Parameter estimation of flow-measurement in digital angiography

Parameter estimation of flow-measurement in digital angiography

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by Krisztian Veress , Csendes Tibor

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Datum	Value	Source
TITLE	Parameter estimation of flow-measurement in digital angiography	SVM HeaderParse 0.2
AUTHOR NAME	Krisztian Veress	SVM HeaderParse 0.2
AUTHOR AFFIL	Institute of Informatics; University of Szeged	SVM HeaderParse 0.2
AUTHOR ADDR	Szeged, Hungary	SVM HeaderParse 0.2
AUTHOR NAME	Csendes Tibor	SVM HeaderParse 0.2
AUTHOR AFFIL	Institute of Informatics; University of Szeged	SVM HeaderParse 0.2
AUTHOR ADDR	Szeged, Hungary	SVM HeaderParse 0.2
ABSTRACT	The purpose of angiographic procedures used in cardiovascular interventions is to classify the patient’s potential of regeneration after strokes caused by dead blood cells in the main arteria. The flow of blood into heart’s capillaries is measured using x-ray radiometry with contrastive fluids. Our task was to fit a 5-parameter Gamma function to the intensity samples extracted from the x-ray angiogramms. The estimation of this function’s parameters is hard given that the raw data set is heavily polluted with several different types of noise. Our complete solution has four main parts which have also been successfully verified and validated. First, we propose a solution for eliminating the noise by applying a specially designed moving window Gauss filter. Secondly, we have designed an algorithm for computing a good initial guess for the Levenberg-Marquardt optimizer in order to achieve the required precision. Third, an algorithm is proposed for selecting significant points on the smoothed data set with an interval-based classification method. Finally we apply the LM algorithm to compute the solutions in a nonlinear least squares way. We have also designed an algorithm which can be used for comparing different results and assign goodness values based on their residuals. This method has been used for measuring improvements during the development. We must emphasize that the proposed algorithms are distinct, they can be used in other applications together or separately since they are generally applicable, they do not depend on specialties of specific presented application.	SVM HeaderParse 0.2
CITATIONS	17 found	ParsCit 1.0