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Active contours without edges.无边缘活动轮廓。
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2
Automatic detection of end systole within a sequence of left ventricular echocardiographic images using autocorrelation and mitral valve motion detection.利用自相关和二尖瓣运动检测自动检测左心室超声心动图序列中的收缩末期。
Annu Int Conf IEEE Eng Med Biol Soc. 2007;2007:4504-7. doi: 10.1109/IEMBS.2007.4353340.
3
An electrostatic deformable model for medical image segmentation.一种用于医学图像分割的静电可变形模型。
Comput Med Imaging Graph. 2008 Jan;32(1):22-35. doi: 10.1016/j.compmedimag.2007.08.012. Epub 2007 Oct 15.
4
Segmentation of vessels from mammograms using a deformable model.使用可变形模型从乳腺X光片中分割血管。
Comput Methods Programs Biomed. 2004 Mar;73(3):233-47. doi: 10.1016/S0169-2607(03)00043-9.
5
Prevalence and correlates of mitral regurgitation in a population-based sample (the Strong Heart Study).基于人群样本的二尖瓣反流患病率及其相关因素（强心研究）
Am J Cardiol. 2001 Feb 1;87(3):298-304. doi: 10.1016/s0002-9149(00)01362-x.
6
Segmentation of the skull in MRI volumes using deformable model and taking the partial volume effect into account.利用可变形模型对磁共振成像（MRI）容积中的颅骨进行分割，并考虑部分容积效应。
Med Image Anal. 2000 Sep;4(3):219-33. doi: 10.1016/s1361-8415(00)00016-5.
7
Progression of mitral regurgitation: a prospective Doppler echocardiographic study.二尖瓣反流的进展：一项前瞻性多普勒超声心动图研究。
J Am Coll Cardiol. 1999 Oct;34(4):1137-44. doi: 10.1016/s0735-1097(99)00313-7.
8
Geodesic deformable models for medical image analysis.用于医学图像分析的测地线可变形模型。
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9
Validation of the proximal isovelocity surface area method for assessing mitral regurgitation in children.用于评估儿童二尖瓣反流的近端等速表面积法的验证
Pediatr Cardiol. 1996 Nov-Dec;17(6):351-9. doi: 10.1007/s002469900079.
10
Echo Doppler evaluation of patients with acute mitral regurgitation: superiority of transesophageal echocardiography with color flow imaging.急性二尖瓣反流患者的超声多普勒评估：经食管超声心动图彩色血流成像的优势
Am Heart J. 1995 May;129(5):967-74. doi: 10.1016/0002-8703(95)90118-3.

一种用于超声心动图图像边界检测的快速基于区域的主动轮廓模型。

A fast region-based active contour model for boundary detection of echocardiographic images.

机构信息

Department of Electrical Engg, IIT Roorkee, Roorkee, India.

出版信息

J Digit Imaging. 2012 Apr;25(2):271-8. doi: 10.1007/s10278-011-9408-8.

DOI:10.1007/s10278-011-9408-8
PMID:21779946
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3295971/
Abstract

This paper presents the boundary detection of atrium and ventricle in echocardiographic images. In case of mitral regurgitation, atrium and ventricle may get dilated. To examine this, doctors draw the boundary manually. Here the aim of this paper is to evolve the automatic boundary detection for carrying out segmentation of echocardiography images. Active contour method is selected for this purpose. There is an enhancement of Chan-Vese paper on active contours without edges. Our algorithm is based on Chan-Vese paper active contours without edges, but it is much faster than Chan-Vese model. Here we have developed a method by which it is possible to detect much faster the echocardiographic boundaries. The method is based on the region information of an image. The region-based force provides a global segmentation with variational flow robust to noise. Implementation is based on level set theory so it easy to deal with topological changes. In this paper, Newton-Raphson method is used which makes possible the fast boundary detection.

摘要

本文提出了超声心动图像中心房和心室的边界检测方法。在二尖瓣反流的情况下，心房和心室可能会扩张。为了检查这一点，医生手动绘制边界。本文的目的是开发自动边界检测方法，以实现超声心动图像的分割。为此选择了活动轮廓法。本文对 Chan-Vese 论文中的无边缘活动轮廓进行了增强。我们的算法基于 Chan-Vese 无边缘活动轮廓，但比 Chan-Vese 模型快得多。在这里，我们开发了一种方法，可以更快地检测超声心动图边界。该方法基于图像的区域信息。基于区域的力提供了一种对噪声鲁棒的全局分割，具有变分流。实现基于水平集理论，因此易于处理拓扑变化。本文使用牛顿-拉斐逊方法，实现了快速边界检测。