Magnetic Resonance Images Of Human Brain Research Articles

Human Brain Magnetic Resonance Imaging Studies for Psychiatric Disorders: The Current Progress and Future Directions.

With the prevalence of psychiatric disorders and the limitations of the diagnostic scheme and treatment options of these disorders, magnetic resonance imaging (MRI) studies play a significant role in uncovering the pathological basis of psychiatric disorders and potentially using biological markers in clinical settings. The use of MRI in clinical research has grown over the past three decades, and current MRI research continues to provide an avenue to guide the development of diagnostic approaches and therapeutic solutions. However, the current shortcomings of MRI studies derive not only from technical limitations (i.e., the range of contrasts that MRI probes or sensors can create) but also from confounding factors in the current methodological approaches of case-control studies for psychiatric disorders. Thus, by reviewing the recent literature on MRI research on psychiatric disorders, we explain the current progress and limitations of brain MRI methodologies used to study psychiatric disorders. We consider the growing use of cross-disorder methods to identify shared and disease-specific pathological features across psychiatric disorders. In addition, we need to outline healthy developmental and aging changes of the brain and investigate the disorder difference as a deviation of the trajectory. Although these methods have provided us with new insights, the demarcation between psychiatric disorders based on a definitive set of pathologies remains limited. This challenge of disease stratification is further complicated by the presence of multiple different sets of disorder pathologies within a single disorder and the different progressive timelines of different disorders. As such, we introduce the ongoing research projects in Japan, namely, the Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS) and the Strategic International Brain Science Research Promotion Program (Brain/MINDS Beyond). These collaborative research initiatives across Japan use neuroimaging and travel-subject harmonization to conduct nationwide MRI studies capable of providing large-scale coherent results, which may address the current limitations of MRI psychiatric disorder research.

Human-to-monkey transfer learning identifies the frontal white matter as a key determinant for predicting monkey brain age.

The application of artificial intelligence (AI) to summarize a whole-brain magnetic resonance image (MRI) into an effective "brain age" metric can provide a holistic, individualized, and objective view of how the brain interacts with various factors (e.g., genetics and lifestyle) during aging. Brain age predictions using deep learning (DL) have been widely used to quantify the developmental status of human brains, but their wider application to serve biomedical purposes is under criticism for requiring large samples and complicated interpretability. Animal models, i.e., rhesus monkeys, have offered a unique lens to understand the human brain - being a species in which aging patterns are similar, for which environmental and lifestyle factors are more readily controlled. However, applying DL methods in animal models suffers from data insufficiency as the availability of animal brain MRIs is limited compared to many thousands of human MRIs. We showed that transfer learning can mitigate the sample size problem, where transferring the pre-trained AI models from 8,859 human brain MRIs improved monkey brain age estimation accuracy and stability. The highest accuracy and stability occurred when transferring the 3D ResNet [mean absolute error (MAE) = 1.83 years] and the 2D global-local transformer (MAE = 1.92 years) models. Our models identified the frontal white matter as the most important feature for monkey brain age predictions, which is consistent with previous histological findings. This first DL-based, anatomically interpretable, and adaptive brain age estimator could broaden the application of AI techniques to various animal or disease samples and widen opportunities for research in non-human primate brains across the lifespan.

The Brainbox-a tool to facilitate correlation of brain magnetic resonance imaging features to histopathology.

Magnetic resonance imaging (MRI) has limitations in identifying underlying tissue pathology, which is relevant for neurological diseases such as multiple sclerosis, stroke or brain tumours. However, there are no standardized methods for correlating MRI features with histopathology. Thus, here we aimed to develop and validate a tool that can facilitate the correlation of brain MRI features to corresponding histopathology. For this, we designed the Brainbox, a waterproof and MRI-compatible 3D printed container with an integrated 3D coordinate system. We used the Brainbox to acquire post-mortem ex vivo MRI of eight human brains, fresh and formalin-fixed, and correlated focal imaging features to histopathology using the built-in 3D coordinate system. With its built-in 3D coordinate system, the Brainbox allowed correlation of MRI features to corresponding tissue substrates. The Brainbox was used to correlate different MR image features of interest to the respective tissue substrate, including normal anatomical structures such as the hippocampus or perivascular spaces, as well as a lacunar stroke. Brain volume decreased upon fixation by 7% (P = 0.01). The Brainbox enabled degassing of specimens before scanning, reducing susceptibility artefacts and minimizing bulk motion during scanning. In conclusion, our proof-of-principle experiments demonstrate the usability of the Brainbox, which can contribute to improving the specificity of MRI and the standardization of the correlation between post-mortem ex vivo human brain MRI and histopathology. Brainboxes are available upon request from our institution.

