Ultrasound imaging LV tracking with adaptive window size and automatic hyper-parameter estimation
The segmentation of the heart's left ventricle (LV) chamber in several medical imaging modalities, e.g. Ultrasound (US) said Magnetic Resonance (MRI), is important from a clinical point of view in the diagnosis of certain cardiopathies. Manual segmentation is difficult, not accurate and time consuming. Therefore, automatic segmentation and tracking during cardiac cycles is needed. In this paper an automatic algorithm to segment the LV boundary along a cardiac cycle from ultrasound image sequences is used and a Bayesian despeckling algorithm is proposed. The prior parameter of the Bayesian filter is automatically estimated and an automatic window size selection strategy in used to adapt its dimension to the statistical characteristics of the image in the vicinity of the deformable contour model which segments the LV boundary. Sequences of real ultrasound images are used to illustrate the effectiveness of the approach and a comparison with other state-of-the-art filtering algorithms is provided.