MR Images Of Human Brain Research Articles

A Novel Brain Tumor Detection and Coloring Technique from 2D MRI Images

The early automated identification of brain tumors is a difficult task in MRI images. For a long time, continuous research efforts have floated a new idea of replacing different grayscale anatomic regions of diagnostic images with appropriate colors that could overcome the problems being faced by radiologists. The colorization of grayscale images is challenging for enhancing various regions’ contrasts by transforming grayscale images into high-contrast color images. This study investigates standard solutions in discriminating between normal and abnormal regions by assigning colors to grayscale human brain MR images to differentiate different kinds of tissues. The proposed approach is influenced by connected component and index-based colorization methods for applying colors to different regions and abnormal areas. It is an automated approach that varies its inputs using luminance and pixel matrix values and provides the possible outcome. After segmentation, a specific algorithm is devised to colorize the region-of-interest (ROI) areas, which distinguishes and applies colors to differentiate the regions. Results show that implementing the watershed-based area segmentation method and ROI selection method based on the morphological operation helps identify tissues during processing. Moreover, the colorization approach based on luminance and pixel matrix after segmentation and ROI selection is beneficial due to better PSNR and SSIM values and visible contrast improvement. Our proposed algorithm works with less processing overhead and uses less time than those of the industry’s previously used color transfer method.

Non-rigid registration of biomedical image for radiotherapy based on adaptive feature density flow

This paper presents a non-rigid biomedical image registration method based on Adaptive feature density flow (AFDF). Considering the muhiplicative characteristics of metadata signal for instance Magnetic Resonance Imaging (MRI) with high-resolution and high-sensitivity, the Non-Local Means Filtering Adapted to Rician Noise (NLMr) is employed to remove Rician noise rather than Gaussian noise with higher Peak Signal to Noise Ratio (PSNR). The deformation information and local shape information are homogeneous, so a flexible dimension of Fast Local Self-Similarity (FLSS) feature descriptor can be constructed to obtain adaptive FLSS (AFLSS) feature descriptor with adaptive Angle and Radial intervals. On the basis of AFLSS, the Adaptive feature density flow (AFDF) is proposed by calculating the dense feature flow field via Belief Propagation algorithm and thereby further optimized by the Pyramid iterative method. Finally, the image registration is completed by using the bicubic interpolation algorithm according to the dense feature flow field. Aiming at radiotherapy positioning control through precise robust registration of biomedical images, experiments of human brain MR images are carried out. The comparative results with PSNR, Root Mean Square Error (RMSE) and normalize mutual information (NMI) show that AFDF performs better than the optical flow method and Scale Invariant Feature Transform (SIFT) flow method, especially for the biomedical scenario with unconscious creep deformation.

Brain Magnetic Resonance Imaging Segmentation Using Possibilistic Clustering Algorithm Combined with Context Constraints

Because the FCM method is simple and effective, a series of research results based on this method are widely used in medical image segmentation. Compared with the traditional FCM, the probability clustering (PCM) algorithm cancels the constraint on the normalization of each sample membership degree in the iterative process, and the clustering effect of the method is improved within a certain range. However, the above two methods only use the gray value of the image pixels in the iterative process, ignoring the context constraint relationship between the high-dimensional image pixels. The two are easily affected by image noise during the segmentation process, resulting in poor robustness, which will affect the segmentation accuracy in practical applications. In order to alleviate this problem, this paper introduces the context constraint information of image based on PCM, and proposes a PCM algorithm that combines context constraints (CCPCM) and successfully applies it to human brain MR image segmentation to further improve the noise immunity of the new algorithm. Expand the applicability of new algorithms in the medical field. Through simulation results on medical images, it is found that compared with the previous classical clustering methods, such as FCM, PCM, etc., the CCPCM has better anti-interference to different noises, and the segmentation boundary is clearer. At the same time, CCPCM algorithm introduces the spatial neighbor information adaptive weighting mechanism in the clustering process, which can adaptively adjust the constraint weight of spatial information and optimize the clustering process, thus improving the segmentation efficiency.

