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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 12/2018

29.03.2017 | Original Article

Morphological segmenting and neighborhood pixel-based locality preserving projection on brain fMRI dataset for semantic feature extraction: an affective computing study

verfasst von: Zongmei Tian, Nilanjan Dey, Amira S. Ashour, Pamela McCauley, Fuqian Shi

Erschienen in: Neural Computing and Applications | Ausgabe 12/2018

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Abstract

Two specific chemical receptive fields of brain, namely the amygdala and the orbital-frontal cortex, are related to valence and arousal in medical experiments. Functional magnetic resonance imaging (fMRI), which is a noninvasive, repeatable, and atomical tool for medical imaging in clinic system, was widely used in affective computing; however, it faces its dataset processing difficulty for dimensional reduction as well as for decreasing the computational complexity. In addition, features extraction from those de-dimensionality datasets is a challenging issue. The current work solved the de-dimensionality issue by using some preprocessing algorithms including clustering, morphological segmenting, and locality preserving projection. In order to keep useful information in fMRI dataset for reduction process, improved neighborhood pixel-based locality preserving projection (NP-LPP) algorithm was addressed and continuously for feature extraction operating using Otsu weighted sum of histogram. Furthermore, a modified covariance power spectral density (MC-PSD) separately in an fMRI Valence–Arousal experiments was measured. The results were analyzed and compared with affective norms English words system. The experiments established that the proposed methods of NP-LPP effectively simplified high complexity of fMRI, and Otsu weighted sum of histogram exhibited superior performance for features extraction compared to the MC-PSD through the calculation root mean standard error. The current proposed method provided a potential application and promising research direction on human semantic retrieval through medical imaging dataset.
Vorheriger Artikel Numerical investigation of heat and mass transfer flow under the influence of silicon carbide by means of plasma-enhanced chemical vapor deposition vertical reactor
Nächster Artikel A hybrid soft computing approach based on feature selection for estimation of filtration combustion characteristics

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Metadaten
Titel
Morphological segmenting and neighborhood pixel-based locality preserving projection on brain fMRI dataset for semantic feature extraction: an affective computing study
verfasst von
Zongmei Tian
Nilanjan Dey
Amira S. Ashour
Pamela McCauley
Fuqian Shi
Publikationsdatum
29.03.2017
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 12/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-2955-2

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