Brain Magnetic Resonance Imaging Images Research Articles

Intelligent Diagnosis Method of MRI Brain Image Using Parallel Self-Organizing Feature Maps Neural Network

Advances in medical imaging skills have promoted the influence of medical imaging in neuroscience. Having advanced medical imaging technology is essential for the medical industry. Magnetic resonance imaging (MRI) plays a central role in medical imaging. It plays a key role in the treatment of various human diseases. Doctors analyze brain size, shape, and location in brain MR images to assess brain disease and develop a medical plan. The manual division of brain tissue by experts is heavy and subjective. Therefore, the study of automatic segmentation of brain MR images has practical significance. Because the characteristics of brain MRI images are low contrast and high noise, which seriously affects the accuracy of image segmentation, the current image segmentation methods have some limitations in this application. In this paper, multiple self-organizing feature maps neural network (SOM-NN) are utilized to construct a parallel self-organizing feature maps neural network (PSOM-NN), which converts the segmentation problem of brain images into the classification problem of PSOMNN. The experiments show that SOM has strong self-learning ability in learning and training, and the parallel ability of PSOM-NN model greatly reduces the segmentation time, improves the real-time performance of the model, and helps to realize fully automatic or semi-automatic segmentation of the lesion area. PSOM can promote the improvement of segmentation accuracy and facilitate intelligent diagnosis.

WITHDRAWN: Multi-perspective scaling convolutional neural networks for high-resolution MRI brain image segmentation

Abstract The occurrence of defect over the soft tissues and nervous system is gradually increasing where Magnetic Resonance Imaging (MRI) is the most preferred method for performing the examination. The brain tumor MR image segmentation performs functionalities like image reconstruction of affected (diseased tissues) and qualitative analysis of infected and normal tissues. The image segmentation accuracy with the physician’s perspective relies over the shape, size, and location of lesions tissues, appropriate diagnostic strategies, and disease determination. The outcomes of this investigation rely over Multi-Perspective Scaling Convolutional Neural Networks (MPS-CNN) model for segmenting brain tumors more effectually and accurately. The multi-scale inputs are given to the proposed CNN model to overcome the necessity to select the appropriate input scale based on the tumor size, neighborhood tumor analysis based on scaled images, and adoption towards various tumor sizes. Therefore, the segmentation accuracy can be increased based on the input multi-scale brain tumor images. Also, the faster segmentation with multi-scaling process accelerates the speed of ensuring real-time segmentation process. This scaling process can effectively segment the brain images in the MRI which enhances the generalization process. It is utilized for predicting the brain lesion tissue of MRI. The simulation is carried out in MATLAB environment. The anticipated MPS-CNN is compared with prevailing approaches like CNN, FCN, U-Net, SegNet, Deep V3, and Deep FCN. And the MPS-CNN shows better trade-off in contrary to other approaches.

Validation of Segmented Brain Tumor from MRI Images Using 3D Printingthe.

Background:Early diagnosis of a brain tumor is important for improving the treatment possibilities. Manually segmenting the tumor from the volumetric data is time-consuming, and the visualization of the tumor is rather challenging. Methods:This paper proposes a user-guided brain tumour segmentation from MRI (Magnetic Resonance Imaging) images developed using Medical Imaging Interaction Toolkit (MITK) and printing the segmented object using the 3D printer for tumour quantification. The proposed method includes segmenting the tumour interactively using connected threshold method, then printing the physical object from the segmented volume of interest. Then the distance between two voxels was measured using electronic callipers on the 3D volume in a specific direction. And next, the same distance was measured in the same direction on the 3D printed object. Results:The technique was tested with n=5 samples (20 readings) of brain MRI images from RIDER Neuro MRI dataset of National Cancer Institute. MITK provides various tools that enable image visualization, registration, and contouring. We were able to achieve the same measurements using both the approaches and this has been tested statistically with paired t-test method. Through this and the observer’s opinion, the accuracy of the segmentation was proved. Conclusion:When the difference in measurement of tumor volume through the electronic calipers and with 3D printed object equates to zero, proves that the segmentation technique is accurate. This helps to delineate the tumor more accurately during radio therapy.