An Evaluation of Structural Brain Tissue Abnormalities to Diagnose Attention Deficit Hyperactivity Disorder

Attention-deficit/hyperactivity disorder (ADHD) is highly associated with brain abnormality. In this paper, an evaluation of structural brain tissue abnormalities to diagnose ADHD is proposed. A Brain Image Data Set containing 40, T2 axial Human ADHD Brain MR Images is created, normalized, preprocessed by Skull stripping method and Caudate Nucleus(CN), Grey Matter(GM) and White Matter(WM)data sets are formed by segregating the structural brain parts CN, GM and WM using Fuzzy C-Means algorithm. The texture, intensity and shape based features of each data set is extracted, and the images with abnormal (reduced) volume and shape are classified with the help of Pearson’s chi-squared (χ2) test by statistical measures and machine learning classifiers. The results show that out of 40, the brain tissues of 25 ADHD brain images are found with reduced volume and size of all CN, GM and WM and remaining images are found with at least any one of these abnormality compared to control group. The findings confirm that ADHD is a brain disorder which mainly affects the growth of the brain tissues and recommends the physicians to initiate the diagnosis process of ADHD with MRI brain scans instead of high risk medication and counseling among the children.

Reconstruction of compressively sampled MR images based on a local shrinkage thresholding algorithm with curvelet transform.

To reduce the magnetic resonance imaging (MRI) data acquisition time and improve the MR image reconstruction performance, reconstruction algorithms based on the iterative shrinkage thresholding algorithm (ISTA) are widely used. However, these traditional algorithms use global threshold shrinkage, which is not efficient. In this paper, a novel algorithm based on local threshold shrinkage, which is called the local shrinkage thresholding algorithm (LSTA), was proposed. The LSTA can shrink differently for different elements from the residual matrix to adjust the shrinkage speed for each element of the image during the iterative process. Then, by taking advantage of the sparser characteristics of the curvelet transform, the LSTA combined with the curvelet transform (CLSTA) can make the construction process more efficient. Finally, compared with ISTA, the generalized thresholding iterative algorithm (GTIA) and the fast iterative shrinkage threshold algorithm (FISTA), when analysing human (brain and cervical) MR images, a conclusion can be drawn that the proposed method has better reconstruction performance in terms of the mean square error (MSE), the peak signal to noise ratio (PNSR), the structural similarity index measure (SSIM), the normalized mutual information (NMI), the transferred edge information (TEI) and the number of iterations. The proposed method can better maintain the detailed information of the reconstructed images and effectively decrease the blurring of the images edges. Graphical abstract.

Inference of ADHD using Linear Discriminant Feature Analysis of Brain Caudate Nucleus

A methodology to inferAttention Deficit/Hyperactivity Disorder (ADHD) from the features of Caudate Nucleus using Linear Discriminant Analysis is proposed in this research work. A Brain Image Data Set containing 40, T2 axial Human ADHD Brain MR Images is created initially. The caudate Nucleus of each image is extracted with Fuzzy C-Means algorithm and stored in the Caudate Nucleus Data Set(CNDS). The texture, intensity and shape based features of CNDS is extracted, analysed and the features which are closely related to the inference of ADHD are identified. With the help of statistical measures and LDA classifier the input images are classified into ADHD and NOADHD classes and further the level of ADHD is identified as Mild, Moderate and severe ADHD images. The LDA classifier results show that out of 40CN brain tissues, 36ADHD images are found with reduced volume, contrast and intensity values compared to control group and 4 images are found with NoADHD. The performanceis evaluated by means of various classification metrics like Confusion matrix, Accuracy measures and Parallel Coordinates Plot. The LDA classifier yields90% of classification accuracy on CN dataset and reveals that the children with ADHD are found with low volume, contrast and Intensity values compared to normal group which suggests the neurologists to diagnose ADHD using Brain scan method instead of Medication or counseling.

Accelerating MR Imaging via Deep Chambolle-Pock Network.

Compressed sensing (CS) has been introduced to accelerate data acquisition in MR Imaging. However, CS-MRI methods suffer from detail loss with large acceleration and complicated parameter selection. To address the limitations of existing CS-MRI methods, a model-driven MR reconstruction is proposed that trains a deep network, named CP-net, which is derived from the Chambolle-Pock algorithm to reconstruct the in vivo MR images of human brains from highly undersampled complex k-space data acquired on different types of MR scanners. The proposed deep network can learn the proximal operator and parameters among the Chambolle-Pock algorithm. All of the experiments show that the proposed CP-net achieves more accurate MR reconstruction results, outperforming state-of-the-art methods across various quantitative metrics.

