Digital Image Correlation

Goh, Si Yi.

doi:10.1007/978-3-642-41714-6_42017

Dictionary Geotechnical Engineering/Wörterbuch GeoTechnik 2014

DOI: 10.1007/978-3-642-41714-6_42017

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Digital Image Correlation

Si Yi. Goh¹

Abstract: Postprint: Rouchier S., Foray G., Godin N., Woloszyn M., Roux J. AbstractThe present work aims at developing a methodology for the detection and monitoring of damage and fractures in building materials in the prospects of energetic renovation. Digital image correlation (DIC) and acoustic emission (AE) monitoring were simultaneously performed during tensile loading tests of fibre reinforced mortar samples. The full-field displacement mappings obtained by DIC revealed all ranges of cracks, from microscopic to m… Show more

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Cited by 2 publications

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Liver fibrosis recognition using multi-compression elastography technique

Wahba¹,

Khalifa²,

Seddik³

et al. 2013

JBiSE

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Liver fibrosis recognition is an important issue in diagnostic imaging. The accurate estimation of liver fibrosis stages is important to establish prognosis and to guide appropriate treatment decisions. Liver biopsy has been for many years the reference procedure to assess histological definition for liver diseases. But biopsy measurement is an invasive method besides it takes large time. So, fast and improved methods are needed. Using elastography technology, a correlation technique can be used to calculate the displacement of liver tissue after it has suffered a compression force. This displacement is related to tissue stiffness, and liver fibrosis can be classified into stages according to that displacement. The value of compression force affects the displacement of tissue and so affects the results of the liver fibrosis diagnosing. By using finite element method, liver fibrosis can be recognized directly within a short time. The proposed work succeeded in recognizing liver fibrosis by a percent reached in average to 86.67% on a simulation environment.

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Liver fibrosis recognition using multi-compression elastography technique

Wahba¹,

Khalifa²,

Seddik³

et al. 2013

JBiSE

View full text Add to dashboard Cite

show abstract

A Finite Element Model for Recognizing Breast Cancer

Wahba¹,

Khalifa²,

Seddik³

et al. 2014

JBiSE

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Breast cancer recognition is an important issue in elastography diagnostic imaging. Breast tumor biopsy has been for many years the reference procedure to assess histological definition for breast diseases. But biopsy measurement is an invasive method besides it takes larger time. So, fast and improved methods are needed. Using elastography technology, a digital image correlation technique can be used to calculate the displacement of breast tissue after it has suffered a compression force. This displacement is related to tissue stiffness, and breast cancer can be classified into benign or malignant according to that displacement. The value of compression force affects the displacement of tissue, and then affects the results of the breast cancer recognition. Finite element method was being used to simulate a model for the breast cancer as a phantom to be used in measurements and study of breast cancer diagnosis. The breast cancer using this phantom can be recognized within a short time. The proposed work succeeded in recognizing breast tumor phantom by an average correct recognition ratio CRR of about 94.25% on a simulation environment. The strain ratio SR for benign and malignant models is also computed. The result of the simulated breast tumor model is compared with real data of 10 lesion cases (6 benign and 4 malignant). The coefficient of variation CV between the simulated SR and the SR using real data reaches to about 5% for benign lesions and 4.78% for malignant lesions. The results of CRR and CV in this proposed work assure that the proposed breast cancer model using finite element modeling is a robust technique for breast tumor simulation where the behavior of real data of breast cancer can be predicted.

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Digital Image Correlation

Cited by 2 publications

References 11 publications

Liver fibrosis recognition using multi-compression elastography technique

Liver fibrosis recognition using multi-compression elastography technique

A Finite Element Model for Recognizing Breast Cancer

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