A 32-Channel Sleeve Antenna Receiver Array for Human Head MRI Applications at 10.5 T.

For human brain magnetic resonance imaging (MRI), high channel count ( ≥ 32 ) radiofrequency receiver coil arrays are utilized to achieve maximum signal-to-noise ratio (SNR) and to accelerate parallel imaging techniques. With ultra-high field (UHF) MRI at 7 tesla (T) and higher, dipole antenna arrays have been shown to generate high SNR in the deep regions of the brain, however the array elements exhibit increased electromagnetic coupling with one another, making array construction more difficult with the increasing number of elements. Compared to a classical dipole antenna array, a sleeve antenna array incorporates the coaxial ground into the feed-point, resulting in a modified asymmetric antenna structure with improved intra-element decoupling. Here, we extended our previous 16-channel sleeve transceiver work and developed a 32-channel azimuthally arranged sleeve antenna receive-only array for 10.5 T human brain imaging. We experimentally compared the achievable SNR of the sleeve antenna array at 10.5 T to a more traditional 32-channel loop array bult onto a human head-shaped former. The results obtained with a head shaped phantom clearly demonstrated that peripheral intrinsic SNR can be significantly improved compared to a loop array with the same number of elements- except for the superior part of the phantom where sleeve antenna elements are not located.

The effects of normobaric and hyperbaric oxygenation on MRI signal intensities in T1 -weighted, T2 -weighted and FLAIR images in human brain.

Dissolved oxygen has known paramagnetic effects in magnetic resonance imaging (MRI). The aim of this study was to compare the effects of normobaric oxygenation (NBO) and hyperbaric oxygenation (HBO) on human brain MRI signal intensities. Baseline brain MRI was performed in 17 healthy subjects (mean age 27.8 ± 3.2). MRI was repeated after exposure to the NBO and HBO at different time points (0 min, 25 min, 50 min). Signal intensities in T 1-weighted, T 2-weighted images and fluid attenuated inversion recovery (FLAIR) signal intensities of several intracranial structures were compared between NBO and HBO. Increased T 1-weighted signal intensities were observed in white and deep grey brain matter, cerebrospinal fluid (CSF), venous blood and vitreous body after exposure to NBO as well as to HBO compared to baseline (Dunnett's test, p < 0.05) without significant differences between both protocols. There was also no significant difference in T 2-weighted signal intensities between NBO and HBO. FLAIR signal intensities were increased only in the vitreous body after NBO and HBO and FLAIR signal of caudate nucleus was decreased after NBO (Dunnett's test, p < 0.05). The statistically significant differences in FLAIR signal intensities were found between NBO and HBO (paired t-test, p < 0.05) in most observed brain structures (paired t-test, p < 0.05). Our results show that NBO and HBO alters signal intensities T 1-weighted and FLAIR images of human brain. The differences between NBO and HBO are most pronounced in FLAIR imaging.

Parcellation of Brain Magnetic Resonance Images using Improved Atlases Selection Model

