Fingertips Segmentation of Thermal Images and Its Potential Use in Hand Thermoregulation Analysis

Castro-Ospina, Andrés Eduardo; Correa-Mira, A. M.; Herrera-Granda, Israel David; Peluffo-Ordóñez, Diego H.; Toro, Hermes A. Fandiño

doi:10.1007/978-3-319-92639-1_38

Cited by 1 publication

(1 citation statement)

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“…These heat variations are further passed to the skin surface, where they can be registered by IR-thermal camera [17,37]. Unfortunately, this BFIRT signal profile is impractical on raw thermal imagery [38]. Irrelevant heat patterns on the skin destroy or dramatically corrupt thermal patterns produced by venous pulse propagation.…”

Section: Bfirt Detection Objectivementioning

confidence: 99%

Thermography Quantification of Human Perfusion Thermal Signature

AlZubaidi¹,

Hussein²,

Basil³

et al. 2018

Preprint

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Blood perfusion quantification is important vital parameters in different diagnostic procedure, using infrared thermography imaging; it is reliable to use this technique as non-contact, non-invasive blood flow measurement method. Therefore, we developed a measurement protocol for blood flow over the arm's anterior surface. By using the superficial brachial and radial veins to be monitored under the impact of cold-excitation of (2 °C to 5 °C), the blood perfusion signal was detected using thermal imager of long-wave infrared spectral range (LWIR, 7μm - 14 μm). The simulation of Penne's bioheat transfer equation was performed to be compared with results obtained from the infrared thermography. Furthermore, the proposed blood flow monitoring using external adjusting of the excitation temperature, by using (cold-compress, or cold air-stream) applied to the region under testing. The signal detected resembles to the hemodynamic pulse of the superficial veins, in the definition of systolic and diastolic phases of the cardiac cycle. Moreover, statistical analysis applied to the BFIRT signals from 24 subjects to estimate the skin's mean temperature after recovery from the thermal excitation.

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Section: Bfirt Detection Objectivementioning

confidence: 99%