A local multiscale variational approach for left ventricle analysis in cardiac images

2018 ◽  
Author(s):  
Leiner Barba Jimenez ◽  
Boris Escalante.Ramírez ◽  
Enrique Vallejo Venegas
Keyword(s):  
Left Ventricle ◽  
Variational Approach ◽  
Cardiac Images

Segmenting and Tracking the Left Ventricle by Learning the Dynamics in Cardiac Images

2005 ◽  
pp. 553-565 ◽  
Author(s):  
W. Sun ◽  
M. Çetin ◽  
R. Chan ◽  
V. Reddy ◽  
G. Holmvang ◽  
...  
Keyword(s):  
Left Ventricle ◽  
Cardiac Images

scholarly journals Left Ventricle Contouring in Cardiac Images Based on Deep Reinforcement Learning

2021 ◽  
Author(s):  
Sixing Yin ◽  
Yameng Han ◽  
Judong Pan ◽  
YIning Wang ◽  
Shufang Li ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Left Ventricle ◽  
Drawing Process ◽  
Path Finding ◽  
Cardiac Image ◽  
Cardiac Images

<pre> In this paper, we propose a novel reinforcement-learning-based framework for left ventricle contouring, which mimics how a cardiologist outlines the left ventricle in a cardiac image. Since such a contour drawing process is simply moving a paintbrush along a specific trajectory, it is thus analogized to a path finding problem.</pre>

Texture Classification and Shape Statistics Variational Approach for Segmentation of Left Ventricle Tagged MR Images

2009 ◽  
Vol 20 (1) ◽  
pp. 30-40 ◽  
Author(s):  
Fu-Chang LIU ◽  
Qiang CHEN ◽  
Quan-Sen SUN ◽  
Pheng Ann HENG ◽  
De-Shen XIA
Keyword(s):  
Left Ventricle ◽  
Variational Approach ◽  
Texture Classification ◽  
Mr Images ◽  
Shape Statistics ◽  
Tagged Mr Images

scholarly journals Sliding Window Based Machine Learning System for the Left Ventricle Localization in MR Cardiac Images

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Abdulkader Helwan ◽  
Dilber Uzun Ozsahin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Left Ventricle ◽  
Recognition Rate ◽  
Detection Task ◽  
Learning System ◽  
Backpropagation Neural Network ◽  
Vision Systems ◽  
Test Set ◽  
Cardiac Images

The most commonly encountered problem in vision systems includes its capability to suffice for different scenes containing the object of interest to be detected. Generally, the different backgrounds in which the objects of interest are contained significantly dwindle the performance of vision systems. In this work, we design a sliding windows machine learning system for the recognition and detection of left ventricles in MR cardiac images. We leverage on the capability of artificial neural networks to cope with some of the inevitable scene constraints encountered in medical objects detection tasks. We train a backpropagation neural network on samples of left and nonleft ventricles. We reformulate the left ventricles detection task as a machine learning problem and employ an intelligent system (backpropagation neural network) to achieve the detection task. We treat the left ventricle detection problem as binary classification tasks by assigning collected left ventricle samples as one class, and random (nonleft ventricles) objects are the other class. The trained backpropagation neural network is validated to possess a good generalization power by simulating it with a test set. A recognition rate of 100% and 88% is achieved on the training and test set, respectively. The trained backpropagation neural network is used to determine if the sampled region in a target image contains a left ventricle or not. Lastly, we show the effectiveness of the proposed system by comparing the manual detection of left ventricles drawn by medical experts and the automatic detection by the trained network.

A deformable model based system for 3D analysis and visualization of left ventricle in MRI cardiac images

RBM-News ◽  
1997 ◽  
Vol 19 (3) ◽  
pp. 81-89
Author(s):  
H Abrishami Moghaddam ◽  
Y Maingourd ◽  
J.F. Lerallut
Keyword(s):  
Left Ventricle ◽  
Deformable Model ◽  
3D Analysis ◽  
Model Based ◽  
Cardiac Images
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