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Robust strain estimation is important in elastography. Strain estimates derived from each estimated deformation provide a form of elasticity image. A method is devised for predicting the accuracy of each strain estimate, which is first applied for dynamic resolution selection: parameters are automatically modulated to produce images with fixed precision at variable resolution. However, a high signal-to-noise ratio (SNRe) and contrast-to-noise ratio (CNRe) are sometimes attained by sacrificing resolution. Currently several strain estimation algorithms have been developed e.g. Global uniform…mehr

Produktbeschreibung
Robust strain estimation is important in elastography. Strain estimates derived from each estimated deformation provide a form of elasticity image. A method is devised for predicting the accuracy of each strain estimate, which is first applied for dynamic resolution selection: parameters are automatically modulated to produce images with fixed precision at variable resolution. However, a high signal-to-noise ratio (SNRe) and contrast-to-noise ratio (CNRe) are sometimes attained by sacrificing resolution. Currently several strain estimation algorithms have been developed e.g. Global uniform stretching (with gradient), Global uniform stretching (with least squares), Adaptive stretching etc. Amongst these Spline based strain estimation technique by Alam et al. is showing better performance than the other conventional strain estimators. In this Book, we will discuss about the performance parameters of 2D plate spline based elastograms such as SNRe & CNRe with the variation of strain & weight. And along with we will also discuss about the performance of various strain estimators and their overall comparison.
Autorenporträt
Md. Tariqul Islam, received his Bachelor of Science in Electrical and Electronic Engineering in 2012 from Islamic University of Technology, a subsidiary organ of OIC. Currently he has been working as a Software Engineer at Samsung Bangladesh Research & Development Institute. His research area of interest includes Biomedical signal processing.