The Study of Applicability of the Decision Tree Method for Contouring of the Left Ventricle Area in Echographic Video Data

Echocardiography is a widespread method for analysing of the heart muscle, in which a consistent set of frames with instant images of the heart are recieved. Cardiologists build a contour, which bound an area of the left ventricle for each frame, because its state gives the information to diagnose diseases of the heart muscle. Doctors have an idea about the regular contour shape. They sometimes ignore some contrasing tissue on the picture and they construct parts of the contour on not contrasting areas of the image. Thus, the analysis results are dependent on the experience of the particular doctor, and therefore, to some extent, are subjective. In this context, the task of automating contouring of the left ventricle on an ultrasound image is relevant. The article discusses the experience of using machine learning method (decision trees) for the automatic identification of the left ventricle region on ultrasound images of the apical four-chamber-projection of the human heart. The list of pixels attributes used in machine learning are submitted. The results of the application of decision trees, as well as quantitative assessments of the quality of delineation of the left ventricle, are shown.