above, which is based on multi-variate local Gaussian distribution and combines the adjacent slice images’ information in MR brain image data to satisfy segmentation. The segmentation is finally achieved through maximizing the likelihood estimation. Experiments demonstrate the advantages of the proposed ACM over the single-slice ACM in local segmentation of MR brain image series.

Brain MR image segmentation based on an improved active contour model.

It is often a difficult task to accurately segment brain magnetic resonance (MR) images with intensity in-homogeneity and noise. This paper introduces a novel level set method for simultaneous brain MR image segmentation and intensity inhomogeneity correction. To reduce the effect of noise, novel anisotropic spatial information, which can preserve more details of edges and corners, is proposed by incorporating the inner relationships among the neighbor pixels. Then the proposed energy function uses the multivariate Student's t-distribution to fit the distribution of the intensities of each tissue. Furthermore, the proposed model utilizes Hidden Markov random fields to model the spatial correlation between neigh-boring pixels/voxels. The means of the multivariate Student's t-distribution can be adaptively estimated by multiplying a bias field to reduce the effect of intensity inhomogeneity. In the end, we reconstructed the energy function to be convex and calculated it by using the Split Bregman method, which allows our framework for random initialization, thereby allowing fully automated applications. Our method can obtain the final result in less than 1 second for 2D image with size 256 × 256 and less than 300 seconds for 3D image with size 256 × 256 × 171. The proposed method was compared to other state-of-the-art segmentation methods using both synthetic and clinical brain MR images and increased the accuracies of the results more than 3%.

Robust generative asymmetric GMM for brain MR image segmentation

Background and objectivesAccurate segmentation of brain tissues from magnetic resonance (MR) images based on the unsupervised statistical models such as Gaussian mixture model (GMM) has been widely studied during last decades. However, most GMM based segmentation methods suffer from limited accuracy due to the influences of noise and intensity inhomogeneity in brain MR images. To further improve the accuracy for brain MR image segmentation, this paper presents a Robust Generative Asymmetric GMM (RGAGMM) for simultaneous brain MR image segmentation and intensity inhomogeneity correction. MethodFirst, we develop an asymmetric distribution to fit the data shapes, and thus construct a spatial constrained asymmetric model. Then, we incorporate two pseudo-likelihood quantities and bias field estimation into the model's log-likelihood, aiming to exploit the neighboring priors of within-cluster and between-cluster and to alleviate the impact of intensity inhomogeneity, respectively. Finally, an expectation maximization algorithm is derived to iteratively maximize the approximation of the data log-likelihood function to overcome the intensity inhomogeneity in the image and segment the brain MR images simultaneously. ResultsTo demonstrate the performances of the proposed algorithm, we first applied the proposed algorithm to a synthetic brain MR image to show the intermediate illustrations and the estimated distribution of the proposed algorithm. The next group of experiments is carried out in clinical 3T-weighted brain MR images which contain quite serious intensity inhomogeneity and noise. Then we quantitatively compare our algorithm to state-of-the-art segmentation approaches by using Dice coefficient (DC) on benchmark images obtained from IBSR and BrainWeb with different level of noise and intensity inhomogeneity. The comparison results on various brain MR images demonstrate the superior performances of the proposed algorithm in dealing with the noise and intensity inhomogeneity. ConclusionIn this paper, the RGAGMM algorithm is proposed which can simply and efficiently incorporate spatial constraints into an EM framework to simultaneously segment brain MR images and estimate the intensity inhomogeneity. The proposed algorithm is flexible to fit the data shapes, and can simultaneously overcome the influence of noise and intensity inhomogeneity, and hence is capable of improving over 5% segmentation accuracy comparing with several state-of-the-art algorithms.

