Stochastic Model-Based Left Ventricle Segmentation in 3D Echocardiography Using Fractional Brownian Motion

2019 ◽  
pp. 77-84
Author(s):  
Omar S. Al-Kadi ◽  
Allen Lu ◽  
Albert J. Sinusas ◽  
James S. Duncan
Keyword(s):  
Brownian Motion ◽  
Left Ventricle ◽  
Stochastic Model ◽  
Fractional Brownian Motion ◽  
3D Echocardiography ◽  
Model Based ◽  
Ventricle Segmentation

scholarly journals Model-based left ventricle segmentation in 3D ultrasound using phase image

2014 ◽  
Author(s):  
Chunliang Wang ◽  
Chun Wang ◽  
Orjan Smedby
Keyword(s):  
Left Ventricle ◽  
3D Ultrasound ◽  
Phase Image ◽  
Filter Method ◽  
Multi Scale ◽  
Model Based ◽  
Phase Map ◽  
Endocardial Surface ◽  
3D Volume ◽  
Ventricle Segmentation

In this paper, we propose a semi-automatic method for left ventricle segmentation. The proposed method utilizes a multi-scale quadrature filter method to enhance the 3D volume, followed by a model-based level set method to segment the endocardial surface of the left ventricle. The phase map from the quadrature filters is also used to weight the influence of contour points when updating the statistical model.

Left ventricle segmentation in fetal echocardiography using a multi-texture active appearance model based on the steered Hermite transform

2016 ◽  
Vol 137 ◽  
pp. 231-245 ◽  
Author(s):  
Lorena Vargas-Quintero ◽  
Boris Escalante-Ramírez ◽  
Lisbeth Camargo Marín ◽  
Mario Guzmán Huerta ◽  
Fernando Arámbula Cosio ◽  
...  
Keyword(s):  
Left Ventricle ◽  
Fetal Echocardiography ◽  
Appearance Model ◽  
Active Appearance Model ◽  
Hermite Transform ◽  
Model Based ◽  
Active Appearance ◽  
Ventricle Segmentation

scholarly journals Endocardial Segmentation using Structured Random Forests in 3D Echocardiography

2014 ◽  
Author(s):  
Joao S. Domingos ◽  
Richard V. Stebbing ◽  
Alison J. Noble
Keyword(s):  
Heart Disease ◽  
Left Ventricle ◽  
Edge Detection ◽  
Random Forests ◽  
3D Model ◽  
3D Echocardiography ◽  
Structured Learning ◽  
Surface Model ◽  
Continuous Surface ◽  
Ventricle Segmentation

Segmentation of the left ventricle endocardium in 3D echocardiography is a critical step for the diagnosis of heart disease. Although recent work has shown effective endocardial edge detection, these techniques still preserve spurious anatomical edge responses that undermine overall ventricle segmentation. In this paper we propose a robust semiautomatic framework based on 2D structured learning that facilitates full 3D model-based endocardial segmentation. This method is evaluated on 30 publicly available datasets from different brands of ultrasound machines. Results show that the proposed method accurately finds the endocardium and effectively converges an explicit and continuous surface model to it.

scholarly journals Multi-frame Attention Network for Left Ventricle Segmentation in 3D Echocardiography

2021 ◽  
pp. 348-357
Author(s):  
Shawn S. Ahn ◽  
Kevinminh Ta ◽  
Stephanie Thorn ◽  
Jonathan Langdon ◽  
Albert J. Sinusas ◽  
...  
Keyword(s):  
Left Ventricle ◽  
3D Echocardiography ◽  
Attention Network ◽  
Ventricle Segmentation

scholarly journals A Wavelet-Based Almost-Sure Uniform Approximation of Fractional Brownian Motion with a Parallel Algorithm

2014 ◽  
Vol 51 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Dawei Hong ◽  
Shushuang Man ◽  
Jean-Camille Birget ◽  
Desmond S. Lun
Keyword(s):  
Brownian Motion ◽  
Convergence Rate ◽  
Fractional Brownian Motion ◽  
Parallel Algorithm ◽  
Uniform Approximation ◽  
Haar Wavelets ◽  
Hurst Index ◽  
Sample Paths ◽  
Vanishing Moment ◽  
Uniform Expansion

We construct a wavelet-based almost-sure uniform approximation of fractional Brownian motion (FBM) (Bt(H))_t∈[0,1] of Hurst index H ∈ (0, 1). Our results show that, by Haar wavelets which merely have one vanishing moment, an almost-sure uniform expansion of FBM for H ∈ (0, 1) can be established. The convergence rate of our approximation is derived. We also describe a parallel algorithm that generates sample paths of an FBM efficiently.

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