Topological correction of infant white matter surfaces using anatomically constrained convolutional neural network

Reconstruction of accurate cortical surfaces without topological errors (i.e., handles and holes) from infant brain MR images is very important in early brain development studies. However, infant brain MR images typically suffer extremely low tissue contrast and dynamic imaging appearance patterns. Thus, it is inevitable to have large amounts of topological errors in the segmented infant brain tissue images, which lead to inaccurately reconstructed cortical surfaces with topological errors. To address this issue, inspired by recent advances in deep learning, we propose an anatomically constrained network for topological correction on infant cortical surfaces. Specifically, in our method, we first locate regions of potential topological defects by leveraging a topology-preserving level set method. Then, we propose an anatomically constrained network to correct those candidate voxels in the located regions. Since infant cortical surfaces often contain large and complex handles or holes, it is difficult to completely correct all errors using one-shot correction. Therefore, we further enroll these two steps into an iterative framework to gradually correct large topological errors. To the best of our knowledge, this is the first work to introduce deep learning approach for topological correction of infant cortical surfaces. We compare our method with the state-of-the-art methods on both simulated topological errors and real topological errors in human infant brain MR images. Moreover, we also validate our method on the infant brain MR images of macaques. All experimental results show the superior performance of the proposed method.

Retrospective correction of intensity inhomogeneity with sparsity constraints in transform-domain: Application to brain MRI

An effective retrospective correction method is introduced in this paper for intensity inhomogeneity which is an inherent artifact in MR images. Intensity inhomogeneity problem is formulated as the decomposition of acquired image into true image and bias field which are expected to have sparse approximation in suitable transform domains based on their known properties. Piecewise constant nature of the true image lends itself to have a sparse approximation in framelet domain. While spatially smooth property of the bias field supports a sparse representation in Fourier domain. The algorithm attains optimal results by seeking the sparsest solutions for the unknown variables in the search space through L1 norm minimization. The objective function associated with defined problem is convex and is efficiently solved by the linearized alternating direction method. Thus, the method estimates the optimal true image and bias field simultaneously in an L1 norm minimization framework by promoting sparsity of the solutions in suitable transform domains. Furthermore, the methodology doesn't require any preprocessing, any predefined specifications or parametric models that are critically controlled by user-defined parameters. The qualitative and quantitative validation of the proposed methodology in simulated and real human brain MR images demonstrates the efficacy and superiority in performance compared to some of the distinguished algorithms for intensity inhomogeneity correction.

An Automatic Medical Image Registration Approach Based on Common Sub-regions of Interest

Accurate and efficient image registration, based on interested common sub-regions is still a challenging task in medical image analysis. This paper presents an automatic features based approach for the rigid and deformable registration of medical images using interested common sub-regions. In the proposed approach, interested common sub-regions in two images (target image and source image) are automatically detected and locally registered. The final global registration is performed, using the transformation parameters obtained from the local registration. Registration using interested common sub-regions is always required in image guided surgery (IGS) and other medical procedures because it considers only the desired objects in medical images instead of the whole image contents. The proposed interested common sub-regions based registration is compared with the two states-of-the-art methods on MR images of human brain. In the experiments of rigid and deformable registrations, we show that our approach outperforms in terms of both the accuracy and time efficiency. The results reveal that interested common sub-region based registration can achieve good performance, regarding both the accuracy as well as the the time efficiency in monomodal brain image registration. In addition, the proposed approach also indicates the potential for multimodal images of different human organs.

IDSS-based Two stage classification of brain tumor using SVM

The computer and image processing has a significant role in detecting tumor area. The decision support systems for human brain MR images are essentially encouraged with the requirement of attaining maximal achievable efficiency and the motivation of the approach which is to enhance the performance of Computer-Aided Diagnosis (CAD) system to detect a tumor in the human brain. Even though numerous support systems have been introduced in the past, this is still an open problem seeking for an accurate and robust decision support system. The Interactive Diagnosis Support System (IDSS) approach has addressed the limitations of nonillumination and low contrast of a brain tumor MR image that influences the procedure of accurate image classification. Thus, the IDSS is implemented in three phases namely image preprocessing for enhancing non-illuminated features, feature extraction and image classification which is accomplished using two-stage interactive SVM Classification. The local binary patterns are detected in the feature extraction for accurate classification of usual and unusual brain MR Images. The experimental outcomes for this approach are carried out using MATLAB R2016a and evaluated using the brain images downloaded from the Internet. The performance metrics such as structured similarity index, sensitivity, specificity and accuracy were used to assess the IDSS-based tumor classification system. When compared with the traditional classifiers such as ANFIS, Backpropagation and K-NN, the IDSS approach has significant brain tumor classification accuracy.