Generalised rough intuitionistic fuzzy c‐means for magnetic resonance brain image segmentation

Intuitionistic fuzzy sets (IFSs), rough sets are efficient tools to handle uncertainty and vagueness present in images and recently are combined to segment medical images in the presence of noise and intensity non homogeneity (INU). These hybrid algorithms are sensitive to initial centroids, parameter tuning and dependency with the fuzzy membership function to define the IFS. In this paper, a novel clustering algorithm, namely generalized rough intutionistic fuzzy c-means (GRIFCM) is proposed for brain magnetic resonance (MR) image segmentation avoiding the dependency with the fuzzy membership function. In this algorithm, each pixel is categorized into three rough regions based on the thresholds obtained by the image data by minimizing the noise. These regions are used to create IFS. The distance measure based on IFS eliminate's the influence of noise and INU present in the image producing accurate brain tissue segmentation. The proposed algorithm is evaluated through simulation and compared it with existing k-means (KM), fuzzy c-means (FCM), Rough fuzzy c-means (RFCM), Generalized rough fuzzy c-means (GRFCM), soft rough fuzzy c-means (SRFCM) and rough intuitionistic fuzzy c-means (RIFCM) algorithms. Experimental results prove the superiority of the proposed algorithm over the considered algorithms in all analyzed scenarios.

Improved ASFCM-based Algorithm for Infant Brain MRI Segmentation

Improved ASFCM-based Algorithm for Infant Brain MRI Segmentation

A Hierarchical Fused Fuzzy Deep Neural Network for Data Classification

Deep learning (DL) is an emerging and powerful paradigm that allows large-scale task-driven feature learning from big data. However, typical DL is a fully deterministic model that sheds no light on data uncertainty reductions. In this paper, we show how to introduce the concepts of fuzzy learning into DL to overcome the shortcomings of fixed representation. The bulk of the proposed fuzzy system is a hierarchical deep neural network that derives information from both fuzzy and neural representations. Then, the knowledge learnt from these two respective views are fused altogether forming the final data representation to be classified. The effectiveness of the model is verified on three practical tasks of image categorization, high-frequency financial data prediction and brain MRI segmentation that all contain high level of uncertainties in the raw data. The fuzzy dDL paradigm greatly outperforms other nonfuzzy and shallow learning approaches on these tasks.

Segmentation of brain MRI –A tri level PSO based approach

Brain MRI plays a very important role for radiologists to diagnose and treat various brain diseases. One of the most important applications of graph partitioning is image segmentation. Various graph based methods for image segmentation application was developed, but it produces unbalance parts and NP Complete. To address such limitations, we have investigated and developed swarm intelligence based approach in which a Tri Level Particle Swarm Optimization (TLPSO) can be applied for partitioning the graph, obtained from an image to be segmented. The proposed classification method includes three stages namely conversion, implementation, selection and extraction. To check the performance of this proposed algorithm, we carried out quantitative as well as qualitative evaluation. Segmentation by graph partitioning in which PSO technology is combined with three levels helps to reduce partitioning imbalance and considers local as well as global features for segmentation. This method generates better segmentation quality and time of convergence is reduced by considerable extent.

Biomedical Image Segmentation using Optimized Fuzzy C-mean Algorithm

Background/Objectives: Automatic segmentation of brain MRI has an important role in image research along with medical image processing. It has been investigated widely in recent research. It helps for patient diagnosis for different diseases its value concerns in diagnostics through various biomedical images such as PET, CT, MRI and X-ray. In this paper, we analyzed for different biomedical images using partition method. The objective is to detect patch in the biomedical images that may lead to tumors. Methods/Statistical Analysis: The objective of segmentation is to divide the complete image into informative regions and respective specific application. Segmentation separates the image from the background, read the contents and isolating it. Both the concept of clustering by fuzzy technique with edge based segmentation method where standard methods like Sobel, Prewitt edge detectors are applied. Further it is optimized using evolutionary algorithm for efficient minimization of the objective function to improve classification accuracy. Findings: To find the smooth image Gaussian filter is used. Successive segmentation has been performed to detect the patch of desired region. It is observed for different images and compared. Improvements/Applications: It will be helpful for clinical analysis and observe the quality of images for diagnosis of diseases. Keywords: Clustering, Fuzzy C-mean Algorithm, Genetic Algorithm, Optimization, PSO, Thresholding