Introduction: Multiple atlas-based parcellation model has been demonstrated to perform better than single atlas-based parcellation model in terms of accuracy of the parcellation of human brain Magnetic Resonance Images (MRI). The weakness of the existing multiple atlas-based parcellation models is that the level of accuracy is limited if used for the ageing brain due to the presence of age-related changes such as atrophy. Aim: The aim of this study is to develop a novel multiple atlases selection model that ensures improved accuracy for the parcellation of the ageing brain by combining Cost function with the Similarity metric and Atrophy measure for atlases selection. This model is called COSA. Materials and Methods: A dataset with ten brain MRIs and ten atlases was used. A brain MRI was used one at a time as the target image while the remaining images constituted the source images. Using each target image, consensus atlases were obtained for COSA from the combination of a cost function, similarity index, and atrophy measure. These atlases were consequently used to parcellate the target image. Performance was assessed using Dice Coefficient and COSA was compared with existing atlases selection models. The existing atlases selection models investigated were Normalized Mutual Information (NMI), Mutual Information (MI), Correlation Ratio (CR), Normalized Correlation Ratio (NCR), and Least Square Error (LSE). Results: Mean of Dice Coefficient were: COSA = 0.7495196, NMI = 0.7479508, MI = 0.7473333, Jaccard Index = 0.7392522, CR = 0.7358384, NC = 0.7358043, Atrophy Measure = 0.7300867, LSE = 0.7299367, Single Atlas =0.6830223. Conclusion: Results show that COSA performs better than the existing multiple Atlas-based models.

5T magnetic resonance imaging: radio frequency hardware and initial brain imaging.

We aimed to demonstrate the feasibility of generating high-resolution human brain magnetic resonance imaging (MRI) at 5 Tesla (T) using a quadrature birdcage transmit/48-channel receiver coil assembly. A quadrature birdcage transmit/48-channel receiver coil assembly was designed for human brain imaging at 5T. The radio frequency (RF) coil assembly was validated by electromagnetic (EM) simulations and phantom imaging experimental studies. The simulated B1+ field inside a human head phantom and inside a human head model generated by the birdcage coils driven in circularly polarized (CP) mode at 3T, 5T and 7T was compared. Signal-to-noise ratio (SNR) maps, the inverse g-factor maps for evaluation of parallel imaging performance, anatomic images, angiography images, vessel wall images and susceptibility weighted images (SWI) were acquired using the RF coil assembly at 5T and compared to those acquired using a 32-channel head coil on a 3T MRI scanner. For the EM simulations, 5T MRI provided less RF inhomogeneity compared to that of 7T. In the phantom imaging study, the distributions of the measured B1+ field were consistent with the distributions of the simulated B1+ field. In the human brain imaging study, the average SNR value of the brain in the transversal plane at 5T was 1.6 times of that at 3T. The 48-channel head coil at 5T had higher parallel acceleration capability than the 32-channel head coil at 3T. The anatomic images at 5T also showed higher SNR than those at 3T. Improved delineation of the hippocampus, lenticulostriate arteries, and basilar arteries was observed at 5T compared to 3T. SWI with a higher resolution of 0.3 mm ×0.3 mm ×1.2 mm could be acquired at 5T, which enabled better visualization of small blood vessels compared to that at 3T. 5T MRI can provide significant SNR improvement compared to that of 3T with less RF inhomogeneity than that of 7T. The ability to obtain high quality in vivo human brain images at 5T using the quadrature birdcage transmit/48-channel receiver coil assembly has significant in clinical and scientific research applications.

Putamen Atrophy Is a Possible Clinical Evaluation Index for Parkinson's Disease Using Human Brain Magnetic Resonance Imaging.

Parkinson's disease is characterized by motor dysfunction caused by functional deterioration of the substantia nigra. Lower putamen volume (i.e., putamen atrophy) may be an important clinical indicator of motor dysfunction and neurological symptoms, such as autonomic dysfunction, in patients with Parkinson's disease. We proposed and applied a new evaluation method for putamen volume measurement on 31 high-resolution T2-weighted magnetic resonance images from 16 patients with Parkinson's disease (age, 80.3 ± 7.30 years; seven men, nine women) and 30 such images from 19 control participants (age, 75.1 ± 7.85 years; eleven men, eight women). Putamen atrophy was expressed using a ratio based on the thalamus. The obtained values were used to assess differences between the groups using the Wilcoxon rank-sum test. The intraclass correlation coefficient showed sufficient intra-rater reliability and validity of this method. The Parkinson's disease group had a significantly lower mean change ratio in the putamen (0.633) than the control group (0.719), suggesting that putamen atrophy may be identified using two-dimensional images. The evaluation method presented in this study may indicate the appearance of motor dysfunction and cognitive decline and could serve as a clinical evaluation index for Parkinson's disease.

