De-noising of left ventricular myocardial borders in magnetic resonance images.

In short axis left ventricular MR images, endocardial borders are the major parameters in evaluation of cardiovascular functions such as end diastolic volume, end systolic volume, and ejection fraction. Functional analysis captures the dynamic behavior of the cardiovascular system as revealed by the movement of the endocardial borders over time. Because of the huge number of MR images, an effective computerized tool is required for real time applications. One of the widely used automatic border detection algorithm-dynamic programming-generates zigzag borderlines, which lead to measurement errors. This paper surveys the performance of the wavelet adaptive filter, the snake, and the medial filter in smoothing over the zigzag borders generated by dynamic programming. Statistical analysis of two hundred and sixty four images from sixteen subjects show that all three algorithms can reduce the border line errors in terms of Hausdorff distance and border area error; however, only the wavelet adaptive filter is effective in providing the physiological measurements such as ejection fraction, end systolic volume and end diastolic volume.