Segmentation of the heart muscle in 3D echocardiographic images provides a tool for visualization of cardiac anatomy and assessment of heart function, and serves as an important pre-processing step for cardiac strain imaging. By incorporating spatial and temporal information of 3D ultrasound image sequences (4D), a fully automated method using image statistics was developed to perform 3D segmentation of the heart muscle. 3D rf-data were acquired with a Philips SONOS 7500 live 3D ultrasound system, and an X4 matrix array transducer (2-4 MHz). Left ventricular images of five healthy children were taken in transthoracial short/long axis view. As a first step, image statistics of blood and heart muscle were investigated. Next, based on these statistics, an adaptive mean squares filter was selected and applied to the images. Window size was related to speckle size (5x2 speckles). The degree of adaptive filtering was automatically steered by the local homogeneity of tissue. As a result, discrimination of heart muscle and blood was optimized, while sharpness of edges was preserved. After this pre-processing stage, homomorphic filtering and automatic thresholding were performed to obtain the inner borders of the heart muscle. Finally, a deformable contour algorithm was used to yield a closed contour of the left ventricular cavity in each elevational plane. Each contour was optimized using contours of the surrounding planes (spatial and temporal) as limiting condition to ensure spatial and temporal continuity.
Better segmentation of the ventricle was obtained using 4D information than using information of each plane separately.