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 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 A framework for evaluating correspondence between brain images using anatomical fiducials

2018 ◽  
Author(s):  
Jonathan C. Lau ◽  
Andrew G. Parrent ◽  
John Demarco ◽  
Geetika Gupta ◽  
Jason Kai ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Human Brain ◽  
Magnetic Resonance Images ◽  
Clinical Applications ◽  
Quantitative Approach ◽  
Stereotactic Neurosurgery ◽  
Brain Images ◽  
Crucial Step ◽  
Individual Participant ◽  
Novice Users

AbstractAccurate spatial correspondence between template and subject images is a crucial step in neuroimaging studies and clinical applications like stereotactic neurosurgery. In the absence of a robust quantitative approach, we sought to propose and validate a set of point landmarks, anatomical fiducials (AFIDs), that could be quickly, accurately, and reliably placed on magnetic resonance images of the human brain. Using several publicly available brain templates and individual participant datasets, novice users could be trained to place a set of 32 AFIDs with millimetric accuracy. Furthermore, the utility of the AFIDs protocol is demonstrated for evaluating subject-to-template and template-to-template registration. Specifically, we found that commonly used voxel overlap metrics were relatively insensitive to focal misregistrations compared to AFID point-based measures. Our entire protocol and study framework leverages open resources and tools, and has been developed with full transparency in mind so that others may freely use, adopt, and modify. This protocol holds value for a broad number of applications including alignment of brain images and teaching neuroanatomy.

Lipsia—a new software system for the evaluation of functional magnetic resonance images of the human brain

2001 ◽  
Vol 25 (6) ◽  
pp. 449-457 ◽  
Author(s):  
Gabriele Lohmann ◽  
Karsten Müller ◽  
Volker Bosch ◽  
Heiko Mentzel ◽  
Sven Hessler ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Human Brain ◽  
Magnetic Resonance Images ◽  
Software System ◽  
Functional Magnetic Resonance

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.

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