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.

Real-time classification of brain tumors in MRI images with a convolutional operator-based hidden Markov model

Classification of brain tumors based on the brain magnetic resonance imaging (MRI) results of patients has become an important problem in medical image processing. A computer program that can efficiently analyze brain MRI images of patients in real time and generate accurate classification results of the tumors in these images can significantly reduce the amount of time needed for diagnosis, which may increase the chances for patients to survive. This paper proposes a new statistical method that can accurately classify three types of brain tumors based on MRI images, the three types of tumors considered include pituitary tumor, glioma, and meningioma. The features for a pixel in an MRI image are obtained by applying a set of convolutional operators to the neighborhood area of the pixel. For training, a hidden Markov model (HMM) is constructed and trained from a training dataset by computing a statistical profile for the feature vectors for pixels in the tumor regions of each type of brain tumors. In addition, a statistical profile is also obtained for pixels that are in the background of a tumor. For classification, the trained HMM is used to assign labels to pixels in an MRI image with a dynamic programming approach and the classification result of the image is obtained from the labels assigned to the tumor region. Both the training and classification processes can be efficiently performed in linear time and does not require the availability of a large amount of computational resources. Experimental results on a large dataset of MRI images show that the proposed method can provide classification results with high accuracy for all three types of brain tumors. A comparison with state-of-the-art methods for brain tumor classification suggests that the proposed method can achieve improved classification accuracy. In addition, real-time analysis also reveals that the proposed approach can probably be used for real-time classification of brain tumors.

Fast Fractal Coding of MRI Images using Deep Reinforcement Learning

This paper presents an algorithm based on Fractal theory by using Iterated Function Systems (IFS). An efficient and fast coding mechanism is proposed by exploiting the self similarity nature in the Brain MRI images. The proposed algorithm utilizes Deep Reinforcement Learning (DRL) technique to learn the transformations required to recreate the original image. We avail of the Adaptive Iterated Function System (AIFS) as the encoding scheme. The proposed algorithm is trained and customized to compress the Medical images, especially Magnetic Resonance Imaging (MRI). The algorithm is tested and evaluated by using the original MR head scan test images. It learns from an existing biomedical dataset viz The Internet Brain Segmentation Repository (IBSR) to predict the new local affine transformations. The empirical analysis shows that the proposed algorithm is at least 4 times faster than the competitive methods and the decoding quality is far distinct with a reduction in the bit rate.

Automated Multimodal image fusion for brain tumor detection

In medical domain, various multimodalities such as Computer tomography (CT) and Magnetic resonance imaging (MRI) are integrated into a resultant fused image. Image fusion (IF) is a method by which vital information can be preserved by extracting all important information from the multiple images into the resultant fused image. The analytical and visual image quality can be enhanced by the integration of different images. In this paper, a new algorithm has been proposed on the basis of guided filter with new fusion rule for the fusion of different imaging modalities such as MRI and Fluorodeoxyglucose images of brain for the detection of tumor. The performance of the proposed method has been evaluated and compared with state-of-the-art image fusion techniques using various qualitative as well as quantitative evaluation metrics. From the results, it has been observed that more information has achieved on edges and content visibility is also high as compared to the other techniques which makes it more suitable for real applications. The experimental results are evaluated on the basis of with-reference and without-references metric such as standard deviation, entropy, peak signal to noise ratio, mutual information etc.

