Dr. Mohiuddin Khan Shourav Profile

Dr. Mohiuddin Khan Shourav

at Kookmin Univ

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SPIE Journal Paper | 23 February 2021 Open Access

Visualization of superficial vein dynamics in dorsal hand by near-infrared imaging in response to elevated local temperature

Mohiuddin Khan Shourav, Jungil Choi, Jung Kyung Kim

JBO, Vol. 26, Issue 02, 026003, (February 2021) https://doi.org/10.1117/12.10.1117/1.JBO.26.2.026003

KEYWORDS: Skin, Veins, Near infrared, Visualization, Temperature metrology, Thermography, Lamps, Infrared cameras, Infrared imaging, Quantitative analysis

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Significance: Dry or moist skin-contact thermal stimulation for vein puncture (VP) and vein cannulation (VC) may not be feasible for sensitive skin. For a damaged, burned, or dark skin, near-infrared (NIR) imaging is preferred to visualize a vein. Postprocessing of NIR images is always required because the skin is a reflective material and veins need segmentation for quantitative analysis. Aim: Our pilot study aims to observe the effect of noncontact local heating on the superficial metacarpal veins in the dorsal surface of the hand and to visualize vein dynamics using an NIR imaging system. Approach: Our experiment consists of studies A and B at two ambient temperatures, 19°C and 25°C. A simple reflection-based NIR imaging system was installed to acquire sequential vein images for 5 min before and after applying 10 min of radiant thermal stimulation. To measure the vein diameter (VD), we trained a convolutional neural network (CNN) on sequential raw images to predict vein-segmentation masks as output images. Later these masked images were postprocessed for the VD measurements. Results: The average VD was significantly increased after thermal stimulation in study A. The maximum increments in VD were 39.3% and 9.19%, 1 min after thermal stimulation in studies A and B, respectively. Both the VD and skin temperature (Tskin) follow negative exponentials in time, and the VD is proportional to Tskin. A multiple linear-regression model was made to predict the final VD. A significant difference was observed in the change of the VD. Conclusions: NIR imaging with CNN can be used for quantitative analyses of vein dynamics. This finding can be further extended to develop real-time, image-guided medical devices by integrating them with a radiant heater and to assist medical practitioners in achieving high success rates for VP or VC.

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Proceedings Article | 1 March 2019 Paper

Near-infrared imaging of vasomotor response in hand for estimation of core temperature

Mohiuddin Khan Shourav, Jung Kyung Kim

Proceedings Volume 10877, 1087717 (2019) https://doi.org/10.1117/12.2525250

KEYWORDS: Veins, Temperature metrology, Data modeling, Near infrared, Skin, Body temperature, Imaging systems

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Core body temperature (CT) is a key indicator of an individual’s risk for heat stroke in the field. Multi-parameter sensors are impractical for field testing as they require long data-collection periods and a wide variety of settings. In the simple near-infrared (NIR) imaging method proposed below, 940-nm light emitting diodes are used for NIR illumination. Continuous images are collected by a video camera and the region of interest is extracted from the video file to calculate the changes in mean intensity over the duration of the video. Increases in vein diameter due to heat stress on the dorsal part of the hand can be quantified with these NIR-illuminated videos. A simple NIR optical imaging system for dorsal vein diameter measurements was tested for its effectiveness in monitoring core temperature changes. The technique for measuring the vein diameter is described in detail. Typically, NIR imaging can be used to measure heart rate and vein patterns, but vein diameter can also be used to infer core temperature. A model was trained using data from five volunteers engaged in a two hour long laboratory exercise, in air temperatures 24–36°C, and with CTs ranging from 36– 40°C. The data was collected from ten participants including various combinations of temperature and clothing. Classifications with the model returned an R² value of 0.897 and root-mean-square error of 0.7428. Though the model for estimating CT cannot serve as a replacement for direct measurement of CT, the results suggest that it is accurate enough for providing practical monitoring of thermal strain in the work place.

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