scholarly journals Bipartite graph modeling of Alzheimer’s disease and its early automated discrimination through region-based level set algorithm and support vector machine in magnetic resonance brain images

2021 ◽  
Vol 20 (3) ◽  
pp. 356-363
Author(s):  
V. Yegnanarayanan ◽  
◽  
M. Anisha ◽  
T. Arun Prasath ◽  
◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Bipartite Graph ◽  
General Procedure ◽  
Brain Network ◽  
Magnetic Resonance Images ◽  
Support Vector ◽  
Brain Images ◽  
Cad System ◽  
Graph Modeling

This paper offers a bird’s eye perception of how bipartite graph modeling could help to comprehend the progression of Alzheimer Disease (AD). We will also discuss the role of the various software tools available in the literature to identify the bipartite structure in AD affected patient brain networks and a general procedure to generate a graph from the AD brain network. Further, as AD is a minacious disorder that leads to the progressive decline of memory and physical ability we resort to Computer-Aided Diagnosis. It has a vital part in the preliminary estimation and finding of AD. We propose an approach to become aware of AD particularly in its beginning phase by analyzing the measurable variations in the hippocampus, grey matter, cerebrospinal fluid and white matter of the brain from Magnetic resonance images. Hence an appropriate segmentation and categorization methods are projected to detect the presence of AD. The trials were carried out on Magnetic resonance images to distinguish from the section of interest. The effectiveness of the CAD system was experimentally evaluated from the images considered from publicly available databases. Obtained findings recommend that the established CAD system has boundless prospective and great guarantee for the prognosis of AD.

scholarly journals Cognitive States from Brain Images : SVM Approach

2012 ◽  
pp. 139-144
Author(s):  
Ramakrushna Swain ◽  
Lambodar Jena ◽  
Narendra K. Kamila
Keyword(s):  
Magnetic Resonance ◽  
Human Brain ◽  
Temporal Correlation ◽  
Magnetic Resonance Images ◽  
Support Vector ◽  
Brain Images ◽  
Cognitive States ◽  
Fmri Study ◽  
New Instrument ◽  
Spatio Temporal

As the field of functional human brain mapping has matured, it has become apparent that a comprehensive understanding of the human brain, and its relationship with cognition, will require a quantitative assessment of individual differences in both brain function and structure. To assess brain structure, accurate classification of magnetic resonance images needed. In recent years, functional Magnetic Resonance Imaging (fMRI) has emerged as a powerful new instrument to collect vast quantities of data about activity in the human brain. As in other modern empirical sciences, this new instrumentation has led to a flood of new data and a corresponding need for new data analysis methods. A standard fMRI study gives rise to massive amounts of noisy data with a complicated spatio-temporal correlation structure. In this paper we discuss the analysis of fMRI data, from the angle of support vector machine classification for analysis of complex, multivariate data.

scholarly journals Morphological, Structural, and Functional Networks Highlight the Role of the Cortical-Subcortical Circuit in Individuals With Subjective Cognitive Decline

2021 ◽  
Vol 13 ◽  
Author(s):  
Xiaowen Xu ◽  
Tao Wang ◽  
Weikai Li ◽  
Hai Li ◽  
Boyan Xu ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Cognitive Decline ◽  
Brain Networks ◽  
Subjective Cognitive Decline ◽  
Support Vector ◽  
Functional Networks ◽  
Resonance Imaging ◽  
Potential Biomarkers

Subjective cognitive decline (SCD) is considered the earliest stage of the clinical manifestations of the continuous progression of Alzheimer’s Disease (AD). Previous studies have suggested that multimodal brain networks play an important role in the early diagnosis and mechanisms underlying SCD. However, most of the previous studies focused on a single modality, and lacked correlation analysis between different modal biomarkers and brain regions. In order to further explore the specific characteristic of the multimodal brain networks in the stage of SCD, 22 individuals with SCD and 20 matched healthy controls (HCs) were recruited in the present study. We constructed the individual morphological, structural and functional brain networks based on 3D-T1 structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI), respectively. A t-test was used to select the connections with significant difference, and a multi-kernel support vector machine (MK-SVM) was applied to combine the selected multimodal connections to distinguish SCD from HCs. Moreover, we further identified the consensus connections of brain networks as the most discriminative features to explore the pathological mechanisms and potential biomarkers associated with SCD. Our results shown that the combination of three modal connections using MK-SVM achieved the best classification performance, with an accuracy of 92.68%, sensitivity of 95.00%, and specificity of 90.48%. Furthermore, the consensus connections and hub nodes based on the morphological, structural, and functional networks identified in our study exhibited abnormal cortical-subcortical connections in individuals with SCD. In addition, the functional networks presented more discriminative connections and hubs in the cortical-subcortical regions, and were found to perform better in distinguishing SCD from HCs. Therefore, our findings highlight the role of the cortical-subcortical circuit in individuals with SCD from the perspective of a multimodal brain network, providing potential biomarkers for the diagnosis and prediction of the preclinical stage of AD.