An automated hybrid approach using clustering and nature inspired optimization technique for improved tumor and tissue segmentation in magnetic resonance brain images

In the domain of human brain image analysis, identification of tumor region and segmentation of tissue structures tend to be a challenging task. Automated segmentation of Magnetic Resonance (MR) brain images would be of great assistance to radiologist, as they minimize the complication evolved due to human interface and offer quicker segmentation results. Automated algorithms offer minimal time duration and lesser manual intervention to a radiologist during clinical diagnosis. Moreover, larger volumes of patient data could be assessed with the aid of an automated algorithm and one such algorithm is proposed through this research to identify the tumor region bounded between normal tissue regions and edema portions. The proposed algorithm offers a better support to a radiologist in the process of diagnosing the pathologies, since; it utilizes both optimization and clustering techniques. Bacteria Foraging Optimization (BFO) and Modified Fuzzy K − Means algorithm (MFKM) are the optimization and clustering techniques used to render efficient MR brain image analysis. The proposed combinational algorithm is compared with Particle Swarm Optimization based Fuzzy C − Means algorithm (PSO based FCM), Modified Fuzzy K − Means (MFKM) and conventional FCM algorithm. The suggested methodology is evaluated using the comparison parameters such as sensitivity, Specificity, Jaccard Tanimoto Co − efficient Index (TC) and Dice Overlap Index (DOI), computational time and memory requirement. The algorithm proposed through this paper has produced appreciable values of sensitivity and specificity, which are 97.14% and 93.94%, respectively. Finally, it is found that the proposed BFO based MFKM algorithm offers better MR brain image segmentation and provides extensive support to radiologists.

Automated template-based brain localization and extraction for fetal brain MRI reconstruction

Most fetal brain MRI reconstruction algorithms rely only on brain tissue-relevant voxels of low-resolution (LR) images to enhance the quality of inter-slice motion correction and image reconstruction. Consequently the fetal brain needs to be localized and extracted as a first step, which is usually a laborious and time consuming manual or semi-automatic task. We have proposed in this work to use age-matched template images as prior knowledge to automatize brain localization and extraction. This has been achieved through a novel automatic brain localization and extraction method based on robust template-to-slice block matching and deformable slice-to-template registration. Our template-based approach has also enabled the reconstruction of fetal brain images in standard radiological anatomical planes in a common coordinate space. We have integrated this approach into our new reconstruction pipeline that involves intensity normalization, inter-slice motion correction, and super-resolution (SR) reconstruction. To this end we have adopted a novel approach based on projection of every slice of the LR brain masks into the template space using a fusion strategy. This has enabled the refinement of brain masks in the LR images at each motion correction iteration. The overall brain localization and extraction algorithm has shown to produce brain masks that are very close to manually drawn brain masks, showing an average Dice overlap measure of 94.5%. We have also demonstrated that adopting a slice-to-template registration and propagation of the brain mask slice-by-slice leads to a significant improvement in brain extraction performance compared to global rigid brain extraction and consequently in the quality of the final reconstructed images. Ratings performed by two expert observers show that the proposed pipeline can achieve similar reconstruction quality to reference reconstruction based on manual slice-by-slice brain extraction. The proposed brain mask refinement and reconstruction method has shown to provide promising results in automatic fetal brain MRI segmentation and volumetry in 26 fetuses with gestational age range of 23 to 38 weeks.

Feature Sensitive Label Fusion With Random Walker for Atlas-Based Image Segmentation.

In this paper, a novel label fusion method is proposed for brain magnetic resonance image segmentation. This label fusion method is formulated on a graph, which embraces both label priors from atlases and anatomical priors from target image. To represent a pixel in a comprehensive way, three kinds of feature vectors are generated, including intensity, gradient, and structural signature. To select candidate atlas nodes for fusion, rather than exact searching, randomized k-d tree with spatial constraint is introduced as an efficient approximation for high-dimensional feature matching. Feature sensitive label prior (FSLP), which takes both the consistency and variety of different features into consideration, is proposed to gather atlas priors. As FSLP is a non-convex problem, one heuristic approach is further designed to solve it efficiently. Moreover, based on the anatomical knowledge, parts of the target pixels are also employed as the graph seeds to assist the label fusion process, and an iterative strategy is utilized to gradually update the label map. The comprehensive experiments carried out on two publicly available databases give results to demonstrate that the proposed method can obtain better segmentation quality.

