首页> 外文期刊>European Physical Journal Plus >Automatic segmentation of the left ventricle in a cardiac MR short axis image using blind morphological operation
【24h】

Automatic segmentation of the left ventricle in a cardiac MR short axis image using blind morphological operation

机译:使用盲体形态操作自动分割心脏MR短轴图像中的左心室

获取原文
获取原文并翻译 | 示例
获取外文期刊封面目录资料

摘要

Conventionally, cardiac MR image analysis is done manually. Automatic examination for analyzing images can replace the monotonous tasks of massive amounts of data to analyze the global and regional functions of the cardiac left ventricle (LV). This task is performed using MR images to calculate the analytic cardiac parameter like end-systolic volume, end-diastolic volume, ejection fraction, and myocardial mass, respectively. These analytic parameters depend upon genuine delineation of epicardial, endocardial, papillary muscle, and trabeculations contours. In this paper, we propose an automatic segmentation method using the sum of absolute differences technique to localize the left ventricle. Blind morphological operations are proposed to segment and detect the LV contours of the epicardium and endocardium, automatically. We test the benchmark Sunny Brook dataset for evaluation of the proposed work. Contours of epicardium and endocardium are compared quantitatively to determine contour's accuracy and observe high matching values. Similarity or overlapping of an automatic examination to the given ground truth analysis by an expert are observed with high accuracy as with an index value of 91.30%. The proposed method for automatic segmentation gives better performance relative to existing techniques in terms of accuracy.
机译:传统上,心脏MR图像分析是手动完成的。分析图像的自动检查可以取代大量数据的单调任务,以分析心脏左心室(LV)的全球和区域功能。使用MR图像进行该任务以分别计算分析心脏参数,分别是末端收缩体积,末端舒张率,喷射分数和心肌质量。这些分析参数依赖于外心外膜,内膜,乳头肌和三相轮廓的真正描绘。在本文中,我们提出了一种自动分割方法,使用绝对差异技术的总和定位左心室。提出了盲体形态学作业,自动地进行细分和检测表皮和内切管的LV轮廓。我们测试基准Sunny Brook DataSet进行评估。地外膜和内膜的轮廓定量比较,以确定轮廓的准确性和观察高匹配值。通过高精度观察到专家对给定的地面事实分析的自动检查的相似性或重叠,并具有91.30%的指标值。所提出的自动分割方法可以在准确性方面具有相对于现有技术的更好性能。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号