Abstract T P32: Computer Aided Detection of Asymptomatic Cerebral Microbleeds on Magnetic Resonance Images

Stroke ◽  
2014 ◽  
Vol 45 (suppl_1) ◽  
Author(s):  
Yan Li ◽  
Yining Huang ◽  
Jue Zhang ◽  
Jing Fang
Keyword(s):  
Magnetic Resonance ◽  
Rating Scale ◽  
Characteristic Curve ◽  
Magnetic Resonance Images ◽  
False Positives ◽  
Cerebral Microbleeds ◽  
Cad System ◽  
Automated Method ◽  
Skull Stripping ◽  
Computer Aided

Purpose: Manual rating of Cerebral microbleeds (CMBs) is time-consuming and inconsistent. Since the presence and number of CMBs have become a potential diagnostic and prognostic biomarker of stroke, an automatic identification method is required. We proposed a computer aided diagnosis (CAD) system for the detection of the CMBs on the magnetic resonance (MR) images automatically. Methods: Eighty-one patients were recruited in this study. CMBs on the MR T2* weighted images were manually rated according to the Microbleed Anatomic Rating Scale (MARS) criteria. Our automated method consisted of two steps: i) Pre-processing: After skull stripping, isolated islands of points were removed while holes were restored to avoid over segmentation. Local threshold segmentation was applied for the initial candidate selection. ii) Identification model: Seven features were extracted from each candidate: area, roundness, intensity, average of the boundary, contrast, shape-intensity and location-mark (according to the probability density templates calculated from the location information of the CMBs). For further identification of each candidate, Random Forest (RF) model was used to distinguish CMBs from the mimics. Results: A total of 337 CMBs in the 81 patients were studied. Comparing with the counting from the experienced doctors, high sensitivity of 92% (310/337) was achieved after pre-processing. The RF model eliminated most of the false-positives while maintaining a reliable sensitivity of 94% (291/310) and specificity of 96% (4272/4450). The area under the Receiver operating characteristic curve was 0.98 ± 0.02 for the detection model. In summary, this CAD system had an overall sensitivity of 86% (291/337) and specificity of 96% (4272/4450), producing only 2.2 false-positives per subject. Conclusion: This presented strategy is technically effective. The results indicate that it has the potential to be used for clinical detection of CMBs.

scholarly journals Effusion attenuates the effect of synovitis on radiographic progression in patients with hand osteoarthritis: a longitudinal magnetic resonance imaging study

2020 ◽  
Vol 40 (1) ◽  
pp. 315-319
Author(s):  
W. Damman ◽  
R. Liu ◽  
M. Reijnierse ◽  
F. R. Rosendaal ◽  
J. L. Bloem ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Radiographic Progression ◽  
Imaging Study ◽  
Magnetic Resonance Images ◽  
High Signal ◽  
Hand Osteoarthritis ◽  
Mr Images ◽  
Contrast Enhanced ◽  
Key Points

AbstractAn exploratory study to determine the role of effusion, i.e., fluid in the joint, in pain, and radiographic progression in patients with hand osteoarthritis. Distal and proximal interphalangeal joints (87 patients, 82% women, mean age 59 years) were assessed for pain. T2-weighted and Gd-chelate contrast-enhanced T1-weighted magnetic resonance images were scored for enhanced synovial thickening (EST, i.e., synovitis), effusion (EST and T2-high signal intensity [hsi]) and bone marrow lesions (BMLs). Effusion was defined as follows: (1) T2-hsi > 0 and EST = 0; or 2) T2-hsi = EST but in different joint locations. Baseline and 2-year follow-up radiographs were scored following Kellgren-Lawrence, increase ≥ 1 defined progression. Associations between the presence of effusion and pain and radiographic progression, taking into account EST and BML presence, were explored on the joint level. Effusion was present in 17% (120/691) of joints, with (63/120) and without (57/120) EST. Effusion on itself was not associated with pain or progression. The association with pain and progression, taking in account other known risk factors, was stronger in the absence of effusion (OR [95% CI] 1.7 [1.0–2.9] and 3.2 [1.7–5.8]) than in its presence (1.6 [0.8–3.0] and 1.3 [0.5–3.1]). Effusion can be assessed on MR images and seems not to be associated with pain or radiographic progression but attenuates the association between synovitis and progression. Key Points• Effusion is present apart from synovitis in interphalangeal joints in patients with hand OA.• Effusion in finger joints can be assessed as a separate feature on MR images.• Effusion seems to be of importance for its attenuating effect on the association between synovitis and radiographic progression.

