Brain Magnetic Resonance Imaging Images Research Articles

Multistage classifier-based approach for Alzheimer's disease prediction and retrieval

The most prevalent and common type of dementia is Alzheimer's disease (AD). However, it is notable that very few people who are suffering from AD are diagnosed correctly and in a timely manner. The definite cause and cure of the disease are still unavailable. The symptoms might be more manageable and its treatment can be more effective, when the impairment is still at an earlier stage or at MCI (mild cognitive impairment). AD can be clinically diagnosed by physical and neurological examination, so there is an need for developing better and efficient diagnostic tools for AD. In recent years, content-based image retrieval (CBIR) systems have been widely researched and applied in many medical applications. Combining an automated image classification system and the radiologist's professional knowledge, to increase the accuracy of prediction and diagnosis, were the main motives. In this paper, a multistage classifier using machine learning, including Naive Bayes classifier, support vector machine (SVM), and K-nearest neighbor (KNN), was used to classify Alzheimer's disease more acceptably and efficiently. For this, MRI (Magnetic resonance imaging) scans were processed by FreeSurfer, a powerful software tool suitable for processing and normalizing brain MRI images. We also applied a feature selection technique - PSO (particle swarm optimization) to many feature vectors in order to obtain the best features that represent the salient characteristics of AD. The results of the proposed method outperform individual techniques in a benchmark database provided by the Alzheimer's Disease Neuroimaging Institute (ADNI).

Observational Study of Clinical and Functional Progression Based on Initial Brain MRI Characteristics in Patients with Alzheimer's Disease.

Background:Magnetic resonance imaging (MRI) is a useful tool to predict the diagnosis and progression of Alzheimer’s disease (AD), especially for primary physicians. However, the correlation between baseline MRI findings and AD progression has not been fully established.Objective:To investigate the correlation between hippocampal atrophy (HA) and white matter hyperintensities (WMH) on initial brain MRI images and the degree of cognitive decline and functional changes over 1 year.Methods:In this prospective, 12-month observational study, dementia outpatients were recruited from 29 centers across South Korea. Baseline assessments of HA and WMH on baseline brain MRI were derived as well as cognitive function, dementia severity, activities of daily living, and acetylcholinesterase inhibitor (AChEI) use. Follow-up assessments were conducted at 6 and 12 months.Results:Among 899 enrolled dementia patients, 748 were diagnosed with AD of whom 654 (87%) were taking AChEIs. Baseline WMH showed significant correlations with age, current alcohol consumption, and Clinical Dementia Rating score; baseline HA was correlated with age, family history, physical exercise, and the results of cognitive assessments. Among the AChEI group, changes in the Korean version of the Instrumental Activities of Daily Living (K-IADL) were correlated with the severity of HA on baseline brain MRI, but not with the baseline severity of WMH. In the no AChEI group, changes in K-IADL were correlated with the severity of WMH and HA at baseline.Conclusion:Baseline MRI findings could be a useful tool for predicting future clinical outcomes by primary physicians, especially in relation to patients’ functional status.

NEW BACTERIA FORAGING AND PARTICLE SWARM HYBRID ALGORITHM FOR MEDICAL IMAGE COMPRESSION

For perfect diagnosis of brain tumour, it is necessary to identify tumour affected regions in the brain in MRI (Magnetic Resonance Imaging) images effectively and compression of these images for transmission over a communication channel at high speed with better visual quality to the experts. An attempt has been made in this paper for identifying tumour regions with optimal thresholds which are optimized with the proposed Hybrid Bacteria Foraging Optimization Algorithm (BFOA) and Particle Swarm Optimization (PSO) named (HBFOA-PSO) by maximizing the Renyi’s entropy and Kapur’s entropy. BFOA may be trapped into local optimal problem and delay in execution time (convergence time) because of random chemo taxis steps in the procedure of algorithm and to get global solution, a theory of swarming is commenced in the structure of HBFOA-PSO. Effectiveness of this HBFOA-PSO is evaluated on six different MRI images of brain with tumours and proved to be better in Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Fitness Function.