A Monopole and Dipole Hybrid Antenna Array for Human Brain Imaging at 10.5 Tesla.

In this letter, we evaluate antenna designs for ultra-high frequency and field (UHF) human brain magnetic resonance imaging (MRI) at 10.5 tesla (T). Although MRI at such UHF is expected to provide major signal-to-noise gains, the frequency of interest, 447 MHz, presents us with challenges regarding improved B1 + efficiency, image homogeneity, specific absorption rate (SAR), and antenna element decoupling for array configurations. To address these challenges, we propose the use of both monopole and dipole antennas in a novel hybrid configuration, which we refer to as a mono-dipole hybrid antenna (MDH) array. Compared to an 8-channel dipole antenna array of the same dimensions, the 8-channel MDH array showed an improvement in decoupling between adjacent array channels, as well as ~18% higher B1 + and SAR efficiency near the central region of the phantom based on simulation and experiment. However, the performances of the MDH and dipole antenna arrays were overall similar when evaluating a human model in terms of peak B1 + efficiency, 10 g SAR, and SAR efficiency. Finally, the concept of an MDH array showed an advantage in improved decoupling, SAR, and B1 + near the superior region of the brain for human brain imaging.

Combining advanced magnetic resonance imaging (MRI) with finite element (FE) analysis for characterising subject-specific injury patterns in the brain after traumatic brain injury

Traumatic brain injury (TBI) is a leading cause of death and disability. The way mechanical impact is transferred to the brain has been shown to be a major determinant for structural damage and subsequent pathological sequalae. Although finite element (FE) models have been used extensively in the investigation of various aspects of TBI and have been instrumental in characterising a TBI injury threshold and the pattern of diffuse axonal injuries, subject-specific analysis has been difficult to perform due to the complexity of brain structures and its material properties. We have developed an efficient computational pipeline that can generate subject-specific FE models of the brain made up of conforming hexahedral elements directly from advanced MRI scans. This pipeline was applied and validated in our sheep model of TBI. Our FE model of the sheep brain accurately predicted the damage pattern seen on post-impact MRI scans. Furthermore, our model also showed a complex time-varying strain distribution pattern, which was not present in the homogeneous model without subject-specific material descriptions. To our knowledge, this is the first fully subject-specific FE model of the sheep brain able to predict structural damage after a head impact. The pipeline developed has the potential to augment the analysis of human brain MRI scans to detect changes in brain structures and function after TBI.

The application of the distributed-order time fractional Bloch model to magnetic resonance imaging

It is now well known that the magnetic resonance imaging (MRI) signal decay deviates from the classical mono-exponential relaxation. This deviation is referred to in the literature as anomalous relaxation. The modelling of this anomalous relaxation can provide a better understanding of MRI magnetization. The purpose of this work is to investigate the utility of the distributed-order time fractional Bloch equations to describe anomalous relaxation processes in human brain MRI data. Two choices of continuous distribution weight functions, which are parameterised by their mean μ and standard deviation σ, are studied to investigate their impact on the model solution behaviour. An implicit numerical method implemented on a graded mesh is proposed to solve the model and the stability and convergence analysis are presented. We also derive semi-analytical solutions of the fully coupled Bloch equations using the Laplace transform technique to assess the accuracy of the numerical scheme. Furthermore, three different voxel models of continuous distribution weight functions, namely a single continuous probability distribution (model 1), two distinct continuous probability distributions (model 2) and a mixture of two continuous probability distributions (model 3), are applied to the in vivo human brain MRI data, and a feasible and reliable parameter estimation method based on a modified hybrid Nelder-Mead simplex search and particle swarm optimization is presented to perform the voxel-level temporal fitting of the MRI data. The application of these distributed-order time fractional Bloch models highlights the validity of the proposed models, and based on the mean square error we conclude that models 2 and 3 might be more suitable than model 1 to characterize anomalous relaxation processes in human brain MRI data.

