Three-Dimensional Reconstruction of the Left Ventricle From Two Angiographic Views: An Evidence Combination Approach

Medina, Rubén; Garreau, Mireille; Toro, Javier; Coatrieux, Jean-Louis; Jugo, D.

doi:10.1109/tsmca.2003.822265

Cited by 10 publications

(8 citation statements)

References 33 publications

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“…Ventriculographic image analysis requires a precise description of ventricular shape in order to quantify the parameters associated with the cardiovascular function [15,16]. Alternatively, image analysis techniques are also useful for performing the visualization of this anatomical structure [17]. The accurate description of ventricular shape and their quantitative analysis are important, since cardiovascular disease (CVD) accounts for one third of the deaths in the world [18].…”

Section: Left Ventricle Angiogramsmentioning

confidence: 99%

An unsupervised clustering framework for automatic segmentation of left ventricle cavity in human heart angiograms

Bravo

Medina

2008

Computerized Medical Imaging and Graphics

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Section: Left Ventricle Angiogramsmentioning

confidence: 99%

An unsupervised clustering framework for automatic segmentation of left ventricle cavity in human heart angiograms

Bravo

Medina

2008

Computerized Medical Imaging and Graphics

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“…The stochastic relaxation algorithm used during the optimization of the conditional probability includes a simulated annealing algorithm that avoids the possibility of the optimization procedure becoming trapped in a useless local minima. This implies the introduction of a temperature parameter in the joint probability distribution function of (12) that results in the following equation: (16) The temperature value is decreased at each iteration. The initial temperature value is taken high enough, such that a large number of proposed voxel modifications are allowed at the beginning of the optimization process.…”

Section: ) Three-dimensional Binary Object Markov Modelmentioning

confidence: 99%

“…As a consequence, it can be affected by patient motion and contrast dye dilution. This work is part of a project [13], [14], where a slice based reconstruction method [15], a fuzzy and evidential approach [16], as well as an evolutionary reconstruction method [17] have been explored. In [14], the first developments of the Markov Random Field based reconstruction approach are presented.…”

Section: Introductionmentioning

confidence: 99%

“…However, only a 3-D Markov model is used and results are limited to the reconstruction of a binary database and a single pair of angiograms. In [16], a method for performing the binary reconstruction from two views is reported. The method is based on the fuzzy set theory and the Dempster-Shafer combination rules in order to cope with the imprecision and ambiguity of the reconstruction process.…”

Section: Introductionmentioning

confidence: 99%

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Markov random field modeling for three-dimensional reconstruction of the left ventricle in cardiac angiography

Medina

Garreau

Toro

et al. 2006

IEEE Trans. Med. Imaging

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This paper reports on a method for left ventricle three-dimensional (3-D) reconstruction from two orthogonal ventriculograms. The proposed algorithm is voxel-based and takes into account the conical projection geometry associated with the biplane image acquisition equipment. The reconstruction process starts with an initial ellipsoidal approximation derived from the input ventriculograms. This model is subsequently deformed in such a way as to match the input projections. To this end, the object is modeled as a 3-D Markov-Gibbs random field, and an energy function is defined so that it includes one term that models the projections compatibility and another one that includes the space-time regularity constraints. The performance of this reconstruction method is evaluated by considering the reconstruction of mathematically synthesized phantoms and two 3-D binary databases from two orthogonal synthesized projections. The method is also tested using real biplane ventriculograms. In this case, the performance of the reconstruction is expressed in terms of the projection error, which attains values between 9.50% and 11.78 % for two biplane sequences including a total of 55 images.

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“…Such examination enables the assessment of morphology and function of the heart. Ventriculographic image analysis requires a precise description of ventricular shape in order to quantify the parameters associated with the cardiovascular function [11] [12] or alternatively for performing the visualization of this anatomical structure [13]. The accurate description of ventricular shape and their quantitative analysis is important, since cardiovascular disease (CVD) accounts for one third of the deaths in the world [14].…”

Section: Introductionmentioning

confidence: 99%

A Clustering Based Approach for Automatic Image Segmentation: An Application to Biplane Ventriculograms

Bravo

Medina

Díaz

2006

Lecture Notes in Computer Science

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Abstract. This paper reports on an automatic method for ventricular cavity segmentation in angiographic images. The first step of the method consists in applying a linear regression model that exploits the functional relationship between the original input image and a smoothed version. This intermediate result is used as input to a clustering algorithm, which is based on a region growing technique. The clustering algorithm is a two stage process. In the first stage an initial segmentation is achieved using as input the result of the linear regression and the smoothed version of the input image. The second stage is intended for refining the initial segmentation based on feature vectors including the area, the gray-level average and the centroid of each candidate region. The segmentation method is conceptually simple and provides an accurate contour detection for the left ventricle cavity.

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Three-Dimensional Reconstruction of the Left Ventricle From Two Angiographic Views: An Evidence Combination Approach

Cited by 10 publications

References 33 publications

An unsupervised clustering framework for automatic segmentation of left ventricle cavity in human heart angiograms

An unsupervised clustering framework for automatic segmentation of left ventricle cavity in human heart angiograms

Markov random field modeling for three-dimensional reconstruction of the left ventricle in cardiac angiography

A Clustering Based Approach for Automatic Image Segmentation: An Application to Biplane Ventriculograms

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