Computer Aided Interpretation of Fibrous Texture in Hepatic Magnetic Resonance Images

2013 ◽  
Vol 647 ◽  
pp. 325-330 ◽  
Author(s):  
Yu Fan Zeng ◽  
Xue Jun Zhang ◽  
Wen Yan ◽  
Li Ling Long ◽  
Yu Kun Huang ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Hepatic Fibrosis ◽  
Region Of Interest ◽  
Texture Features ◽  
Magnetic Resonance Images ◽  
Optimal Number ◽  
Support Vector ◽  
Mr Images ◽  
Accuracy Rate ◽  
Occurrence Matrix

The fibrous texture in liver is one of important signs for interpreting the chronic liver diseases in radiologists’ routines. In order to investigate the usefulness of various texture features calculated by computer algorithm on hepatic magnetic resonance (MR) images, 15 texture features were calculated from the gray level co-occurrence matrix (GLCM) within a region of interest (ROI) which was selected from the MR images with 6 stages of hepatic fibrosis. By different combination of 15 features as input vectors, the classifier had different performance in staging the hepatic fibrosis. Each combination of texture features was tested by Support Vector Machine (SVM) with leave one case out method. 173 patients’ MR images including 6 stages of hepatic fibrosis were scanned within recent two years. The result showed that optimal number of features was confirmed from 3 to 7 by investigating the classified accuracy rate between each stage/group. It is evident that angular second moment, entropy, sum average and sum entropy played the most significant role in classification.

scholarly journals Hybrid RGSA and Support Vector Machine Framework for Three-Dimensional Magnetic Resonance Brain Tumor Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
R. Rajesh Sharma ◽  
P. Marikkannu
Keyword(s):  
Brain Tumor ◽  
Magnetic Resonance ◽  
Gravitational Search Algorithm ◽  
Three Dimensional ◽  
Magnetic Resonance Images ◽  
Tumor Classification ◽  
Classification Model ◽  
Support Vector ◽  
Preliminary Evaluation ◽  
Distribution Method

A novel hybrid approach for the identification of brain regions using magnetic resonance images accountable for brain tumor is presented in this paper. Classification of medical images is substantial in both clinical and research areas. Magnetic resonance imaging (MRI) modality outperforms towards diagnosing brain abnormalities like brain tumor, multiple sclerosis, hemorrhage, and many more. The primary objective of this work is to propose a three-dimensional (3D) novel brain tumor classification model using MRI images with both micro- and macroscale textures designed to differentiate the MRI of brain under two classes of lesion, benign and malignant. The design approach was initially preprocessed using 3D Gaussian filter. Based on VOI (volume of interest) of the image, features were extracted using 3D volumetric Square Centroid Lines Gray Level Distribution Method (SCLGM) along with 3D run length and cooccurrence matrix. The optimal features are selected using the proposed refined gravitational search algorithm (RGSA). Support vector machines, over backpropagation network, andk-nearest neighbor are used to evaluate the goodness of classifier approach. The preliminary evaluation of the system is performed using 320 real-time brain MRI images. The system is trained and tested by using a leave-one-case-out method. The performance of the classifier is tested using the receiver operating characteristic curve of 0.986 (±002). The experimental results demonstrate the systematic and efficient feature extraction and feature selection algorithm to the performance of state-of-the-art feature classification methods.

scholarly journals Brain Tumor Classification of MRI Images Using Deep Convolutional Neural Network

2021 ◽  
Vol 38 (4) ◽  
pp. 1171-1179
Author(s):  
Swaraja Kuraparthi ◽  
Madhavi K. Reddy ◽  
C.N. Sujatha ◽  
Himabindu Valiveti ◽  
Chaitanya Duggineni ◽  
...  
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Data Augmentation ◽  
Magnetic Resonance Images ◽  
Tumor Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Tumor Type

Manual tumor diagnosis from magnetic resonance images (MRIs) is a time-consuming procedure that may lead to human errors and may lead to false detection and classification of the tumor type. Therefore, to automatize the complex medical processes, a deep learning framework is proposed for brain tumor classification to ease the task of doctors for medical diagnosis. Publicly available datasets such as Kaggle and Brats are used for the analysis of brain images. The proposed model is implemented on three pre-trained Deep Convolution Neural Network architectures (DCNN) such as AlexNet, VGG16, and ResNet50. These architectures are the transfer learning methods used to extract the features from the pre-trained DCNN architecture, and the extracted features are classified by using the Support Vector Machine (SVM) classifier. Data augmentation methods are applied on Magnetic Resonance images (MRI) to avoid the network from overfitting. The proposed methodology achieves an overall accuracy of 98.28% and 97.87% without data augmentation and 99.0% and 98.86% with data augmentation for Kaggle and Brat's datasets, respectively. The Area Under Curve (AUC) for Receiver Operator Characteristic (ROC) is 0.9978 and 0.9850 for the same datasets. The result shows that ResNet50 performs best in the classification of brain tumors when compared with the other two networks.

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