Parameter estimation of flow-measurement in digital angiography

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 fl...

Teljes leírás

Elmentve itt :
Bibliográfiai részletek
Szerzők: Veress Krisztián
Csendes Tibor
Testületi szerző: Conference for PhD Students in Computer Science (7.) (2010) (Szeged)
Dokumentumtípus: Cikk
Megjelent: 2011
Sorozat:Acta cybernetica 20 No. 1
Kulcsszavak:Számítástechnika, Kibernetika
Tárgyszavak:
doi:10.14232/actacyb.20.1.2011.13

Online Access:http://acta.bibl.u-szeged.hu/12906
LEADER 02711nab a2200253 i 4500
001 acta12906
005 20220617142120.0
008 161015s2011 hu o 0|| eng d
022 |a 0324-721X 
024 7 |a 10.14232/actacyb.20.1.2011.13  |2 doi 
040 |a SZTE Egyetemi Kiadványok Repozitórium  |b hun 
041 |a eng 
100 1 |a Veress Krisztián 
245 1 0 |a Parameter estimation of flow-measurement in digital angiography  |h [elektronikus dokumentum] /  |c  Veress Krisztián 
260 |c 2011 
300 |a 189-206 
490 0 |a Acta cybernetica  |v 20 No. 1 
520 3 |a 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. One quick and reliable method for estimating this potential could save lives and would allow further treatments to be more accurately planned. Our task was to fit a 5-parameter Gamma function to the intensity samples extracted from the x-ray angiograms. 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 formulated an algorithm based on interval arithmetic which can be effectively used for comparing nonlinear least-squares fit 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 the presented application. 
650 4 |a Természettudományok 
650 4 |a Számítás- és információtudomány 
695 |a Számítástechnika, Kibernetika 
700 0 1 |a Csendes Tibor  |e aut 
710 |a Conference for PhD Students in Computer Science (7.) (2010) (Szeged) 
856 4 0 |u http://acta.bibl.u-szeged.hu/12906/1/actacyb_20_1_2011_13.pdf  |z Dokumentum-elérés