Brain/MINDS Beyond Human Brain MRI (BMB HBM) Project: Understanding the Pathophysiology of Psychiatric Disorders

The AMED Brain/MINDS Beyond (BMB) Human Brain MRI (BMB HBM) project aims to gain a deeper understanding of the pathophysiology of neuropsychiatric disorders and identify clinical biomarkers using large-scale magnetic resonance imaging (MRI) studies. To achieve these objectives, we established a novel MRI protocol using high-resolution multimodal images and implemented the scanning protocol in traveling subjects at 13 research centers and performed prospective and retrospective data harmonization. The BMB project is expected to enable high-quality multi-site MRI harmonization and promote international collaboration for standardized diagnosis of neuropsychiatric diseases based on neuroimaging.

Application of Artificial Intelligence in the MRI Classification Task of Human Brain Neurological and Psychiatric Diseases: A Scoping Review.

Artificial intelligence (AI) for medical imaging is a technology with great potential. An in-depth understanding of the principles and applications of magnetic resonance imaging (MRI), machine learning (ML), and deep learning (DL) is fundamental for developing AI-based algorithms that can meet the requirements of clinical diagnosis and have excellent quality and efficiency. Moreover, a more comprehensive understanding of applications and opportunities would help to implement AI-based methods in an ethical and sustainable manner. This review first summarizes recent research advances in ML and DL techniques for classifying human brain magnetic resonance images. Then, the application of ML and DL methods to six typical neurological and psychiatric diseases is summarized, including Alzheimer’s disease (AD), Parkinson’s disease (PD), major depressive disorder (MDD), schizophrenia (SCZ), attention-deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD). Finally, the limitations of the existing research are discussed, and possible future research directions are proposed.

Hyperpolarized 129 Xe multi-slice imaging of the human brain using a 3D gradient echo pulse sequence.

To demonstrate the possibility of performing multi-slice in-vivo human brain MRI using hyperpolarized (HP) xenon-129 (129 Xe) in two different orientations and to calculate the signal-to-noise ratio (SNR). Two healthy female participants were imaged during a single breath-hold of HP 129 Xe using a Philips Achieva 3.0T MRI scanner (Philips, Andover, MA). Each HP 129 Xe multi-slice brain image was acquired during separate HP 129 Xe breath-holds using 3D gradient echo (GRE) imaging. The acquisition started 10 s after the inhalation of 1 L of HP 129 Xe. Overall, four sagittal and three axial images were acquired (seven imaging sessions per participant). The SNR was calculated for each slice in both orientations. The first ever HP 129 Xe multi-slice images of the brain were acquired in axial and sagittal orientations. The HP 129 Xe signal distribution correlated well with the gray matter distribution. The highest SNR values were close in the axial and sagittal orientations (19.46 ± 3.25 and 18.76 ± 4.94, respectively). Additionally, anatomical features, such as the ventricles, were observed in both orientations. The possibility of using multi-slice HP 129 Xe human brain magnetic resonance imaging was demonstrated for the first time. HP 129 Xe multi-slice MRI can be implemented for brain imaging to improve current diagnostic methods.

Calibration-Less Multi-Coil Compressed Sensing Magnetic Resonance Image Reconstruction Based on OSCAR Regularization.

Over the last decade, the combination of compressed sensing (CS) with acquisition over multiple receiver coils in magnetic resonance imaging (MRI) has allowed the emergence of faster scans while maintaining a good signal-to-noise ratio (SNR). Self-calibrating techniques, such as ESPiRIT, have become the standard approach to estimating the coil sensitivity maps prior to the reconstruction stage. In this work, we proceed differently and introduce a new calibration-less multi-coil CS reconstruction method. Calibration-less techniques no longer require the prior extraction of sensitivity maps to perform multi-coil image reconstruction but usually alternate estimation sensitivity map estimation and image reconstruction. Here, to get rid of the nonconvexity of the latter approach we reconstruct as many MR images as the number of coils. To compensate for the ill-posedness of this inverse problem, we leverage structured sparsity of the multi-coil images in a wavelet transform domain while adapting to variations in SNR across coils owing to the OSCAR (octagonal shrinkage and clustering algorithm for regression) regularization. Coil-specific complex-valued MR images are thus obtained by minimizing a convex but nonsmooth objective function using the proximal primal-dual Condat-Vù algorithm. Comparison and validation on retrospective Cartesian and non-Cartesian studies based on the Brain fastMRI data set demonstrate that the proposed reconstruction method outperforms the state-of-the-art (-ESPIRiT, calibration-less AC-LORAKS and CaLM methods) significantly on magnitude images for the T1 and FLAIR contrasts. Additionally, further validation operated on 8 to 20-fold prospectively accelerated high-resolution ex vivo human brain MRI data collected at 7 Tesla confirms the retrospective results. Overall, OSCAR-based regularization preserves phase information more accurately (both visually and quantitatively) compared to other approaches, an asset that can only be assessed on real prospective experiments.

