Feature-Based Fusion of Medical Imaging Data

Calhoun, Vince D.; Adalı, Tülay

doi:10.1109/titb.2008.923773

Cited by 198 publications

(154 citation statements)

References 73 publications

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“…The components highlight regions that have been implicated in the development of AD, including the medial temporal lobes at MR imaging and the temporoparietal lobes and posterior cingulate region at FDG PET (2,13,14). This technique has been used to identify disease-related regions in schizophrenia (44), but its use in early AD needs to be further investigated. Until these findings are combining biomarkers to predict conversion to AD, and many suggest that combining modalities offers additional information.…”

Section: Neuroradiology: Alzheimer Disease Conversion Prediction Withmentioning

confidence: 99%

Predicting Cognitive Decline in Subjects at Risk for Alzheimer Disease by Using Combined Cerebrospinal Fluid, MR Imaging, and PET Biomarkers

et al. 2013

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For the Alzheimer's Disease Neuroimaging InitiativePurpose:To assess the extent to which multiple Alzheimer disease (AD) biomarkers improve the ability to predict future decline in subjects with mild cognitive impairment (MCI) compared with predictions based on clinical parameters alone. Materials and Methods:All protocols were approved by the institutional review board at each site, and written informed consent was obtained from all subjects. The study was HIPAA compliant. Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline magnetic resonance (MR) imaging and fluorine 18 fluorodeoxyglucose (FDG) positron emission tomography (PET) studies for 97 subjects with MCI were used. MR imaging-derived gray matter probability maps and FDG PET images were analyzed by using independent component analysis, an unbiased data-driven method to extract independent sources of information from whole-brain data.The loading parameters for all MR imaging and FDG components, along with cerebrospinal fluid (CSF) proteins, were entered into logistic regression models (dependent variable: conversion to AD within 4 years). Eight models were considered, including all combinations of MR imaging, PET, and CSF markers with the covariates (age, education, apolipoprotein E genotype, Alzheimer's Disease Assessment Scale-Cognitive subscale score). Results:Combining MR imaging, FDG PET, and CSF data with routine clinical tests significantly increased the accuracy of predicting conversion to AD compared with clinical testing alone. The misclassification rate decreased from 41.3% to 28.4% (P , .00001). FDG PET contributed more information to routine tests (P , .00001) than CSF (P = .32) or MR imaging (P = .08). Conclusion:Imaging and CSF biomarkers can improve prediction of conversion from MCI to AD compared with baseline clinical testing. FDG PET appears to add the greatest prognostic information.q RSNA, 2012 Supplemental material: http://radiology.rsna.org/lookup /suppl

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Section: Neuroradiology: Alzheimer Disease Conversion Prediction Withmentioning

confidence: 99%

Predicting Cognitive Decline in Subjects at Risk for Alzheimer Disease by Using Combined Cerebrospinal Fluid, MR Imaging, and PET Biomarkers

et al. 2013

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“…Magnetic Resonance Imaging MRI, Positron Emission Tomography PET) as they are non-invasive, portable, less expensive, safe for longterm monitoring, and reported to be a good complementary [51,52]. We propose joint Independent Component Analysis (jICA) to fuse EEG and fNIRS measurements [53][54][55]. The jICA technique has been previously developed for integrating EEG and fMRI signals, to improve spatio-temporal resolution [56,57].…”