An Efficient Classification of MRI Brain Images

The unprecedented improvements in computing capabilities and the introduction of advanced techniques for the analysis, interpretation, processing, and visualization of images have greatly diversified the domain of medical sciences and resulted in the field of medical imaging. The Magnetic Resonance Imaging (MRI), an advanced imaging technique, is capable of producing high quality images of the human body including the brain for diagnosis purposes. This paper proposes a simple but efficient solution for the classification of MRI brain images into normal, and abnormal images containing disorders and injuries. It uses images with brain tumor, acute stroke and alzheimer, besides normal images, from the public dataset developed by harvard medical school, for evaluation purposes. The proposed model is a four step process, in which the steps are named: 1). Pre-processing, 2). Features Extraction, 3). Features Reduction, and 4). Classification. Median filter, being one of the best algorithms, is used for the removal of noise such as salt and pepper, and unwanted components such as scalp and skull, in the pre-processing step. During this stage, the images are converted from gray scale to colored images for further processing. In second step, it uses Discrete Wavelet Transform (DWT) technique to extract different features from the images. In third stage, Color Moments (CMs) are used to reduce the number of features and get an optimal set of characteristics. Images with the optimal set of features are passed to different classifiers for the classification of images. The Feed Forward - ANN (FF-ANN), an individual classifier, which was given a 65% to 35% split ratio for training and testing, and hybrid classifiers called: Random Subspace with Random Forest (RSwithRF) and Random Subspace with Bayesian Network (RSwithBN), which used 10-Fold cross validation technique, resulted in 95.83%, 97.14% and 95.71% accurate classification, in corresponding order. These promising results show that the proposed method is robust and efficient, in comparison with, existing classification methods in terms of accuracy with smaller number of optimal features.

Super-resolution reconstruction of brain magnetic resonance images via lightweight autoencoder

Magnetic Resonance Imaging (MRI) is useful to provide detailed anatomical information such as images of tissues and organs within the body that are vital for quantitative image analysis. However, typically the MR images acquired lacks adequate resolution because of the constraints such as patients’ comfort and long sampling duration. Processing the low resolution MRI may lead to an incorrect diagnosis. Therefore, there is a need for super resolution techniques to obtain high resolution MRI images. Single image super resolution (SR) is one of the popular techniques to enhance image quality. Reconstruction based SR technique is a category of single image SR that can reconstruct the low resolution MRI images to high resolution images. Inspired by the advanced deep learning based SR techniques, in this paper we propose an autoencoder based MRI image super resolution technique that performs reconstruction of the high resolution MRI images from low resolution MRI images. Experimental results on synthetic and real brain MRI images show that our autoencoder based SR technique surpasses other state-of-the-art techniques in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), Information Fidelity Criterion (IFC), and computational time.

Hybrid Learning Model for Metal Artifact Reduction

In today’s healthcare, the human brain imaging is done for finding the tumors and other disorders of the brain. The Magnetic Resonance Imaging (MRI) plays a significant role throughout the complete clinical procedure starting from diagnostics and treatment planning to surgical processes and follow up studies. The MRI of brain allows the clinical expert for the earliest detection and treatment of brain abnormality or any neurological diseases, which is the most treatable stage that gives patients the greatest chance of survival. An artifact is a feature appearing in an image which is not present in the original imaged object. The types of artifacts are herringbone artifact, zipper artifact, motion artifact, aliasing artifact, chemical shift artifact, magnetic susceptibility artifact, central point artifact, Gibbs ringing artifact and intensity inhomogeneity artifact. After segmentation, the features are extracted using Gray level co-occurrence matrix (GLCM) and an CNN, Deep belief network, Proposed hybrid model (Based on CNN and Deep belief network (DBN)) and Morphological Technique with Segmentation Techniques is implemented to classify the brain MRI images as either normal (without tumor) or abnormal (with tumor). Proposed hybrid model for metal artifact reduction and represent though the experiment our proposed model very effective to existing one. Results in Accuracy (in %) Before artifact removal(92.12%), After artifact removal (95.77%)

A 3D multiscale view convolutional neural network with attention for mental disease diagnosis on MRI images.

