Automatic Cardiac View Classification of Echocardiogram
Abstract
We propose a fully automatic system for cardiac view classification of echocardiogram. Given an echo study video sequence, the system outputs a view label among the pre-defined standard views. The system is built based on a machine learning approach that extracts knowledge from an annotated database. It characterizes three features: 1) integrating local and global evidence, 2) utilizing view specific knowledge, and 3) employing a multi-class Logit-boost algorithm. In our prototype system, we classify four standard cardiac views: apical four chamber and apical two chamber, parasternal long axis and parasternal short axis (at mid cavity). We achieve a classification accuracy over 96% both of training and test data sets and the system runs in a second in the environment of Pentium 4 PC with 3.4 GHz CPU and 1.5 G RAM.
Cite
Text
Park et al. "Automatic Cardiac View Classification of Echocardiogram." IEEE/CVF International Conference on Computer Vision, 2007. doi:10.1109/ICCV.2007.4408867Markdown
[Park et al. "Automatic Cardiac View Classification of Echocardiogram." IEEE/CVF International Conference on Computer Vision, 2007.](https://mlanthology.org/iccv/2007/park2007iccv-automatic/) doi:10.1109/ICCV.2007.4408867BibTeX
@inproceedings{park2007iccv-automatic,
title = {{Automatic Cardiac View Classification of Echocardiogram}},
author = {Park, Jin Hyeong and Zhou, Shaohua Kevin and Simopoulos, Costas and Otsuki, Joanne and Comaniciu, Dorin},
booktitle = {IEEE/CVF International Conference on Computer Vision},
year = {2007},
pages = {1-8},
doi = {10.1109/ICCV.2007.4408867},
url = {https://mlanthology.org/iccv/2007/park2007iccv-automatic/}
}