Comparative Evaluation of Medical Thermal Image Enhancement Techniques for Breast Cancer Detection

Thermography is a potential medical imaging modality due to its capability in providing additional physiological information. Medical thermal images obtained from infrared thermography systems incorporate valuable temperature properties and profiles, which could indicate underlying abnormalities. The quality of thermal images is often degraded due to noise, which affects the measurement processes in medical imaging. Contrast stretching and image filtering techniques are normally adopted in medical image enhancement processes. In this study, a comparative evaluation of contrast stretching and image filtering on individual channels of true color thermal images was conducted. Their individual performances were quantitatively measured using mean square error (MSE) and peak signal to noise ratio (PSNR). The results obtained showed that contrast stretching altered the temperature profile of the original image while image filtering appeared to enhance the original image with no changes in its profile. Further measurement of both MSE and PSNR showed that the Wiener filtering method outperformed other filters with an average MSE value of 0.0045 and PSNR value of 78.739 dB. Various segmentation methods applied to both filtered and contrast stretched images proved that the filtering method is preferable for in-depth analysis

Analysis of breathing patterns from thermal images using an automated segmentation method

Breathing is one of the important vital signs in diagnosing and monitoring for patients' treatment and disease. Few modalities have been used to evaluate breathing activity such as respiratory belt, thermistor and capacitive sensor. However, these requires external attachments such as electrode or sensor which might be inconvenience over long period of time. Hence, we proposed the use of thermography as a contactless monitoring device. In this study, inspiration time and expiration time of three different breathing patterns such as normal, prolonged and rapid breathing patterns were measured by using the thermography. Thermal images obtained from the subjects were processed and analysed by using an automated segmentation method which integrate the knowledge of edge-based and region-based segmentation methods into the algorithm developed. The algorithm developed in this study has shown that the tracker was able to segment the region of interest of the thermal images automatically and it provides a more accurate and stable results than manual calculation method. Thus, three different types of breathing patterns could be identified based on the inspiration time to expiration time ratio. Results shows that there was less than 5% of relative error which suggest the benefit of this algorithm