Segmentation and tracking in echocardiographic sequences: active contours guided by optical flow estimates

Abstract

This paper presents a method for segmentation and tracking of cardiac structures in ultrasound image sequences. The developed algorithm is based on the active contour framework. This approach requires initial placement of the contour close to the desired position in the image, usually an object outline. Best contour shape and position are then calculated, assuming that at this configuration a global energy function, associated with a contour, attains its minimum. Active contours can be used for tracking by selecting a solution from a previous frame as an initial position in a present frame. Such an approach, however, fails for large displacements of the object of interest. This paper presents a technique that incorporates the information on pixel velocities (optical flow) into the estimate of initial contour to enable tracking of fast-moving objects. The algorithm was tested on several ultrasound image sequences, each covering one complete cardiac cycle. The contour successfully tracked boundaries of mitral valve leaflets, aortic root and endocardial borders of the left ventricle. The algorithm-generated outlines were compared against manual tracings by expert physicians. The automated method resulted in contours that were within the boundaries of intraobserver variability.

Physically And Statistically Based Deformable Models For Medical Image Analysis

“…In [20] a method for segmenting and tracking cardiac structures in ultrasound image sequences was presented. In integrating the contour's equation of motion, the method sets the initial velocities of the contour vertices to OF estimates, and sets their positions relative to the final position from the preceding frame.…”

“…An important step toward this analysis is segmentation of endocardial boundaries of the left ventricle (LV) [20,[36][37][38][39][40]. Although segmenting anatomical objects in high-SNR images can be done with simple techniques, problems do arise when the images are corrupted with noise and the object itself is not clearly or completely visible in the image.…”

Extracting cardiac shapes and motion of the chick embryo heart outflow tract from four-dimensional optical coherence tomography images

“…Algorithms have been developed to automatically extract the shape of tubular structures from vascular images. [13][14][15][16][17][18][19] In particular, deformable models, 20,21 such as active-contour (snake), have been widely used to detect the boundary of target tissues and extract their shapes. [22][23][24][25] These models, however, typically fail to correctly detect target tissues when adjacent tissues with similar intensity are present.…”

Velocity Estimation in Ultrasound Images: A Block Matching Approach

“…only at the heart walls). Motivated by the results obtained in [10] and a recent similarity measure derived according to a simplified image formation model of ultrasound images [4], we have developed a new block-matching method. The underlying framework of the method is Singh's algorithm [13].…”