Computer Assisted Diagnosis (CAD) based on brain Magnetic Resonance Imaging (MRI) is a popular research field for the computer science and medical engineering. Traditional machine learning and deep learning methods were employed in the classification of brain MRI images in the previous studies. However, the current algorithms rarely take into consideration the influence of multi-scale brain connectivity disorders on some mental diseases. To improve this defect, a deep learning structure was proposed based on MRI images, which was designed to consider the brain's connections at different sizes and the attention of connections. In this work, a Multiscale View (MV) module was proposed, which was designed to detect multi-scale brain network disorders. On the basis of the MV module, the path attention module was also proposed to simulate the attention selection of the parallel paths in the MV module. Based on the two modules, we proposed a 3D Multiscale View Convolutional Neural Network with Attention (3D MVA-CNN) for classification of MRI images for mental disease. The proposed method outperformed the previous 3D CNN structures in the structural MRI data of ADHD-200 and the functional MRI data of schizophrenia. Finally, we also proposed a preliminary framework for clinical application using 3D CNN, and discussed its limitations on data accessing and reliability. This work promoted the assisted diagnosis of mental diseases based on deep learning and provided a novel 3D CNN method based on MRI data.

Morphological Study on Magnetic Resonance Imaging of the Normal Pituitary Gland in Nepalese Population: Age and Sex-Related Changes

Introduction: The pituitary gland is the master endocrine gland that secretes important hormones and controls other glands. Its size, shape and volume vary with age and sex and also in various pathological conditions. This study was conducted to identify the size, shape and volume of the normal pituitary gland in different age groups of both genders using Magnetic Resonance Imaging (MRI).Methods: A retrospective study of the brain MRI Images of 203 patients was done in the Radiology and Imaging department of Pokhara Academy of Health Sciences, Nepal. Anterior-posterior dimension, transverse dimension, height and shape of the pituitary gland were noted and volume was calculated. Descriptive analysis and relationship of pituitary parameters with age and sex were assessed using ANOVA and Chi-square tests.Results: The pituitary height and volume was maximum in the 11-20years age group. Similarly, the mean height and volume of the pituitary gland in female patients of each age group were greater than that of male patients in the same age group. The inferential analysis revealed significant differences for mean pituitary height and volume with age but not with gender. The upper surface convexity was maximum in the age group 11-20years.Conclusion: The study has shown a clear trend with regards to the morphology of the pituitary gland with the age and sex of the individual highlighting the size, shape and volume of the pituitary gland should be taken into consideration while evaluating the glands for any neuroendocrine disorders.

Detection and diagnosis of brain tumors using deep learning convolutional neural networks

AbstractThe detection of brain tumors in brain magnetic resonance imaging (MRI) image is an important process for preventing earlier death. This article proposes an automated computer aided method for detecting and locating the brain tumors in brain MRI images using deep learning algorithms. The proposed method has three sub modules as preprocessing, classifications and segmentation. In this article, data augmentation is used as preprocessing method. The preprocessed brain MRI images are classified into either tumor case or nontumor case using classification approach. In this brain tumor detection and segmentation process, convolutional neural networks (CNNs) classification architecture is used for classifying the brain images. The morphological based segmentation methodology is used in this article for segmenting the tumor regions in classified brain images. Further, the segmented tumor regions are diagnosed into “Mild” and “Severe” case using CNN architecture. The proposed methodology is applied on the brain images from open access dataset. The performance of the proposed system is analyzed in terms of sensitivity, specificity, and precision, F‐score, Disc similarity index and tumor region segmentation accuracy on set of brain images. The simulation results of this proposed framework are verified by expert radiologist.

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.

