Thermal Wave Imaging for Detection of Osteoporosis

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.

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

Volume 3A: Biomedical and Biotechnology Engineering ◽

10.1115/imece2013-64200 ◽

2013 ◽

Cited By ~ 2

Author(s):

Arka Bhowmik ◽

Ramjee Repaka ◽

Subhash C. Mishra ◽

Ravibabu Mulaveesala

Keyword(s):

Skin Lesion ◽

Thermal Wave ◽

Numerical Study ◽

Quantitative Information ◽

High Peak Power ◽

3 Dimensional ◽

Non Invasive ◽

Wave Imaging ◽

Lock In ◽

Thermal Wave Imaging

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.

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Inspection of Defects in CFRP based on Principal Components

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c4658.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 2367-2370

Keyword(s):

Thermal Wave ◽

Principal Component ◽

Destructive Testing ◽

Excitation Scheme ◽

Reinforced Plastic ◽

Wave Imaging ◽

Carbon Fibre Reinforced Plastic ◽

Processing Techniques ◽

Subsurface Defects ◽

Thermal Wave Imaging

Recent advances in thermal non-destructive testing (TNDT) witnessed improved defect detection capabilities in various fields. Active thermography enables fast and easy inspection of products made of composites. A number of post processing techniques are being developed with an aim to enhance the subsurface defects from the thermographic data. This paper explores the idea of applying principal component analysis (PCA) to thermal wave imaging for possible enhancement of subsurface defects in carbon fibre reinforced plastic (CFRP) material. The experimentation is carried over CFRP sample using quadrature frequency modulated thermal wave imaging (QFMTWI) excitation scheme and results are compared with conventional phase based methods. The results demonstrate the potential of this approach for detecting subsurface defects in CFRP.

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Defect Detection Based on Segmentation of Thermographic Images of Frequency Modulated Thermal Wave Imaging

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.c2241.0210321 ◽

2021 ◽

Vol 10 (3) ◽

pp. 210-214

Author(s):

V.Phani Bhushan ◽

K. Murali ◽

K.S. Sagar Reddy

Keyword(s):

Image Segmentation ◽

Image Fusion ◽

Thermal Wave ◽

Destructive Testing ◽

Segmented Images ◽

Wave Imaging ◽

Image Fusion Technique ◽

Processing Techniques ◽

Non Destructive ◽

Thermal Wave Imaging

To improve the usefulness of the data, the raw images acquired during non-destructive testing should be processed by image processing techniques. In this paper, by Frequency Modulated Thermal Wave Imaging, we use the image fusion technique to boost the detection capability of defects in a GFRP sample with 25 squared Teflon inserts of different sizes positioned at various depths. In applications such as detection, image segmentation is useful where it is difficult to process the entire image at a time. In this paper, Adaptive Thresholding Image segmentation is used to classify the delamination in Thermographic Images of Infrared Non-Destructive Research on images captured at two different times. Image fusion is later applied to segmented images. Image fusion is used to merge two or more pictures of a different focus and to provide the best picture quality. Fusion is carried out using the Basic Averaging Method here. Using Relative Foreground Area error, the performance of the proposed method is quantitatively assessed. The region and shape of an object are important parameters in the case of Non-Destructive Evaluation. Such parameters are contrasted with current methods of segmentation

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