Region of interest detection and evaluation in functional near infrared spectroscopy

Raul FERNANDEZ ROJAS, Xu HUANG, Keng-Liang Oub

    Research output: Contribution to journalArticle

    9 Citations (Scopus)

    Abstract

    This paper describes the use of a computational method based on an optical flow algorithm to detect regions of interest in near infrared (NIR) spectroscopy. The evaluation of such method is also presented. Visual inspection and cross correlation analysis of NIR cortical activation images were used to evaluate our method. The visual analysis exposed pain-related activations in the primary somatosensory cortex (S1) after stimulation which is consistent with similar studies, and the cross correlation results showed dominant channels on both cerebral hemispheres. Optical flow exhibited the nature of the dominant channel, the extent of the stimulation spatial distribution and the stimulation status. In addition, the directions of the optical flow vectors were linked to the stimulation perception of the participant. The two evaluation methods confirmed the success of our method in finding the region of interest in both hemispheres. The results of this real case study show that the computational method can successfully analyse and detect regions of interest and show temporal interactions between channels, and could be employed to investigate pain assessment in human subjects.
    Original languageEnglish
    Pages (from-to)317-326
    Number of pages10
    JournalJournal of Near Infrared Spectroscopy
    Volume24
    Issue number4
    DOIs
    Publication statusPublished - 2016

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    Near infrared spectroscopy
    Optical flows
    Computational methods
    Chemical activation
    Spatial distribution
    Inspection
    Infrared radiation

    Cite this

    FERNANDEZ ROJAS, Raul ; HUANG, Xu ; Oub, Keng-Liang. / Region of interest detection and evaluation in functional near infrared spectroscopy. In: Journal of Near Infrared Spectroscopy. 2016 ; Vol. 24, No. 4. pp. 317-326.
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    abstract = "This paper describes the use of a computational method based on an optical flow algorithm to detect regions of interest in near infrared (NIR) spectroscopy. The evaluation of such method is also presented. Visual inspection and cross correlation analysis of NIR cortical activation images were used to evaluate our method. The visual analysis exposed pain-related activations in the primary somatosensory cortex (S1) after stimulation which is consistent with similar studies, and the cross correlation results showed dominant channels on both cerebral hemispheres. Optical flow exhibited the nature of the dominant channel, the extent of the stimulation spatial distribution and the stimulation status. In addition, the directions of the optical flow vectors were linked to the stimulation perception of the participant. The two evaluation methods confirmed the success of our method in finding the region of interest in both hemispheres. The results of this real case study show that the computational method can successfully analyse and detect regions of interest and show temporal interactions between channels, and could be employed to investigate pain assessment in human subjects.",
    keywords = "Hemodynamic response, Image analysis, NIR spectroscopy, Optical flow, Pain detection",
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    Region of interest detection and evaluation in functional near infrared spectroscopy. / FERNANDEZ ROJAS, Raul; HUANG, Xu; Oub, Keng-Liang.

    In: Journal of Near Infrared Spectroscopy, Vol. 24, No. 4, 2016, p. 317-326.

    Research output: Contribution to journalArticle

    TY - JOUR

    T1 - Region of interest detection and evaluation in functional near infrared spectroscopy

    AU - FERNANDEZ ROJAS, Raul

    AU - HUANG, Xu

    AU - Oub, Keng-Liang

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    N2 - This paper describes the use of a computational method based on an optical flow algorithm to detect regions of interest in near infrared (NIR) spectroscopy. The evaluation of such method is also presented. Visual inspection and cross correlation analysis of NIR cortical activation images were used to evaluate our method. The visual analysis exposed pain-related activations in the primary somatosensory cortex (S1) after stimulation which is consistent with similar studies, and the cross correlation results showed dominant channels on both cerebral hemispheres. Optical flow exhibited the nature of the dominant channel, the extent of the stimulation spatial distribution and the stimulation status. In addition, the directions of the optical flow vectors were linked to the stimulation perception of the participant. The two evaluation methods confirmed the success of our method in finding the region of interest in both hemispheres. The results of this real case study show that the computational method can successfully analyse and detect regions of interest and show temporal interactions between channels, and could be employed to investigate pain assessment in human subjects.

    AB - This paper describes the use of a computational method based on an optical flow algorithm to detect regions of interest in near infrared (NIR) spectroscopy. The evaluation of such method is also presented. Visual inspection and cross correlation analysis of NIR cortical activation images were used to evaluate our method. The visual analysis exposed pain-related activations in the primary somatosensory cortex (S1) after stimulation which is consistent with similar studies, and the cross correlation results showed dominant channels on both cerebral hemispheres. Optical flow exhibited the nature of the dominant channel, the extent of the stimulation spatial distribution and the stimulation status. In addition, the directions of the optical flow vectors were linked to the stimulation perception of the participant. The two evaluation methods confirmed the success of our method in finding the region of interest in both hemispheres. The results of this real case study show that the computational method can successfully analyse and detect regions of interest and show temporal interactions between channels, and could be employed to investigate pain assessment in human subjects.

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    KW - Image analysis

    KW - NIR spectroscopy

    KW - Optical flow

    KW - Pain detection

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