Atlas pre-selection strategies to enhance the efficiency and accuracy of multi-atlas brain segmentation tools.

Multi-atlas brain segmentation of human brain MR images allows quantification research in structural neuroimaging. To achieve high accuracy and computational efficiency of segmentation relies on a custom subset of atlases for each target subject. However, the criterion for atlas pre-selection remains an open question. In this study, two atlas pre-selection approaches based on location-based feature matching were proposed and compared to random and mutual information-based methods using a database of 47 atlases. A varying number of atlases ranked top with hierarchical structural granularity were compared using Dice overlap. The results indicated that the proposed 4L approach consistently led to the highest level of accuracy at a given number of employed atlases in both adult and geriatric populations. In addition, the proposed two methods (4L and LV) can reduce 20 times computational time compared with the stereotypical mutual information-based method. Our pre-selection strategy would provide better segmentation performance in terms of both accuracy and efficiency. The proposed atlas pre-selection will be further implemented into our online automatic brain image segmentation system (www.mricloud.org).

An efficient volumetric segmentation of cerebral lateral ventricles

Human brain is a set of four communicating network of ventricles heaving with cerebrospinal fluid (CSF) which is located inside the brain parenchyma. An efficient segmentation of cerebral lateral ventricles one in each hemisphere can support the study of efficient pathologies for successful conclusion of various diseases. In this paper, an efficient and fast energy optimised technique for volumetric segmentation of lateral ventricles from MR images of human brain is proposed which is based on geodesic active contours using level set method. The proposed approach consists of mainly four main stages: 1. Preprocessing stage, 2. Presegmentation stage, 3. Contour Evolution with Energy optimisation stage, 4. Termination stage. Experiments on multislice MRI data obtained dice coefficient of 0.955, jaccard coefficient of 0.915 and other surface distance measures demonstrate the advantages of the proposed approach in both accuracy and efficiency.

Evaluation of the effect of doubling atlases using midsagittal plane on multi-atlas based segmentation of brain structures.

Normal human brain exhibits approximately bi-fold symmetry with respect to its midsagittal plane (MSP). The objective of this work is to investigate the effect of doubling atlases (i.e., reference images) used in multi-atlas fusion methods by exploiting the inherent bilateral symmetry of human brain. To this end, we perform automated segmentation of 15 subcortical structures using Local Weighted Voting (LWV) fusion method with varying number of atlases. We consider three specific scenarios for atlases while performing fusion: (i) fusion with original OASIS atlases, (ii) with atlases obtained by flipping the original atlases based on their MSP, and (iii) with both original and flipped atlases. Evaluations are performed on the publicly available OASIS dataset of 20 normal human brain MR images. One of the key findings of this study is that when the number of atlases available for fusion is less than 10, fusion by combining both the original and flipped atlases provided more accurate segmentations than using only the original atlases, or only the flipped atlases.

Robust anatomical landmark detection with application to MR brain image registration

Comparison of human brain MR images is often challenged by large inter-subject structural variability. To determine correspondences between MR brain images, most existing methods typically perform a local neighborhood search, based on certain morphological features. They are limited in two aspects: (1) pre-defined morphological features often have limited power in characterizing brain structures, thus leading to inaccurate correspondence detection, and (2) correspondence matching is often restricted within local small neighborhoods and fails to cater to images with large anatomical difference. To address these limitations, we propose a novel method to detect distinctive landmarks for effective correspondence matching. Specifically, we first annotate a group of landmarks in a large set of training MR brain images. Then, we use regression forest to simultaneously learn (1) the optimal sets of features to best characterize each landmark and (2) the non-linear mappings from the local patch appearances of image points to their 3D displacements towards each landmark. The learned regression forests are used as landmark detectors to predict the locations of these landmarks in new images. Because each detector is learned based on features that best distinguish the landmark from other points and also landmark detection is performed in the entire image domain, our method can address the limitations in conventional methods. The deformation field estimated based on the alignment of these detected landmarks can then be used as initialization for image registration. Experimental results show that our method is capable of providing good initialization even for the images with large deformation difference, thus improving registration accuracy.