Denoising and segmentation of MR images using fourth order non‐linear adaptive PDE and new convergent clustering

AbstractAt present, digital image processing plays a vital role in medical imaging areas and specifically in magnetic resonance imaging (MRI) of brain images such as axial and coronal sections. This article mainly focused on the MRI brain images. The existing methods such as total variation (MC), parallel MRI, modified pyramidal dual‐tree direction filter, adaptive dictionary selection algorithm, classifier methods, and fuzzy clustering techniques are poor in image eminence and precision. Thus, this article presents a novel approach consisting of denoising followed by segmentation. The objective of these proposed methods was visual eminence improvement of medical images to examine tumor extent using an adaptive partial differential equation (APDE)‐based analysis with soft threshold function in denoising. The fourth order, nonlinear APDE was used to denoise the image depending on gradient and Laplacian operators associated with the new adaptive Haar‐type wavelet transform. A second approach was the new convergent K‐means clustering for segmentation. The convergent K‐means procedure diminishes the summation of the squared deviations of structures in a cluster from the center. The significance of these proposed methods was to compute their performances in terms of mean squared error, peak signal‐to‐noise ratio, structure similarity, segmentation accuracy, false hit, missed‐term, and elapsed time. The results were analyzed with the MATLAB software.

Cerebral Small Vessel, But Not Large Vessel Disease, Is Associated With Impaired Cerebral Autoregulation During Cardiopulmonary Bypass: A Retrospective Cohort Study.

Impaired cerebral blood flow (CBF) autoregulation during cardiopulmonary bypass (CPB) is associated with stroke and other adverse outcomes. Large and small arterial stenosis is prevalent in patients undergoing cardiac surgery. We hypothesize that large and/or small vessel cerebral arterial disease is associated with impaired cerebral autoregulation during CPB. A retrospective cohort analysis of data from 346 patients undergoing cardiac surgery with CPB enrolled in an ongoing prospectively randomized clinical trial of autoregulation monitoring were evaluated. The study protocol included preoperative transcranial Doppler (TCD) evaluation of major cerebral artery flow velocity by a trained vascular technician and brain magnetic resonance imaging (MRI) between postoperative days 3 and 5. Brain MRI images were evaluated for chronic white matter hyperintensities (WMHI) by a vascular neurologist blinded to autoregulation data. "Large vessel" cerebral vascular disease was defined by the presence of characteristic TCD changes associated with stenosis of the major cerebral arteries. "Small vessel" cerebral vascular disease was defined based on accepted scoring methods of WMHI. All patients had continuous TCD-based autoregulation monitoring during surgery. Impaired autoregulation occurred in 32.4% (112/346) of patients. Preoperative TCD demonstrated moderate-severe large vessel stenosis in 67 (25.2%) of 266 patients with complete data. In adjusted analysis, female sex (odds ratio [OR], 0.46; 95% confidence interval [CI], 0.25-0.86; P = .014) and higher average temperature during CPB (OR, 1.23; 95% CI, 1.02-1.475; P = .029), but not moderate-severe large cerebral arterial stenosis (P = .406), were associated with impaired autoregulation during CPB. Of the 119 patients with available brain MRI data, 42 (35.3%) demonstrated WMHI. The presence of small vessel cerebral vascular disease was associated with impaired CBF autoregulation (OR, 3.25; 95% CI, 1.21-8.71; P = .019) after adjustment for age, history of peripheral vascular disease, preoperative hemoglobin level, and preoperative treatment with calcium channel blocking drugs. These data confirm that impaired CBF autoregulation is prevalent during CPB predisposing affected patients to brain hypoperfusion or hyperperfusion with low or high blood pressure, respectively. Small vessel, but not large vessel, cerebral vascular disease, male sex, and higher average body temperature during CPB appear to be associated with impaired autoregulation.

