Detection of Subsurface Skin Lesion Using Frequency Modulated Thermal Wave Imaging: A Numerical Study

Author(s):  
Arka Bhowmik ◽  
Ramjee Repaka ◽  
Subhash C. Mishra ◽  
Ravibabu Mulaveesala

Infrared thermography is one of the promising and non-invasive imaging approaches which can be performed either in passive or in active mode. Due to its inherent capabilities, viz., fast, safe and subsurface feature extraction, this technique has been widely used in bio-medical imaging. In conventional passive approach, imaging may not provide enough contrast for detection of subsurface skin lesion. However, this limitation can be surmounted by using active thermography technique in which controlled energy is being supplied to the skin. This controlled stimulus not only helps in the detection of deeper subsurface details but also helps in getting the quantitative information of hidden features. Apart from the various widely used active approaches such as modulated lock-in thermography (LT) and high peak power pulsed based thermography (Pulsed Thermography - PT and Pulse Phase Thermography - PPT) techniques, the present article highlights an alternative approach which can be performed in less time as compared to LT and with much less peak powers as compared to pulsed based thermography (PT and PPT) techniques. The present work utilizes a non-stationary thermal wave imaging approach to map the subsurface skin lesion. The multilayered skin has been modeled and simulated for a given frequency modulated heat stimulus using 3-dimensional bio-heat equation.

Author(s):  
Zhong Ouyang ◽  
Li Wang ◽  
Xun Wang ◽  
Feng Zhang ◽  
L. D. Favro ◽  
...  

2021 ◽  
Author(s):  
Prathipa R ◽  
Ramadevi R ◽  
Chinnammal V ◽  
Rajalakshmi S ◽  
Poonkuzhali I

Abstract Osteoporosis is a clinical sickness wherein the bones end up brittle and volatile because of tissue loss, which is usually caused by hormonal changes or a calcium or vitamin D deficiency. Osteoporosis has neither clinical signs nor symptoms, until some fracture occur. The aim of our project is to predict bone brittleness in order to detect osteoporosis using Image processing techniques. The objective measurement of bone mineral density (BMD), is presently accepted as the best indicator of osteoporosis fractures. For measuring and assessing biomaterials, thermal wave imaging is a potential , non-invasive, non-contact and safe imaging method.. Thermal wave imaging has the unique ability to measure physiological changes. The thermal images of bone are taken and removal of noise is carried out and undergone stationary wavelets transform process to improve the resolution of edges. The result shows that Artificial Neural Network is capable of predicting the brittleness of the bone using Regression in Machine Learning.


Author(s):  
P. K. Kuo ◽  
Z. J. Feng ◽  
T. Ahmed ◽  
L. D. Favro ◽  
R. L. Thomas ◽  
...  

1983 ◽  
Vol 44 (C6) ◽  
pp. C6-519-C6-524
Author(s):  
K. R. Grice ◽  
L. J. Inglehart ◽  
L. D. Favro ◽  
P. K. Kuo ◽  
R. L. Thomas

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