Rough set based bilateral filter design for denoising brain MR images

Graphical abstractDisplay Omitted HighlightsThe soft computing, in precise rough set theory (RST) based class information and edge information is used in bilateral framework for medical image denoising problem.RST based framework is proposed as prior information for denoising purpose.The proposal is able to restrict the conventional bilateral filter to over smooth the region near the boundaries.It is extension of our conference paper with wide range of noise amount and different modalities of MR images. We extended it to real human brain MR images also.Performance has been compared with state-of-the-art methods and found to be satisfactory. A study on bilateral filter for denoising reveals that more informative the filters are, better is the result expected. Moreover, getting precise information of the image with noise is a difficult task. In the current work, a rough set theory (RST) based approach is used to derive pixel level edge map and class labels which in turn are used to improve the performance of bilateral filters. RST handles the uncertainty present in the data even under noise. The basic structure of existing bilateral filter is not changed much, however, boosted up by prior information derived by rough edge map and rough class labels. The filter is extensively applied to denoise brain MR images. The results are compared with that of the state-of-the-art approaches. The experiments have been performed on two real (normal and pathological disordered) human MR databases. The performance of the proposed filter is found to be better, in terms of benchmark metrics.

A cytoarchitecture-driven myelin model reveals area-specific signatures in human primary and secondary areas using ultra-high resolution in-vivo brain MRI

This work presents a novel approach for modelling laminar myelin patterns in the human cortex in brain MR images on the basis of known cytoarchitecture. For the first time, it is possible to estimate intracortical contrast visible in quantitative ultra-high resolution MR images in specific primary and secondary cytoarchitectonic areas. The presented technique reveals different area-specific signatures which may help to study the spatial distribution of cortical T1 values and the distribution of cortical myelin in general. It may lead to a new discussion on the concordance of cyto- and myeloarchitectonic boundaries, given the absence of such concordance atlases. The modelled myelin patterns are quantitatively compared with data from human ultra-high resolution in-vivo 7T brain MR images (9 subjects). In the validation, the results are compared to one post-mortem brain sample and its ex-vivo MRI and histological data. Details of the analysis pipeline are provided. In the context of the increasing interest in advanced methods in brain segmentation and cortical architectural studies, the presented model helps to bridge the gap between the microanatomy revealed by classical histology and the macroanatomy visible in MRI.

Enhancement of the low resolution image quality using randomly sampled data for multi-slice MR imaging.

Low resolution images are often acquired in in vivo MR applications involving in large field-of-view (FOV) and high speed imaging, such as, whole-body MRI screening and functional MRI applications. In this work, we investigate a multi-slice imaging strategy for acquiring low resolution images by using compressed sensing (CS) MRI to enhance the image quality without increasing the acquisition time. In this strategy, low resolution images of all the slices are acquired using multiple-slice imaging sequence. In addition, extra randomly sampled data in one center slice are acquired by using the CS strategy. These additional randomly sampled data are multiplied by the weighting functions generated from low resolution full k-space images of the two slices, and then interpolated into the k-space of other slices. In vivo MR images of human brain were employed to investigate the feasibility and the performance of the proposed method. Quantitative comparison between the conventional low resolution images and those from the proposed method was also performed to demonstrate the advantage of the method.

Hippocampus Segmentation Techniques: A Survey

Since the volume of hippocampal formation has been found to be an early biomarker for MCI and Alzheimer's disease, hippocampus segmentation plays a significant role in clinical diagnosis. Because hippocampus in MR images presents features of low contrast, low signal-to-noise ratio and discontinuous boundaries, accurate segmentation still remains a challenging task. We presented a survey of the methods used to segment the hippocampal formation in MR images of human brain and concluded with a discussion on the trend of the future research in hippocampus segmentation.

Optimal Feature Selection using Independent Component Analysis and Significant Feature Processing Applied to Human Brain MR Images

Optimal Feature Selection using Independent Component Analysis and Significant Feature Processing Applied to Human Brain MR Images