Automated approach for detection of ischemic stroke using Delaunay Triangulation in brain MRI images

It is difficult to develop an accurate algorithm to detect the stroke lesions using magnetic resonance imaging (MRI) images due to variation in different lesion sizes, variation in morphological structure, and similarity in intensity of lesion with normal brain in three types of stroke, namely partial anterior circulation syndrome (PACS), lacunar syndrome (LACS) and total anterior circulation stroke (TACS). In this paper, we have integrated the advantages of Delaunay triangulation (DT) and fractional order Darwinian particle swarm optimization (FODPSO), called DT-FODPSO technique for automatic segmentation of the structure of the stroke lesion. The approach was validated on 192 MRI images obtained from different stroke subjects. Statistical and morphological features were extracted and classified according to the Oxfordshire community stroke project (OCSP) using support vector machine (SVM) and random forest (RF) classifiers. The method effectively detected the stroke lesions and achieved promising results with an average sensitivity of 0.93, accuracy of 0.95, JI of 0.89 and Dice similarity index of 0.93 using RF classifier. These promising results indicates the DT based optimized approach is efficient in detecting ischemic stroke and it can aid the neuro-radiologists to validate their routine screening.

Non‐subsampled shearlet transform based MRI and PET brain image fusion using simplified pulse coupled neural network and weight local features in YIQ colour space

Magnetic resonance imaging (MRI) and positron emission tomography (PET) image fusion is a recent hybrid modality used in several oncology applications. The MRI image shows the brain tissue anatomy and does not contain any functional information, while the PET image indicates the brain function and has a low spatial resolution. A perfect MRI–PET fusion method preserves the functional information of the PET image and adds spatial characteristics of the MRI image with the less possible spatial distortion. In this context, the authors propose an efficient MRI–PET image fusion approach based on non-subsampled shearlet transform (NSST) and simplified pulse-coupled neural network model (S-PCNN). First, the PET image is transformed to YIQ independent components. Then, the source registered MRI image and the Y -component of PET image are decomposed into low-frequency (LF) and high-frequency (HF) subbands using NSST. LF coefficients are fused using weight region standard deviation (SD) and local energy, while HF coefficients are combined based on S-PCCN which is motivated by an adaptive-linking strength coefficient. Finally, inverse NSST and inverse YIQ are applied to get the fused image. Experimental results demonstrate that the proposed method has a better performance than other current approaches in terms of fusion mutual information, entropy, SD, fusion quality, and spatial frequency.

Segmentation and clustering in brain MRI imaging.

Clustering is a vital task in magnetic resonance imaging (MRI) brain imaging and plays an important role in the reliability of brain disease detection, diagnosis, and effectiveness of the treatment. Clustering is used in processing and analysis of brain images for different tasks, including segmentation of brain regions and tissues (grey matter, white matter, and cerebrospinal fluid) and clustering of the atrophy in different parts of the brain. This paper presents a state-of-the-art review of brain MRI studies that use clustering techniques for different tasks.

Optimal multilevel thresholding selection for brain MRI image segmentation based on adaptive wind driven optimization

Medical image processing is one of the real research regions in the most recent four decades. Numerous researchers have contributed very great algorithms and reported outstanding results. In this paper, adaptive wind driven optimization (AWDO) based multilevel thresholding is implemented for the segmentation Magnetic Resonance Image (MRI) brain images. The axial T2-weighted MRI brain images are considered for image segmentation. The effectiveness of the AWDO for multilevel thresholding of MR images is yet to be explored, and this paper presents humble contribution in this context. The optimal multilevel thresholding is found by maximizing the very popular objectives such as between class variance (Otsu method) and Kapur’s entropy. The efficiency of proposed approach was compared with the outcomes of existing algorithms like RGA, GA, Nelder–Mead simplex, PSO, BF and ABF. To check the effectiveness of the proposed algorithm, experimental results are analyzed quantitatively and qualitatively. The results showed the superiority of the proposed approach regarding better segmentation results.

Performance evaluation of automated brain tumor detection systems with expert delineations and interobserver variability analysis in diseased patients on magnetic resonance imaging

ABSTRACTIntervention by human expert has turned out to be essential for computerized analysis systems desiring to be approved by medical regulatory bodies. Further, to validate the performance of automated diagnosis systems, interobserver variability analysis is critically important. The purpose of this article is twofold: (i) firstly to perform interobserver variability analysis of two experienced Radiologists interpreting lesion boundary in brain magnetic resonance images; (ii) secondly, to evaluate the performance of recently proposed automated lesion segmentation model with that of the two experienced Radiologists who performed manual delineations of lesion boundary. Experiments were conducted on the database consisting of 80 real-time brain images with glioma tumor acquired using magnetic resonance imaging (MRI). Extensive statistical analysis such as the two tailed T-test, analysis of variance (ANOVA) test, Mann-Whitney U test, regression and correlation tests, etc. are performed to compare the lesions detected manually by experts and that by the automated method. Furthermore, three quantitative measures namely, dice similarity index, Jaccard coefficient, and Hausdorff distance are used to evaluate the automated lesion detection method. The experimental results show that the lesion boundaries detected by the automated method are very close to the manual delineations provided by the expert Radiologists. It is concluded that the automated systems for brain lesion detection can be utilized as a part of routine clinical practice to help the medical professionals in determining the exact location and area of lesions in brain MRI images.

