scholarly journals First and Second Order Statistics Features for Classification of Magnetic Resonance Brain Images

2012 ◽  
Vol 03 (02) ◽  
pp. 146-153 ◽  
Author(s):  
Namita Aggarwal ◽  
R. K. Agrawal
Keyword(s):  
Magnetic Resonance ◽  
Order Statistics ◽  
Second Order ◽  
Brain Images ◽  
Second Order Statistics

Classification of Alzheimer’s from T2 Trans-Axial Brain MR Images

2012 ◽  
Vol 2 (3) ◽  
pp. 30-43 ◽  
Author(s):  
Namita Aggarwal ◽  
Bharti Rana ◽  
R. K. Agrawal
Keyword(s):  
Feature Extraction ◽  
Order Statistics ◽  
Second Order ◽  
Discrete Wavelet ◽  
Elderly Persons ◽  
Extraction Techniques ◽  
Mr Images ◽  
Second Order Statistics ◽  
Brain Mr Images

Alzheimer’s disease is the most common form of dementia occurring in the elderly persons. Its early diagnosis may help in providing proper treatment. To date, there is no appropriate technique available to automatically classify it using MR brain images. In this work, first-and-second-order-statistics (FSOS) was employed for classification of Alzheimer’s from T2 trans-axial brain MR images. Although FSOS is a simple and well known feature extraction technique, it is not yet explored for Alzheimer’s classification. Performance of FSOS was compared with the state-of-the-art feature extraction techniques. Five commonly used classifiers were employed to build decision models. The performance of the models was evaluated in terms of sensitivity, specificity, accuracy, F-measure, training, and testing time. These models were built with varying number of training samples. Results showed that FSOS outperforms all the other existing feature extraction techniques in terms of all the considered performance measures. This was also validated by a statistical test. Interestingly, it was found that FSOS gives high performance irrespective of the choice of classifier and it works well even on small available number of samples, which is usually desired for all real time problems.Keyword: Discrete Wavelet Transform, Feature Extraction, First and Second Order Statistics, Gabor Transform, Magnetic Resonance Imaging, Slantlet Transform

scholarly journals Toward Measuring Target Perception: First-Order and Second-Order Deep Network Pipeline for Classification of Fixation-Related Potentials

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Hong Zeng ◽  
Junjie Shen ◽  
Wenming Zheng ◽  
Aiguo Song ◽  
Jia Liu
Keyword(s):  
Order Statistics ◽  
Object Perception ◽  
Representation Learning ◽  
Second Order ◽  
Visual Object ◽  
Second Order Statistics ◽  
First Order ◽  
Related Potentials ◽  
Perceptual Ability

The topdown determined visual object perception refers to the ability of a person to identify a prespecified visual target. This paper studies the technical foundation for measuring the target-perceptual ability in a guided visual search task, using the EEG-based brain imaging technique. Specifically, it focuses on the feature representation learning problem for single-trial classification of fixation-related potentials (FRPs). The existing methods either capture only first-order statistics while ignoring second-order statistics in data, or directly extract second-order statistics with covariance matrices estimated with raw FRPs that suffer from low signal-to-noise ratio. In this paper, we propose a new representation learning pipeline involving a low-level convolution subnetwork followed by a high-level Riemannian manifold subnetwork, with a novel midlevel pooling layer bridging them. In this way, the discriminative power of the first-order features can be increased by the convolution subnetwork, while the second-order information in the convolutional features could further be deeply learned with the subsequent Riemannian subnetwork. In particular, the temporal ordering of FRPs is well preserved for the components in our pipeline, which is considered to be a valuable source of discriminant information. The experimental results show that proposed approach leads to improved classification performance and robustness to lack of data over the state-of-the-art ones, thus making it appealing for practical applications in measuring the target-perceptual ability of cognitively impaired patients with the FRP technique.

Second-order statistics of natural images

2003 ◽  
Vol 52-54 ◽  
pp. 467-472 ◽  
Author(s):  
Hauke Bartsch ◽  
Klaus Obermayer
Keyword(s):  
Order Statistics ◽  
Second Order ◽  
Natural Images ◽  
Second Order Statistics
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