Adoption of Snake Variable Model-Based Method in Segmentation and Quantitative Calculation of Cardiac Ultrasound Medical Images

Abstract

This paper intends to explore the effect of the enhanced snake variable model in the segmentation of cardiac ultrasound images and its adoption in quantitative measurement of cardiac cavity. First, the basic principles of the traditional snake model and the gradient vector flow (GVF) snake model are explained. Then, an ellipsoid model is constructed to obtain the initial contour of the heart based on the three-dimensional volume of cardiac ultrasound image, and a discretized triangular mesh model is generated. Finally, the vortical gradient vector flow (VGVF) external force field is introduced and combined with the greedy algorithm to process the deformation of the initial ellipsoid contour of the heart. The segmentation effect is quantitatively evaluated regarding the area overlap rate (AOR) and the mean contour distance (MCD). The results show that the VGVF snake model can segment the deep recessed area of the "U-shaped map" in contrast to the traditional snake model and the GVF snake model. After being applied to ultrasonic image segmentation, the VGVF snake model obtains the segmentation result that is close to the doctor's manual segmentation result, and the average AOR and MCD are 97.4% and 3.2, respectively. The quantitative evaluation of the cardiac cavity is carried out based on the segmentation results, and the measurement of the volume change of the left ventricle within a cardiac cycle is realized. To sum up, VGVF snake model is superior to the traditional snake and GVF snake models in terms of ultrasonic image segmentation, which realizes the three-dimensional segmentation and quantitative calculation of the cardiac cavity.

Figure 1

Three-dimensional volume data conversion of cardiac ultrasound images based on interpolation.

Figure 2

Ellipse fitting based on initial area growth (a) and three-dimensional initial contour fitting of ellipsoid (b).

Figure 3

Triangular basic unit parameters.

Figure 4

The segmentation process of the cardiac cavity. Note. The yellow fill is the introduced VGVF snake model.

Figure 5

U-shaped contour extraction results of different segmentation algorithms. (a) The original image; (b) initial contour marking based on ellipse model; (c) the contour extracted by the snake model; (d) the external force field distribution of the snake model; (e) the contour extracted by the GVF snake model; (f) the external force field distribution of the GVF snake model; (g) the contour extracted by the VGVF snake model; (h) the external force field distribution of the VGVF snake model.

Figure 6

Comparison of the effect of different models for segmentation of cardiac ultrasound images. (a) Original echocardiogram; (b) snake model segmentation results; (c) GVF snake model segmentation results; (d) VGVF snake model segmentation results; (e) doctor manual segmentation results; the yellow dashed line is the segmentation result.

Figure 7

Comparison of AOR and MCD of ultrasound images processed by different models.

Figure 8

Heart ultrasound segmentation results of the ellipsoid contour VGVF snake deformation model (the yellow dotted line in the figure is the segmentation result of the VGVF snake model).

Figure 9

Heart ultrasound segmentation results manually segmented by the doctor (the red dotted line in the figure is the result of manual segmentation by the doctor).

Figure 10

AOR and MCD of ellipsoid contour VGVF snake deformation model segmenting ultrasound images.

Figure 11

Three-dimensional display of the segmentation results of the cardiac cavity. (a) The triangular mesh model of the cardiac cavity. (b) The surface rendering result of the cardiac cavity.

Figure 12

The volume change of the left ventricle based on the segmentation result of the cardiac cavity. (a) Heat map of left ventricular volume change. (b) Left ventricular volume versus time curve.