Image Based Group Classifier for Brain Tumor Detection Using Machine Learning Technique

In view of insights of the Central Brain Tumor Registry of the United States (CBTRUS), brain tumor is one of the main sources of disease related deaths in the World. It is the subsequent reason for tumor related deaths in adults under the age 20-39. Magnetic Resonance Imaging (MRI) is assuming a significant job in the examination of neuroscience for contemplating brain images. The investigation of brain MRI Images is useful in brain tumor analysis process. Features will be extricated and selected from the segmented pictures and afterward grouped by utilizing the classification procedures to analyze whether the patient is ordinary (having no tumor) or irregular (having tumor). One of the most dangerous cancers is brain tumor or cancer which affects the human body's main nervous system. Infection that can affect is very sensitive to the brain. Two types of brain tumors are present. The tumor may be categorized as benign and malignant. The benign tumor represents a change in the shape and structure of the cells, but cannot contaminate or spread to other cells in the brain. The malignant tumor can spread and grow if not carefully treated and removed. The detection of brain tumors is a difficult and sensitive task involving the classifier's experience. In the proposed work a Group based Classifier for Brain Tumor Recognition (GbCBTD) is introduced for the efficient segmentation of MRI images and for identification of tumor. The use of Convolutional Neural Network (CNN) system to classify the brain tumor type is presented in this work. Relevant features are extracted from images and by using CNN with machine learning technique, tumor can be recognized. CNN can reduce the cost and increase the performance of brain tumor detection. The proposed work is compared to the traditional methods and the results show that the proposed method is effective in detecting tumors.

Clustering of Brain Tumors in Brain MRI Images based on Extraction of Textural and Statistical Features

<p>The purpose of this article is to investigate techniques for classifying tumor grade from magnetic resonance imaging (MRI). This requires early diagnosis of the brain tumor and its grade. Magnetic resonance imaging may show a clear tumor in the brain, but doctors need to measure the tumor in order to treat more or to advance treatment. For this purpose, digital imaging techniques along with machine learning can help to quickly identify tumors and also treatments and types of surgery. These combined techniques in understanding medical images for researchers are an important tool to increase the accuracy of diagnosis. In this paper, classification methods for MRI images of tumors of the human brain are performed to review the astrocytoma-containing glands. Methods used to classify brain tumors, including preprocessing, screening, tissue extraction, and statistical features of the tumor using two types of T<sub>1</sub>W and Flair brain MRI images and also the method of dimensionality reduction of extracted features and how to train them in classification are also explained. Determine the tumor area using three classification of Fuzzy Logic <em>C</em><em>-</em><em>Means</em><em> </em>Clustering (FCM), Probabilistic Neural Networks (PNN) and Support Vector Machines (SVM). In this paper, simulated and real MRI images are used. The results obtained from the proposed methods in this paper are compared with the reference results and the results show that the proposed approach can increase the reliability of brain tumor diagnosis.</p>

Automated Brain Tumor Detection Based on Feature Extraction from The MRI Brain Image Analysis

The brain tumors are among the common deadly illness that requires early, reliable detection techniques, current identification, and imaging methods that depend on the decisions of neuro-specialists and radiologists who can make possible human error. This takes time to manually identify a brain tumor. This work aims to design an intelligent model capable of diagnosing and predicting the severity of magnetic resonance imaging (MRI) brain tumors to make an accurate decision. The main contribution is achieved by adopting a new multiclass classifier approach based on a collected real database with new proposed features that reflect the precise situation of the disease. In this work, two artificial neural networks (ANNs) methods namely, Feed Forward Back Propagation neural network (FFBPNN) and support vector machine (SVM), used to expectations the level of brain tumors. The results show that the prediction result by the (FFBPN) network will be better than the other method in time record to reach an automatic classification with classification accuracy was 97% for 3-class which is considered excellent accuracy. The software simulation and results of this work have been implemented via MATLAB (R2012b).