CNN Fusion Based Brain Tumor Detection from MRI images using Active Contour Segmentation Techniques

Early diagnosis of a brain tumor may increase life expectancy. Magnetic resonance imaging (MRI) accompanied by several segmentation algorithms is preferred as a reliable method for assessment. This manuscript presented the performance analysis of clustered based and fusion-based segmentation techniques intended to detect the tumor from human brain MRI images in an efficient manner. Four primary steps are involved in this work such as pre-processing, clustering, segmentation, and fusion techniques. The main clustering methods such as K-means and fuzzy c-means (FCM) were first applied to the pre-processed MRI images, and then, the clustered images were segmented directly using the active contour segmentation techniques such as chan-vese (C-V) and level set method (LSM). Then in the next step, the clustered images were fused by using the non-sub sampled contour transform (NSCT) and convolution neural network (CNN) fusion methods, and then, the fused images were segmented by using the C-V and LSM segmentation methods again. The results of both clustered based and fusion-based segmentation in terms of structural similarity index measure (SSIM), dice coefficient (DC), computational time, sensitivity, precision, and segmentation accuracy revealed that CNN fusion-based C-V segmentation performs better than without fusion (clustered based or direct segmentation) to detect the tumors from the MRI images. The results indicate that C-V performs better with CNN as compared with the LSM. Finally, the fusion-based segmentation is an efficient approach to detect the tumor from the MRI images with minimal information loss and high segmentation accuracy over the clustered based segmentation.

Brain/MINDS beyond human brain MRI project: A protocol for multi-level harmonization across brain disorders throughout the lifespan

Psychiatric and neurological disorders are afflictions of the brain that can affect individuals throughout their lifespan. Many brain magnetic resonance imaging (MRI) studies have been conducted; however, imaging-based biomarkers are not yet well established for diagnostic and therapeutic use. This article describes an outline of the planned study, the Brain/MINDS Beyond human brain MRI project (BMB-HBM, FY2018 ~ FY2023), which aims to establish clinically-relevant imaging biomarkers with multi-site harmonization by collecting data from healthy traveling subjects (TS) at 13 research sites. Collection of data in psychiatric and neurological disorders across the lifespan is also scheduled at 13 sites, whereas designing measurement procedures, developing and analyzing neuroimaging protocols, and databasing are done at three research sites. A high-quality scanning protocol, Harmonization Protocol (HARP), was established for five high-quality 3 T scanners to obtain multimodal brain images including T1 and T2-weighted, resting-state and task functional and diffusion-weighted MRI. Data are preprocessed and analyzed using approaches developed by the Human Connectome Project. Preliminary results in 30 TS demonstrated cortical thickness, myelin, functional connectivity measures are comparable across 5 scanners, suggesting sensitivity to subject-specific connectome. A total of 75 TS and more than two thousand patients with various psychiatric and neurological disorders are scheduled to participate in the project, allowing a mixed model statistical harmonization. The HARP protocols are publicly available online, and all the imaging, demographic and clinical information, harmonizing database will also be made available by 2024. To the best of our knowledge, this is the first project to implement a prospective, multi-level harmonization protocol with multi-site TS data. It explores intractable brain disorders across the lifespan and may help to identify the disease-specific pathophysiology and imaging biomarkers for clinical practice.

