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使用主动形状模型-水平集方法对超声图像中的左心室进行稳健的边界检测。

Robust boundary detection of left ventricles on ultrasound images using ASM-level set method.

作者信息

Zhang Yaonan, Gao Yuan, Li Hong, Teng Yueyang, Kang Yan

机构信息

Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, China.

出版信息

Biomed Mater Eng. 2015;26 Suppl 1:S1291-6. doi: 10.3233/BME-151427.

Abstract

Level set method has been widely used in medical image analysis, but it has difficulties when being used in the segmentation of left ventricular (LV) boundaries on echocardiography images because the boundaries are not very distinguish, and the signal-to-noise ratio of echocardiography images is not very high. In this paper, we introduce the Active Shape Model (ASM) into the traditional level set method to enforce shape constraints. It improves the accuracy of boundary detection and makes the evolution more efficient. The experiments conducted on the real cardiac ultrasound image sequences show a positive and promising result.

摘要

水平集方法在医学图像分析中已得到广泛应用,但在超声心动图图像上用于分割左心室(LV)边界时存在困难,因为边界不是很清晰,且超声心动图图像的信噪比不是很高。在本文中,我们将主动形状模型(ASM)引入传统的水平集方法以施加形状约束。这提高了边界检测的准确性并使演化更有效。在真实心脏超声图像序列上进行的实验显示出积极且有前景的结果。

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