Colored Representation of Brain Gray Scale MRI Images to Potentially Underscore the Variability and Sensitivity of Images

Objective: The drive of this research activity is to report a colorization method to enhance the visualization, cell characterization and interpretation of brain Magnetic Resonance (MR) images, indicative of the underlying physical density of bio tissues. Background: In daily health care environments, medical scanners are being used to generate gray scale images of anatomical structures. Method: Severity of diseases is determined from this luminance component only. Irrefutably, if this high dimensional gray scale, medical data is visualized in colored versions, the definitive and more accurate the pathological assessment process will be. A number of methods have been reported to represent brain Magnetic Resonance Images (MRI) data in color with the cost of computational complexity. In this research work, an efficient method of colorization using frequencies from visible range of color spectrum, has been proposed to embody the variations and sensitivity of the brain MRI images. Conclusion: Side by side, visual comparison based on multiple MRI sequences of identical subjects, by domain experts, proved adequate success and fruitfulness of the story. Keywords: Brain, MRI, enhancement, colorization of soft tissues, gray scale images, visualization.

Increased white matter connectivity seen in young judo athletes with MRI

AIM: To compare the structural and functional differences in the brains of children who practise judo against controls using diffusion tensor imaging (DTI).MATERIALS AND METHODS: This prospective study was approved by the Institutional Review Board. Young judo athletes (n = 14; 17-21 years; mean, 19 years) and age-matched healthy controls (n = 7; 16-21 years; mean, 19 years) underwent standard structural brain magnetic resonance imaging (MRI) and DTI using a 3 T MRI system. Brain MRI images were reviewed for evidence of injury or structural anomalies. Tractography and the fractional anisotropy (FA) of white matter tracts were calculated. Between groups comparison was conducted using two-sample t-test.RESULTS: The brains of the young judo athletes were structurally normal. Significantly increased FA, up to 18% in the internal capsule (p<0.001), 22% in the genu of corpus callosum (p<0.001), 13% in the trunk of corpus callosum (p<0.005), 16% in the splenium of corpus callosum (p<0.02) and 16% in the superior longitudinal fasciculus (p = 0.001), were found in the judo athletes compared to controls.CONCLUSION: DTI demonstrates that children who participate in judo have generalised increased FA compared to children with a less physically active lifestyle. (C) 2018 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

MRI Image Segmentation Using Conditional Spatial FCM Based on Kernel-Induced Distance Measure

Fuzzy C-means (FCM) clustering is the widest spread clustering approach for medical image segmentation because of its robust characteristics for data classification. But, it does not fully utilize the spatial information and is therefore very sensitive to noise and intensity inhomogeneity in magnetic resonance imaging (MRI). In this paper, we propose a conditional spatial kernel fuzzy C-means (CSKFCM) clustering algorithm to overcome the mentioned problem. The approach consists of two successive stages. First stage is achieved through the incorporation of local spatial interaction among adjacent pixels in the fuzzy membership function imposed by an auxiliary variable associated with each pixel. The variable describes the involvement level of each pixel for construction of membership functions and different clusters. Then, we adapted a kernel-induced distance to replace the original Euclidean distance in the FCM, which is shown to be more robust than FCM. The problem of sensitivity to noise and intensity inhomogeneity in MRI data is effectively reduced by incorporating a kernel-induced distance metric and local spatial information into a weighted membership function. The experimental results show that the proposed algorithm has advantages in accuracy and robustness against noise in comparison with the FCM, SFCM and CSFCM methods on MRI brain images.

