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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

An efficient automated algorithm to detect ocular surface temperature on sequence of thermograms using snake and

Jen Hong Tan¹, E Y K Ng, Rajendra Acharya U

¹College of Engineering, Nanyang Technological University, Singapore.

Journal of Medical Systems

|August 13, 2010

Summary

This study introduces an automated algorithm for precise eye and cornea localization in functional infrared imaging sequences. The novel method achieves high accuracy, covering 92% of the true ocular region with minimal non-ocular inclusion.

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Area of Science:

Ophthalmology
Medical Imaging
Biomedical Engineering

Background:

Functional infrared imaging is increasingly used in medicine, particularly for breast cancer and ocular abnormalities.
Accurate localization of ocular structures is crucial for diagnostic analysis in thermographic sequences.

Purpose of the Study:

To present an automated algorithm for precise localization of the eye and cornea in ocular thermographic sequences.
To enable automated, manual-assistance-free localization using functional infrared imaging.

Main Methods:

Development of a novel target tracing function integrated with the snake algorithm.
Utilization of a genetic algorithm to find the global minimum of the tracing function for accurate localization.
Application of the algorithm to ocular thermographic sequences recorded via functional infrared imaging.

Main Results:

The algorithm successfully achieves automated localization of ocular regions without manual intervention.
On average, the algorithm's delineated region covers 92% of the true ocular area.
Inclusion of non-ocular regions within the localized area is minimal, accounting for less than 5%.

Conclusions:

The proposed algorithm offers an accurate and automated solution for eye and cornea localization in functional infrared imaging.
This method enhances the efficiency and reliability of ocular thermography analysis.
The high coverage of true ocular regions and low non-ocular inclusion demonstrate the algorithm's effectiveness.