A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth

Longitudinal characterization of early brain growth in-utero has been limited by a number of challenges in fetal imaging, the rapid change in size, shape and volume of the developing brain, and the consequent lack of suitable algorithms for fetal brain image analysis. There is a need for an improved digital brain atlas of the spatiotemporal maturation of the fetal brain extending over the key developmental periods. We have developed an algorithm for construction of an unbiased four-dimensional atlas of the developing fetal brain by integrating symmetric diffeomorphic deformable registration in space with kernel regression in age. We applied this new algorithm to construct a spatiotemporal atlas from MRI of 81 normal fetuses scanned between 19 and 39 weeks of gestation and labeled the structures of the developing brain. We evaluated the use of this atlas and additional individual fetal brain MRI atlases for completely automatic multi-atlas segmentation of fetal brain MRI. The atlas is available online as a reference for anatomy and for registration and segmentation, to aid in connectivity analysis, and for groupwise and longitudinal analysis of early brain growth.

Segmentation of brain MRI using active contour model

ABSTRACTAlzheimer disease is a neurodegenerative disorder that impairs memory, cognitive function, and gradually leads to dementia, physical deterioration, loss of independence, and death of the affected individual. In this context, segmentation of medical images is a very important technique in the field of image analysis and Computer‐Assisted Diagnosis. In this article, we introduce a new automatic method of brain images’ segmentation based on the Active Contour (AC) model to extract the Hippocampus and the Corpus Callosum (CC). Our contribution is to combine the geometric method with the statistical method of the AC. We used the Caselle Level Set and added a learning phase to build an average shape and to make the initialization task automatic. For the step of contour evolution, we used the principle of Level set and we added to it the a priori knowledge. Experimental results are very promising. © 2017 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 27, 3–11, 2017

Independent Component Analysis-Support Vector Machine-Based Computer-Aided Diagnosis System for Alzheimer's with Visual Support.

Computer-aided diagnosis (CAD) systems constitute a powerful tool for early diagnosis of Alzheimer's disease (AD), but limitations on interpretability and performance exist. In this work, a fully automatic CAD system based on supervised learning methods is proposed to be applied on segmented brain magnetic resonance imaging (MRI) from Alzheimer's disease neuroimaging initiative (ADNI) participants for automatic classification. The proposed CAD system possesses two relevant characteristics: optimal performance and visual support for decision making. The CAD is built in two stages: a first feature extraction based on independent component analysis (ICA) on class mean images and, secondly, a support vector machine (SVM) training and classification. The obtained features for classification offer a full graphical representation of the images, giving an understandable logic in the CAD output, that can increase confidence in the CAD support. The proposed method yields classification results up to 89% of accuracy (with 92% of sensitivity and 86% of specificity) for normal controls (NC) and AD patients, 79% of accuracy (with 82% of sensitivity and 76% of specificity) for NC and mild cognitive impairment (MCI), and 85% of accuracy (with 85% of sensitivity and 86% of specificity) for MCI and AD patients.

Early life adversity is associated with a smaller hippocampus in male but not female depressed in-patients: a case\u2013control study