Simultaneous single- and multi-contrast super-resolution for brain MRI images based on a convolutional neural network

In magnetic resonance imaging (MRI), the acquired images are usually not of high enough resolution due to constraints such as long sampling times and patient comfort. High-resolution MRI images can be obtained by super-resolution techniques, which can be grouped into two categories: single-contrast super-resolution and multi-contrast super-resolution, where the former has no reference information, and the latter applies a high-resolution image of another modality as a reference. In this paper, we propose a deep convolutional neural network model, which performs single- and multi-contrast super-resolution reconstructions simultaneously. Experimental results on synthetic and real brain MRI images show that our convolutional neural network model outperforms state-of-the-art MRI super-resolution methods in terms of visual quality and objective quality criteria such as peak signal-to-noise ratio and structural similarity.

Optimized Fuzzy Clustering Algorithms for Brain MRI Image Segmentation Based on Local Gaussian Probability and Anisotropic Weight Models

Brain Magnetic Resonance Imaging (MRI) image segmentation is one of the critical technologies of clinical medicine, and is the basis of three-dimensional reconstruction and downstream analysis between normal tissues and diseased tissues. However, there are various limitations in brain MRI images, such as gray irregularities, noise, and low contrast, reducing the accuracy of the brain MRI images segmentation. In this paper, we propose two optimization solutions for the fuzzy clustering algorithm based on local Gaussian probability fuzzy C-means (LGP-FCM) model and anisotropic weight fuzzy C-means (AW-FCM) model and apply it in brain MRI image segmentation. An FCM clustering algorithm is proposed based on AW-FCM. By introducing the new neighborhood weight calculation method, each point has the weight of anisotropy, effectively overcomes the influence of noise on the image segmentation. In addition, the LGP model is introduced in the objective function of fuzzy clustering, and a fuzzy clustering segmentation algorithm based on LGP-FCM is proposed. A clustering segmentation algorithm of adaptive scale fuzzy LGP model is proposed. The neighborhood scale corresponding to each pixel in the image is automatically estimated, which improves the robustness of the model and achieves the purpose of precise segmentation. Extensive experimental results demonstrate that the proposed LGP-FCM algorithm outperforms comparison algorithms in terms of sensitivity, specificity and accuracy. LGP-FCM can effectively segment the target regions from brain MRI images.

Similarity Measure-Based Possibilistic FCM With Label Information for Brain MRI Segmentation.

Magnetic resonance imaging (MRI) is extensively applied in clinical practice. Segmentation of the MRI brain image is significant to the detection of brain abnormalities. However, owing to the coexistence of intensity inhomogeneity and noise, dividing the MRI brain image into different clusters precisely has become an arduous task. In this paper, an improved possibilistic fuzzy c -means (FCM) method based on a similarity measure is proposed to improve the segmentation performance for MRI brain images. By introducing the new similarity measure, the proposed method is more effective for clustering the data with nonspherical distribution. Besides that, the new similarity measure could alleviate the "cluster-size sensitivity" problem that most FCM-based methods suffer from. Simultaneously, the proposed method could preserve image details as well as suppress image noises via the use of local label information. Experiments conducted on both synthetic and clinical images show that the proposed method is very effective, providing mitigation to the cluster-size sensitivity problem, resistance to noisy images, and applicability to data with more complex distribution.

An Improvement of MRI Brain Images Classification Using Dragonfly Algorithm as Trainer of Artificial Neural Network

Computer software is frequently used for medical decision support systems in different areas. Magnetic Resonance Images (MRI) are widely used images for brain classification issue. This paper presents an improved method for brain classification of MRI images. The proposed method contains three phases, which are, feature extraction, dimensionality reduction, and an improved classification technique. In the first phase, the features of MRI images are obtained by discrete wavelet transform (DWT). In the second phase, the features of MRI images have been reduced, using principal component analysis (PCA). In the last (third) stage, an improved classifier is developed. In the proposed classifier, Dragonfly algorithm is used instead of backpropagation as training algorithm for artificial neural network (ANN). Some other recent training-based Neural Networks, SVM, and KNN classifiers are used for comparison with the proposed classifier. The classifiers are utilized to classify image as normal or abnormal MRI human brain image. The results show that the proposed classifier is outperformed the other competing classifiers.