Bias-Corrected Intuitionistic Fuzzy C-Means With Spatial Neighborhood Information Approach for Human Brain MRI Image Segmentation

Segmentationof MRI imagesin the presence of artifacts such as noise and bias field effect (BFE) is challenging. In this article, to handle both artifacts, we have formulated an intuitionistic fuzzy set (IFS) theory based bias-corrected intuitionistic fuzzy c-means with spatial neighborhood information (SNI) method for magnetic resonance imaging (MRI) image segmentation. We represent the image in terms of IFSs using two well-known intuitionistic fuzzy generation functions, namely Sugeno’s negation function (SNF) and Yager’s negation function (YNF). In this way, the proposed approach takes advantage of the IFS theory to address the uncertainty and vagueness present in data. The SNI term helps to retain the fine image details in the segmentation process, which is usually lost when smoothing is used. The BFE is modeled as a slow varying intensity field additive in nature with mean zero in IFS framework. The proposed approach estimates the bias field and produces a segmented image simultaneously. The segmentation performance of the proposed method is evaluated in terms of the Dice score, average segmentation accuracy (ASA), and index of variation. Further, the comparison of the proposed method with other similar state-of-the-art methods on two well-known publicly available Brain MRI datasets shows the significant improvements in segmentation performance in terms of ASA, Dice score, and index of variation. The proposed approach achieves 93% ASA in the presence of severe bias field and noise artifacts on BrainWeb simulated dataset, which outperformed the state-of-the-art methods.

AUTOMATED CLASSIFICATION FOR BRAIN MRIS BASED ON 2D MF-DFA METHOD

Magnetic resonance image (MRI) is an important tool to diagnose human diseases effectively. It is very important for research and clinical application to classify the normal and abnormal human brain MRI images automatically. In this paper, an accurate and efficient technique is proposed to extract features of MRIs and classify these images into normal and abnormal categories. We use two-dimensional multifractal detrended fluctuation analysis (2D MF-DFA) to obtain image features. These features are the local generalized Hurst exponents calculated by 2D MF-DFA. In this regard, the values of Hurst exponents are given as the training input vector and are taken to the classifiers. We use [Formula: see text]-nearest neighbor ([Formula: see text]-NN) and support vector machine (SVM) to classify a specific brain MRI as normal or glioma affected. For SVM, we apply the leave-one-out cross-validation method for experimental verification. The 2D MF-DFA-SVM system achieved accuracy, sensitivity, and specificity of [Formula: see text] ±[Formula: see text], [Formula: see text], and [Formula: see text] ±[Formula: see text], respectively. The 2D MF-DFA-[Formula: see text]-NN system achieved accuracy, sensitivity, and specificity of [Formula: see text], [Formula: see text], and [Formula: see text], respectively. We find that when performing binary classification for brain MRIs, the SVM is superior to [Formula: see text]-NN. In addition, our experimental results indicate that the proposed 2D MF-DFA-SVM achieved excellent outcomes compared to those of the previous works. The proposed system is a promising system to clinical use.

A hybrid image enhancement based brain MRI images classification technique

The classification of brain magnetic resonance imaging (MRI) images into normal and abnormal classes, has great potential to reduce the radiologists workload. Statistical analysis based approaches has been widely employed for this purpose which are comprised of four stages such as pre-processing, feature extraction, feature reduction and classification. The outcome of such approaches are highly dependent upon the image quality: better the image, higher the outcome. In this paper, we present a hypothesis that the quality of the image, which is enhanced at the pre-processing stage, can play a significant role in enhancing the classification performance of any statistical approach. To strengthen our theory we first employed an improved image enhancement technique, which consists of three different sub-stages: noise removal using median filter, contrast enhancement using histogram equalization technique and image conversion from gray-scale to RGB. After image enhancement, we extract features from an enhanced MR brain image using a discrete wavelet transform and these feature are further reduced by color moments i.e mean, standard deviation, and skewness. Finally, we trained an advanced deep neural network (DNN) to categorize the human brain MRI images as normal or pathological. The approach obtained 95.8% which is significantly higher than the previous state-of-the-art techniques. The result evident that our hypothesis about the role of image enhancement process in medical image classification, is realistic and also have potential to improve the performance of other medical image analysis technique.