Support vector machine classification of fMRI data in image and k-space domains
نویسندگان
چکیده
INTRODUCTION Multivariate pattern classification and prediction offers an alternative to standard univariate analysis techniques, and has recently been applied in MR imaging using support vector machines (SVM) [1], and used to attain real-time feedback [2]. The standard approach has been to use reconstructed image magnitude data. However, additional information may be contained in the image phase data [3]; and the ability to use the original k-space data might yield processing or acquisition advantages. This study explores applying SVM techniques both to the complex image data (magnitude and phase), and directly on the acquired k-space data.
منابع مشابه
A new classification method based on pairwise SVM for facial age estimation
This paper presents a practical algorithm for facial age estimation from frontal face image. Facial age estimation generally comprises two key steps including age image representation and age estimation. The anthropometric model used in this study includes computation of eighteen craniofacial ratios and a new accurate skin wrinkles analysis in the first step and a pairwise binary support vector...
متن کاملFeature Selection and Classification of Microarray Gene Expression Data of Ovarian Carcinoma Patients using Weighted Voting Support Vector Machine
We can reach by DNA microarray gene expression to such wealth of information with thousands of variables (genes). Analysis of this information can show genetic reasons of disease and tumor differences. In this study we try to reduce high-dimensional data by statistical method to select valuable genes with high impact as biomarkers and then classify ovarian tumor based on gene expression data of...
متن کاملFeature Selection Using Multi Objective Genetic Algorithm with Support Vector Machine
Different approaches have been proposed for feature selection to obtain suitable features subset among all features. These methods search feature space for feature subsets which satisfies some criteria or optimizes several objective functions. The objective functions are divided into two main groups: filter and wrapper methods. In filter methods, features subsets are selected due to some measu...
متن کاملClassification of transformer faults using frequency response analysis based on cross-correlation technique and support vector machine
One of the most important methods for transformers fault diagnosis (especially mechanical defects) is the frequency response analysis (FRA) method. The most important step in the FRA diagnostic process is to differentiate the faults and classify them in different classes. This paper uses the intelligent support vector machine (SVM) method to classify transformer faults. For this purpose, two gr...
متن کاملIdentification of mild cognitive impairment disease using brain functional connectivity and graph analysis in fMRI data
Background: Early diagnosis of patients in the early stages of Alzheimer's, known as mild cognitive impairment, is of great importance in the treatment of this disease. If a patient can be diagnosed at this stage, it is possible to treat or delay Alzheimer's disease. Resting-state functional magnetic resonance imaging (fMRI) is very common in the process of diagnosing Alzheimer's disease. In th...
متن کامل