Automatic brain tissue segmentation based on graph filter

BackgroundAccurate segmentation of brain tissues from magnetic resonance imaging (MRI) is of significant importance in clinical applications and neuroscience research. Accurate segmentation is challenging due to the tissue heterogeneity, which is caused by noise, bias filed and partial volume effects.MethodsTo overcome this limitation, this paper presents a novel algorithm for brain tissue segmentation based on supervoxel and graph filter. Firstly, an effective supervoxel method is employed to generate effective supervoxels for the 3D MRI image. Secondly, the supervoxels are classified into different types of tissues based on filtering of graph signals.ResultsThe performance is evaluated on the BrainWeb 18 dataset and the Internet Brain Segmentation Repository (IBSR) 18 dataset. The proposed method achieves mean dice similarity coefficient (DSC) of 0.94, 0.92 and 0.90 for the segmentation of white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF) for BrainWeb 18 dataset, and mean DSC of 0.85, 0.87 and 0.57 for the segmentation of WM, GM and CSF for IBSR18 dataset.ConclusionsThe proposed approach can well discriminate different types of brain tissues from the brain MRI image, which has high potential to be applied for clinical applications.

Predicting CT Image From MRI Data Through Feature Matching With Learned Nonlinear Local Descriptors.

Attenuation correction for positron-emission tomography (PET)/magnetic resonance (MR) hybrid imaging systems and dose planning for MR-based radiation therapy remain challenging due to insufficient high-energy photon attenuation information. We present a novel approach that uses the learned nonlinear local descriptors and feature matching to predict pseudo computed tomography (pCT) images from T1-weighted and T2-weighted magnetic resonance imaging (MRI) data. The nonlinear local descriptors are obtained by projecting the linear descriptors into the nonlinear high-dimensional space using an explicit feature map and low-rank approximation with supervised manifold regularization. The nearest neighbors of each local descriptor in the input MR images are searched in a constrained spatial range of the MR images among the training dataset. Then the pCT patches are estimated through k-nearest neighbor regression. The proposed method for pCT prediction is quantitatively analyzed on a dataset consisting of paired brain MRI and CT images from 13 subjects. Our method generates pCT images with a mean absolute error (MAE) of 75.25 ± 18.05 Hounsfield units, a peak signal-to-noise ratio of 30.87 ± 1.15 dB, a relative MAE of 1.56 ± 0.5% in PET attenuation correction, and a dose relative structure volume difference of 0.055 ± 0.107% in , as compared with true CT. The experimental results also show that our method outperforms four state-of-the-art methods.

Multi-Modality Cascaded Convolutional Neural Networks for Alzheimer’s Disease Diagnosis

Accurate and early diagnosis of Alzheimer's disease (AD) plays important role for patient care and development of future treatment. Structural and functional neuroimages, such as magnetic resonance images (MRI) and positron emission tomography (PET), are providing powerful imaging modalities to help understand the anatomical and functional neural changes related to AD. In recent years, machine learning methods have been widely studied on analysis of multi-modality neuroimages for quantitative evaluation and computer-aided-diagnosis (CAD) of AD. Most existing methods extract the hand-craft imaging features after image preprocessing such as registration and segmentation, and then train a classifier to distinguish AD subjects from other groups. This paper proposes to construct cascaded convolutional neural networks (CNNs) to learn the multi-level and multimodal features of MRI and PET brain images for AD classification. First, multiple deep 3D-CNNs are constructed on different local image patches to transform the local brain image into more compact high-level features. Then, an upper high-level 2D-CNN followed by softmax layer is cascaded to ensemble the high-level features learned from the multi-modality and generate the latent multimodal correlation features of the corresponding image patches for classification task. Finally, these learned features are combined by a fully connected layer followed by softmax layer for AD classification. The proposed method can automatically learn the generic multi-level and multimodal features from multiple imaging modalities for classification, which are robust to the scale and rotation variations to some extent. No image segmentation and rigid registration are required in pre-processing the brain images. Our method is evaluated on the baseline MRI and PET images of 397 subjects including 93AD patients, 204 mild cognitive impairment (MCI, 76 pMCI +128 sMCI) and 100 normal controls (NC) from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results show that the proposed method achieves an accuracy of 93.26% for classification of AD vs. NC and 82.95% for classification pMCI vs. NC, demonstrating the promising classification performance.