Generalized relative entropy in functional magnetic resonance imaging

Cabella, Brenno; Sturzbecher, Marcio J.; Araújo, Dráulio Barros de; Neves, Ubiraci P. C.

doi:10.1016/j.physa.2008.09.029

Cited by 19 publications

(13 citation statements)

References 21 publications

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“…This result (q = 0.8) was identical to the conclusions of Sturzbecher et al [38] and Cabella et al [39]. Diniz et al [40] on the other hand found that q was best as 0.2 for CSF, 0.1 for white matter and 1.5 for gray matter.…”

Section: Computational Burden Analysissupporting

confidence: 86%

Preclinical Diagnosis of Magnetic Resonance (MR) Brain Images via Discrete Wavelet Packet Transform with Tsallis Entropy and Generalized Eigenvalue Proximal Support Vector Machine (GEPSVM)

Zhang

Dong

Wang

et al. 2015

Entropy

176

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Abstract:Background: Developing an accurate computer-aided diagnosis (CAD) system of MR brain images is essential for medical interpretation and analysis. In this study, we propose a novel automatic CAD system to distinguish abnormal brains from normal brains in MRI scanning. Methods: The proposed method simplifies the task to a binary classification problem. We used discrete wavelet packet transform (DWPT) to extract wavelet packet coefficients from MR brain images. Next, Shannon entropy (SE) and Tsallis entropy (TE) were harnessed to obtain entropy features from DWPT coefficients. Finally, generalized eigenvalue proximal support vector machine (GEPSVM), and GEPSVM with radial basis function (RBF) kernel, were employed as classifier. We tested the four proposed diagnosis methods (DWPT + SE + GEPSVM, DWPT + TE + GEPSVM, DWPT + SE + GEPSVM + RBF, and DWPT + TE + GEPSVM + RBF) on three benchmark datasets of Dataset-66, Dataset-160, and Dataset-255. Results: The 10 repetition of K-fold stratified cross validation results showed the proposed DWPT + TE + OPEN ACCESSEntropy 2015, 17 1796 GEPSVM + RBF method excelled not only other three proposed classifiers but also existing state-of-the-art methods in terms of classification accuracy. In addition, the DWPT + TE + GEPSVM + RBF method achieved accuracy of 100%, 100%, and 99.53% on , the offline learning cost 8.4430s and online prediction cost merely 0.1059s. Conclusions: We have proved the effectiveness of the proposed method, which achieved nearly 100% accuracy over three benchmark datasets.

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Section: Computational Burden Analysissupporting

confidence: 86%

Preclinical Diagnosis of Magnetic Resonance (MR) Brain Images via Discrete Wavelet Packet Transform with Tsallis Entropy and Generalized Eigenvalue Proximal Support Vector Machine (GEPSVM)

Zhang

Dong

Wang

et al. 2015

Entropy

176

View full text Add to dashboard Cite

show abstract

“…The question is now if the same method is chosen as the best one if physiological noise is incorporated in the data generation process. Therefore, in the final simulation study we compared the standard SPM analysis against an analysis method for event-related fMRI developed by Cabella et al (2009). The latter technique is based on the generalized relative entropy of the time series and uses the Kullback-Leibler divergence to distinguish between activated epochs and resting epochs (see Cabella et al, 2009 for a full description).…”

Section: Resultsmentioning

confidence: 99%

“…We show that by including physiological noise in the data generation process, the simulation results in terms of sensitivity and specificity drop dramatically. Additionally, we used the new proposed simulation model to compare a standard SPM analysis against the method proposed by Cabella et al (2009). The results indicate that the analysis of data containing no physiological noise yields a better performance of the SPM analysis.…”

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confidence: 99%

How ignoring physiological noise can bias the conclusions from fMRI simulation results

Welvaert

Rosseel

2012

Journal of Neuroscience Methods

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“…To determine the statistical power of the proposed test, we construct its receiver operating characteristic (ROC) curve from the probability distribution of the fraction of remaining tumour cells for simulated experiments that evolve either to cure or non-cure. ROC curve is an important tool which has been widely used in many research areas such as radiology [13] and signal analysis [14][15][16]. In a recent study [17] ROC curves were used in prediction procedures related to prostate disease.…”

Section: −1mentioning

confidence: 99%

ROC Analysis in Radiotherapy: A TCP Model-Based Test

Santos

Neves²

2013

OJAppS

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Several mathematical models have been proposed to describe the dynamics of irradiated cancer cells and to evaluate the tumour control probability (TCP). In this article, we propose a TCP model-based statistical test for predicting the outcome of a radiation treatment. We determine the foresight capability of prostate tumour erradication (cure) from Monte Carlo simulations of the Dawson-Hillen TCP model. We construct the receiver operating characteristic (ROC) curves of the test from the probability distributions of the fraction of remaining tumour cells for simulated experiments that evolve either to cure or non-cure. Simulations show that a similar procedure may be applicable to clinical data. Results suggest that the evaluation of tumour sizes after the treatment has started may be used for short-term prognosis.

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Generalized relative entropy in functional magnetic resonance imaging

Cited by 19 publications

References 21 publications

Preclinical Diagnosis of Magnetic Resonance (MR) Brain Images via Discrete Wavelet Packet Transform with Tsallis Entropy and Generalized Eigenvalue Proximal Support Vector Machine (GEPSVM)

Preclinical Diagnosis of Magnetic Resonance (MR) Brain Images via Discrete Wavelet Packet Transform with Tsallis Entropy and Generalized Eigenvalue Proximal Support Vector Machine (GEPSVM)

How ignoring physiological noise can bias the conclusions from fMRI simulation results

ROC Analysis in Radiotherapy: A TCP Model-Based Test

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