BackgroundThree studies assessed the association of early life adversity (ELA) and hippocampal volumes in depressed patients, of which one was negative and the two others did not control for several potential confounding variables. Since the association of ELA and hippocampal volumes differ in male and female healthy volunteers, we investigated the association of ELA and hippocampal volumes in depressed patients, while focusing specifically on sex and controlling for several relevant socio-demographic and clinical variables.MethodsSixty-three depressed in-patients treated in a psychiatric setting, with a current Major Depressive Episode (MDE) and a Major Depressive Disorder (MDD) were included and assessed for ELA. Hippocampal volumes were measured with brain magnetic resonance imaging (MRI) and automatic segmentation. They were compared between patients with (n = 28) or without (n = 35) ELA. After bivariate analyses, multivariate regression analyses tested the interaction of sex and ELA on hippocampal volume and were adjusted for several potential confounding variables. The subgroups of men (n = 26) and women (n = 37) were assessed separately.ResultsPatients with ELA had a smaller hippocampus than those without ELA (4.65 (±1.11) cm3 versus 5.25 (±1.01) cm3), bivariate: p = 0.03, multivariate: HR = 0.40, 95%CI [0.23;0.71], p = 0.002), independently from other factors. This association was found in men (4.43 (±1.22) versus 5.67 (±0.77) cm3), bivariate: p = 0.006, multivariate HR = 0.23, 95%CI [0.06;0.82], p = 0.03) but not in women.ConclusionELA is associated with a smaller hippocampus in male but not female depressed in-patients. The reasons for this association should be investigated in further studies.

Based on rough set and fuzzy clustering of MRI brain segmentation

A new method of MRI brain segmentation integrates fuzzy [Formula: see text]-means (FCM) clustering and rough set theory. In this paper, we use rough set algorithm to find the suitable initial clustering number to initial clustering centers for FCM. Then we use FCM to MRI brain segmentation, but the algorithm of FCM has the limitation of converging to local infinitesimal point in medical segmentation. While avoiding being trapped in a local optimum, we use the particle swarm optimization algorithm to restrict convergence of FCM which can reduce calculation. The final experiment results show that improved algorithm not only retains the advantages of rapid convergence but also can control the local convergence and improve the global search ability. The method in this paper is better than that of cluttering performance.

Multilevel Thresholding Method Based on Electromagnetism for Accurate Brain MRI Segmentation to Detect White Matter, Gray Matter, and CSF.

This work explains an advanced and accurate brain MRI segmentation method. MR brain image segmentation is to know the anatomical structure, to identify the abnormalities, and to detect various tissues which help in treatment planning prior to radiation therapy. This proposed technique is a Multilevel Thresholding (MT) method based on the phenomenon of Electromagnetism and it segments the image into three tissues such as White Matter (WM), Gray Matter (GM), and CSF. The approach incorporates skull stripping and filtering using anisotropic diffusion filter in the preprocessing stage. This thresholding method uses the force of attraction-repulsion between the charged particles to increase the population. It is the combination of Electromagnetism-Like optimization algorithm with the Otsu and Kapur objective functions. The results obtained by using the proposed method are compared with the ground-truth images and have given best values for the measures sensitivity, specificity, and segmentation accuracy. The results using 10 MR brain images proved that the proposed method has accurately segmented the three brain tissues compared to the existing segmentation methods such as K-means, fuzzy C-means, OTSU MT, Particle Swarm Optimization (PSO), Bacterial Foraging Algorithm (BFA), Genetic Algorithm (GA), and Fuzzy Local Gaussian Mixture Model (FLGMM).

Performance Evaluation of Parallel Genetic Algorithm for Brain MRI Segmentation in Hadoop and Spark

Objectives: The radical growth of brain MRI data demands faster and accurate processing. To meet these demands, it is necessary to develop a design in cloud platform using distributed platforms. Methods/Analysis: In this paper, we introduce an architecture developed for the cloud using Apache Hadoop to segment the brain MRI images. The scanned MRI images are uploaded through either through web interface or mobile app to the system in the public cloud. The Parallel Genetic Algorithm (PGA) in the cloud system enabled with Hadoop or Spark is used to segment the given MRI images. Findings: The processing time taken for different size of data varying from 2GB to 10GB in a different number of clusters varying from one to five are denoted. This process has been implemented in both Apache Hadoop and Apache Spark. The time ranges from 12 to 24 secs approximately in Hadoop whereas the processing time has come down from 4 to 7 secs in Spark. First of all, the results prove that the network based applications for Medical Image Processing are outperformed by the cloud platform applications. Novelty/Improvement: Distributed Platforms have been used in Cloud environment for Brain MRI segmentation using Parallel Genetic Algorithm.