Literature DB >> 24691170

Vein visualization using a smart phone with multispectral Wiener estimation for point-of-care applications.

Jae Hee Song, Choye Kim, Yangmo Yoo.   

Abstract

Effective vein visualization is clinically important for various point-of-care applications, such as needle insertion. It can be achieved by utilizing ultrasound imaging or by applying infrared laser excitation and monitoring its absorption. However, while these approaches can be used for vein visualization, they are not suitable for point-of-care applications because of their cost, time, and accessibility. In this paper, a new vein visualization method based on multispectral Wiener estimation is proposed and its real-time implementation on a smart phone is presented. In the proposed method, a conventional RGB camera on a commercial smart phone (i.e., Galaxy Note 2, Samsung Electronics Inc., Suwon, Korea) is used to acquire reflectance information from veins. Wiener estimation is then applied to extract the multispectral information from the veins. To evaluate the performance of the proposed method, an experiment was conducted using a color calibration chart (ColorChecker Classic, X-rite, Grand Rapids, MI, USA) and an average root-mean-square error of 12.0% was obtained. In addition, an in vivo subcutaneous vein imaging experiment was performed to explore the clinical performance of the smart phone-based Wiener estimation. From the in vivo experiment, the veins at various sites were successfully localized using the reconstructed multispectral images and these results were confirmed by ultrasound B-mode and color Doppler images. These results indicate that the presented multispectral Wiener estimation method can be used for visualizing veins using a commercial smart phone for point-of-care applications (e.g., vein puncture guidance).

Mesh:

Year:  2014        PMID: 24691170     DOI: 10.1109/JBHI.2014.2313145

Source DB:  PubMed          Journal:  IEEE J Biomed Health Inform        ISSN: 2168-2194            Impact factor:   5.772


  3 in total

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Journal:  Biomed Opt Express       Date:  2016-11-28       Impact factor: 3.732

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3.  Numerical Demultiplexing of Color Image Sensor Measurements via Non-linear Random Forest Modeling.

Authors:  Jason Deglint; Farnoud Kazemzadeh; Daniel Cho; David A Clausi; Alexander Wong
Journal:  Sci Rep       Date:  2016-06-27       Impact factor: 4.379

  3 in total

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