Summary
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. Objectives: 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.
Keywords
Digital image processing - feature extraction - aortic valve stenosis - Doppler echocardiography