Section: Introductionmentioning

confidence: 99%

Mental stress assessment using simultaneous measurement of EEG and fNIRS

Al-Shargie

Kiguchi

Badruddin

et al. 2016

Biomed. Opt. Express

211

159

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Previous studies reported mental stress as one of the major contributing factors leading to various diseases such as heart attack, depression and stroke. An accurate stress assessment method may thus be of importance to clinical intervention and disease prevention. We propose a joint independent component analysis (jICA) based approach to fuse simultaneous measurement of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) on the prefrontal cortex (PFC) as a means of stress assessment. For the purpose of this study, stress was induced by using an established mental arithmetic task under time pressure with negative feedback. The induction of mental stress was confirmed by salivary alpha amylase test. Experiment results showed that the proposed fusion of EEG and fNIRS measurements improves the classification accuracy of mental stress by +3.4% compared to EEG alone and +11% compared to fNIRS alone. Similar improvements were also observed in sensitivity and specificity of proposed approach over unimodal EEG/fNIRS. Our study suggests that combination of EEG (frontal alpha rhythm) and fNIRS (concentration change of oxygenated hemoglobin) could be a potential means to assess mental stress objectively. References and links1. J. Decker, "The Stress Syndrome," Am. J. Nurs. 65(3), 97-99 (1965). 2. L. R. Murphy, "Stress management in work settings: a critical review of the health effects," Am. J. Health Promot. 11(2), 112-135 (1996). 3. B. Czéh, T. Michaelis, T. Watanabe, J. Frahm, G. de Biurrun, M. van Kampen, A. Bartolomucci, and E. Fuchs, "Stress-induced changes in cerebral metabolites, hippocampal volume, and cell proliferation are prevented by antidepressant treatment with tianeptine," Proc. Natl. Acad. Sci. U.S.A. 98(22), 12796-12801 (2001). 4. C. M. Vander Weele, C. Saenz, J. Yao, S. S. Correia, and K. A. Goosens, "Restoration of hippocampal growth hormone reverses stress-induced hippocampal impairment," Front. Behav. Neurosci. 7, 66 (2013). 5. A. Vyas, R. Mitra, B. S. Shankaranarayana Rao, and S. Chattarji, "Chronic stress induces contrasting patterns of dendritic remodeling in hippocampal and amygdaloid neurons," J. Neurosci. 22(15), 6810-6818 (2002). 6. B. S. McEwen, "Central effects of stress hormones in health and disease: Understanding the protective and damaging effects of stress and stress mediators," Eur. J. Pharmacol. 583(2-3), 174-185 (2008). 7. P. C. Strike and A. Steptoe, "Systematic review of mental stress-induced myocardial ischaemia," Eur. Heart J.24(8), 690-703 (2003). 8. A. Tsutsumi, K. Kayaba, and S. Ishikawa, "Impact of occupational stress on stroke across occupational classes and genders," Soc. Sci. Med. 72(10), 1652-1658 (2011). 9. R. A. Ajjan and P. J. Grant, "Cardiovascular disease prevention in patients with type 2 diabetes: The role of oral anti-diabetic agents," Diab. Vasc. Dis. Res. 3(3), 147-158 (2006). 10. C. Hammen, "Stress and depression," Annu. Rev. Clin. Psychol. 1(1), 293-319 (2005 532-545 (1972). 22. T. G. Vrijkotte, L. J. van Doornen, and E. ...

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“…Image fusion methods are usually divided into transform domain [5][6][7] and spatial domain [8][9][10] techniques. Fusion methods in the spatial domain are directly on pixel graylevel or color space from the source images for fusion operation, so the spatial domain fusion methods are also known as singlescale fusion method.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Sub Band Filtering Based Medical Image Fusion Through Non-subsampled Contourlet Transform

Harijana¹,

Gandluru²,

Nanjappa³

2018

IJIES

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Abstract:Medical image fusion is a powerful tool in the medical oriented applications such as diagnosis, treatment planning and image-guided radiotherapy. In this paper, a new medical image fusion approach is proposed based on Non-Subsampled Contourlet Transform (NSCT) and Adaptive sub band filtering. Though there are so many approaches proposed in earlier to find a more and clear fused image, they didn't focus on the computational complexity. Since the computational complexity directly related to the feature count, the rise in the level number of sub band images rises the computational complexity. The adaptive sub band filtering mechanism retrieves the informative sub band images only form all the obtained high frequency sub band images. Further to improve the fusion performance, the proposed approach fuses the low frequency subband images based on phase congruency rule and the high frequency subband images based on Log-Gabor energy rule. Visually and quantitatively experimental results indicate that the proposed fusion method can obtain more effective and accurate fusion results of medical images than other algorithms.

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Feature-Based Fusion of Medical Imaging Data

Cited by 198 publications

References 73 publications

Predicting Cognitive Decline in Subjects at Risk for Alzheimer Disease by Using Combined Cerebrospinal Fluid, MR Imaging, and PET Biomarkers

Predicting Cognitive Decline in Subjects at Risk for Alzheimer Disease by Using Combined Cerebrospinal Fluid, MR Imaging, and PET Biomarkers

Mental stress assessment using simultaneous measurement of EEG and fNIRS

An Adaptive Sub Band Filtering Based Medical Image Fusion Through Non-subsampled Contourlet Transform

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