Automated Categorization of Brain Tumor from MRI Using CNN features and SVM

Automated tumor characterization has a prominent role in the computer-aided diagnosis (CAD) system for the human brain. Despite being a well-studied topic, CAD of brain tumors poses severe challenges in some specific aspects. One such challenging problem is the category-based classification of brain tumors among glioma, meningioma, and pituitary tumors using magnetic resonance imaging (MRI) images. The emergence of deep learning and machine learning algorithms have addressed image classification tasks with promising results. But an associated limitation with the medical image classification is the small sizes of medical image databases. This limitation, in turn, limits the availability of medical images for training deep neural networks. To mitigate this challenge, we adopt a combination of convolutional neural network (CNN) features with support vector machine (SVM) for classification of the medical images. The fully automated system is evaluated using Figshare open dataset containing MRI images for the three types of brain tumors. CNN is designed to extract features from brain MRI images. For enhanced performance, a multiclass SVM is used with CNN features. Testing and evaluation of the integrated system followed a fivefold cross-validation procedure. The proposed model attained an overall classification accuracy of 95.82%, better than the state-of-the-art method. Extensive experiments are performed on other MRI datasets for the brain to ascertain the improved performance of the proposed system. When the amount of available training data is small, the SVM classifier is observed to perform better than the softmax classifier for the CNN features. Compared to transfer learning-based classification, the adopted strategy of CNN-SVM has lesser computations and memory requirements.

Twenty-Four-Hour Ambulatory Blood Pressure Variability Associated With Cerebral Small Vessel Disease MRI Burden and Its Progression in Inpatients With Cerebrovascular Disease.

Background: Lacunar infarcts, white matter lesions, cerebral microbleed, enlarged perivascular space and brain atrophy are regarded as magnetic resonance imaging (MRI) manifestations of cerebral small vessel disease (cSVD). 24-hour blood pressure variability (BPV) has been reported to relate with cerebral small vessel disease, but the impact of 24-h BPV on the total MRI cSVD burden and its progression in inpatients with cerebrovascular disease has not been investigated yet.Methods: We enrolled inpatients with cerebrovascular disease, who underwent the 24-h ambulatory blood pressure monitoring (ABPM) and the brain MRI scan at baseline and had the follow-up brain MRI images stored in the clinical information system of our hospital. BPV was quantified by the calculation of standard deviation (SD), coefficient of variation (CV), weighted standard deviation (wSD) of blood pressure record. We evaluated the total cSVD score on baseline MRI and the MRI followed-up to obtain the total burden of cSVD. The cSVD burden progression was estimated through the comparison of the total cSVD score on the two MRIs.Results: A total of 140 patients with an average age of 65.6 years were finally enrolled, 82.9% (116/140) of whom had one or more cSVD markers. After a median of 4.4 years follow-up, cSVD score progression were found in 50.7% (71/140) of the patients. Both SD and CV of SBP and DBP during 24-h and daytime as well as the SBP wSD differed significantly among different total cSVD score groups. The SBP SD and CV during 24-h and daytime, the SBP SD in nighttime, the DBP SD and CV during the daytime were significantly higher in the cSVD progression group than those in the cSVD no-progression group. The SBP wSD and the DBP wSD were significantly higher in the cSVD progression group than those in the cSVD no-progression group. Logistic regression analyses revealed that daytime SBP SD and SBP wSD were independent risk factors for total cSVD burden [daytime SBP SD: OR = 1.628, 95% CI = 1.105–2.398 (per 5 mmHg increase in SD), P = 0.014; SBP wSD: OR = 2.248, 95% CI = 1.564–3.230 (per 5 mmHg increase in wSD), P < 0.001)] and SBP wSD was a significant predictor for cSVD progression [OR = 2.990, 95% CI = 1.053–8.496 (per 5 mmHg increase in wSD), P = 0.040].Conclusion: Higher BPV were significantly related with total cSVD burden in inpatients with cerebrovascular disease. SBP SD during daytime and SBP wSD were independent risk factor for total cSVD burden and SBP wSD was an predictive factor for cSVD progression.

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.