title: Semi-automation of Doppler Spectrum Image Analysis for Grading Aortic Valve Stenosis Severity.
abstract: BACKGROUND
  Doppler echocardiography analysis has become a golden standard in the modern diagnosis of heart diseases. In this paper, we propose a set of techniques for semi-automated parameter extraction for aortic valve stenosis severity grading.
  
  
  OBJECTIVE
  The main objectives of the study is to create echocardiography image processing techniques, which minimize manual image processing work of clinicians and leads to reduced human error rates.
  
  
  METHODS
  Aortic valve and left ventricle output tract spectrogram images have been processed and analyzed. A novel method was developed to trace systoles and to extract diagnostic relevant features. The results of the introduced method have been compared to the findings of the participating cardiologists.
  
  
  RESULTS
  The experimental results showed the accuracy of the proposed method is comparable to the manual measurement performed by medical professionals. Linear regression analysis of the calculated parameters and the measurements manually obtained by the cardiologists resulted in the strongly correlated values: peak systolic velocity's and mean pressure gradient's R2 both equal to 0.99, their means' differences equal to 0.02 m/s and 4.09 mmHg, respectively, and aortic valve area's R2 of 0.89 with the two methods means' difference of 0.19 mm.
  
  
  CONCLUSIONS
  The introduced Doppler echocardiography images processing method can be used as a computer-aided assistance in the aortic valve stenosis diagnostics. In our future work, we intend to improve precision of left ventricular outflow tract spectrogram measurements and apply data mining methods to propose a clinical decision support system for diagnosing aortic valve stenosis.
authors.affiliation: Olegas Niakšu, Vilnius University, Institute of Mathematics and Informatics, Akademijos str. 4, Vilnius 08663, Lithuania, E-mail: niaksu@acm.org.
authors.forename: O
authors.initials: O
authors.lastname: Niakšu
authors.forename: G
authors.initials: G
authors.lastname: Balčiunaitė
authors.forename: R J
authors.initials: RJ
authors.lastname: Kizlaitis
authors.forename: P
authors.initials: P
authors.lastname: Treigys
mesh_terms: D001021:Aortic Valve / Q000503:physiopathology*; D001024:Aortic Valve Stenosis / Q000000981:diagnostic imaging* / Q000503:physiopathology*; D001331:Automation; D001783:Blood Flow Velocity; D002309:Cardiology / Q000379:methods; D057225:Data Mining; D015150:Echocardiography, Doppler / Q000379:methods*; D006352:Heart Ventricles / Q000000981:diagnostic imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D016014:Linear Models; D008490:Medical Informatics / Q000379:methods; D015233:Models, Statistical; D011312:Pressure; D015203:Reproducibility of Results; D013599:Systole
keywords: Digital image processing; Doppler echocardiography; aortic valve stenosis; feature extraction