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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

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Non-Invasive Forehead Segmentation in Thermographic Imaging.

Francisco J Rodriguez-Lozano¹, Fernando León-García², M Ruiz de Adana³

¹Department of Electronic and Computer Engineering, Universidad de Córdoba, Edificio Leonardo da Vinci, Campus de Rabanales, 14071 Córdoba, Spain. fj.rodriguez@uco.es.

Sensors (Basel, Switzerland)

|September 25, 2019

Summary

This summary is machine-generated.

A new method automatically segments forehead regions in thermal images for accurate body temperature measurement. This non-intrusive technique achieves 90% accuracy and operates in real-time, even with facial accessories.

Keywords:

body parameters computer vision forehead segmentation image processing thermographic imaging

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Area of Science:

Thermography
Biomedical Engineering
Computer Vision

Background:

Forehead temperature correlates with core body temperature, crucial for applications like thermal comfort and lie detection.
Manual segmentation of forehead regions in thermal images is time-consuming and lacks non-intrusive automated tools.

Purpose of the Study:

To develop a novel, automated method for forehead segmentation in thermal images.
To extract average forehead temperature non-intrusively.
To address the lack of user-interaction tools for this task.

Main Methods:

A simple, novel algorithm for forehead region segmentation from thermal images.
Method designed to be invariant to face position, morphology, and external objects.
Real-time processing capability at 83 frames per second.

Main Results:

Achieved 90% accuracy in forehead segmentation compared to manual segmentation, using the Jaccard coefficient.
Demonstrated real-time performance on embedded systems with low computational resources.
Introduced a new dataset of thermal face images with challenging features (glasses, beards, masks, varied neck poses).

Conclusions:

The proposed method offers an accurate and efficient solution for automated forehead segmentation in thermal imaging.
The technique's robustness and real-time capability make it suitable for various practical applications.
The new dataset facilitates further research in thermal facial analysis.