The automatic segmentation of ultrasonic heart image using self-creating and organizing neural network has been studied.
本文研究用自产生和自组织神经网络方法进行超声心脏图像的自动分割。
Meanwhile, two-dimensional sequence segmentation on heart MR images is the essential step inbuilding model of heart movements in three-dimensional.
同时，对二维序列的心脏MR图像分割，也是心脏三维运动重建的必须步骤。
The heart boundary on the sequence image can be divided by the image segmentation method and is matched with the boundary between two continuous frames to form the boundary displacement amount.
采用图像分割方法分割出序列图像上的心脏边界，匹配连续两帧之间的边界得到边界位移量。
With this model, for a new case, an adaptive segmentation and fitting scheme is used to obtain its 3D flexible shape which is interpreted as the deformable surfaces of the patient heart.
在此基础上，针对具体病人进行图像分割和形状拟合以计算其心脏静态和动态形状参数，然后分析得到与心脏功能相关的一些重要参数。
To achieve this, noise in the PCG signal should be removed, and a segmentation method should be used to locate the first heart sound.
为此，需要消除PC G信号中的噪声，并用分段算法定位第一心音。
To achieve this, noise in the PCG signal should be removed, and a segmentation method should be used to locate the first heart sound.
为此，需要消除PC G信号中的噪声，并用分段算法定位第一心音。
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