Advances in Computational Vision and Medical Image Processing: Methods and Applications (Computational Methods in Applied Sciences) 2009th Edition

Advances in Computational Vision and Medical Image Processing: Methods and Applications 2009th EditionComputational methodologies of signal processing and imaging analysis, namely considering 2D and 3D images, are commonly used in different applications of the human society.

Get ebook : $10.00 


For example, Computational Vision systems are progressively used for surveillance tasks, traf?c analysis, recognition process, inspection p- poses, human-machine interfaces, 3D vision and deformation analysis. One of the main characteristics of the Computational Vision domain is its int- multidisciplinary. In fact, in this domain, methodologies of several more fundam- tal sciences, such as Informatics, Mathematics, Statistics, Psychology, Mechanics and Physics are usually used.

Besides this inter-multidisciplinary characteristic, one of the main reasons that contributes for the continually effort done in this domain of the human knowledge is the number of applications in the medical area. For instance, it is possible to consider the use of statistical or physical procedures on medical images in order to model the represented structures.

This modeling can have different goals, for example: shape reconstruction, segmentation, registration, behavior interpretation and simulation, motion and deformation analysis, virtual reality, computer-assisted therapy or tissue characterization.

The main objective of the ECCOMAS Thematic Conferences on Computational Vision and Medical Image Processing (VIPimage) is to promote a comprehensive forum for discussion on the recent advances in the related ?elds trying to id- tify widespread areas of potential collaboration between researchers of different sciences.

Advances in Computational Vision and Medical Image Processing: Methods and Applications (Computational Methods in Applied Sciences) 2009th Edition

by Joao Tavares (Editor), R. M. Natal Jorge (Editor)
ISBN-13: 978-1402090851
ISBN-10: 1402090854

Advances in Computational Vision and Medical Image Processing: Methods and Applications 2009th Edition Contents

1 Modeling Cardiovascular Anatomy
from Patient-Specific Imaging …………………………… 1
Chandrajit Bajaj and Samrat Goswami
2 Geodesic Methods for Shape and Surface Processing ………….. 29
Gabriel Peyre and Laurent D. Cohen ´
3 Robust Shape Estimation with Deformable Models …………… 57
Jorge S. Marques, Jacinto C. Nascimento, Arnaldo J. Abrantes,
and Margarida Silveira
4 Digital Geometry and Its Applications to Medical Imaging ……… 77
Reneta P. Barneva and Valentin E. Brimkov
5 Multimodality in Brain Imaging: Methodologic Aspects
and Applications …………………………………….. 93
Sonia I. Gonc ´ ¸alves
6 Research Steps Towards Human Sequence Evaluation ………… 105
Jordi Gonzalez, F. Xavier Roca, and Juan J. Villanueva `
7 3D Object Reconstruction from Uncalibrated Images
Using an Off-the-Shelf Camera ………………………….. 117
Teresa C.S. Azevedo, Joao Manuel R.S. Tavares, and M ˜ ario A.P. Vaz ´
8 Edge-Images Using a Uninorm-Based Fuzzy Mathematical
Morphology: Opening and Closing ……………………….. 137
Manuel Gonzalez-Hidalgo, Arnau Mir Torres, Daniel Ruiz-Aguilera, ´
and Joan Torrens Sastre
9 A Tissue Relevance and Meshing Method for Computing
Patient-Specific Anatomical Models in Endoscopic Sinus
Surgery Simulation …………………………………… 159
M.A. Audette, I. Hertel, O. Burgert, and G. Strauss
vii
viii Contents
10 A Robust Eye Tracking Procedure for Medical and Industrial
Applications ………………………………………… 173
Alberto De Santis and Daniela Iacoviello
11 3D Reconstruction of the Retinal Arterial Tree
Using Subject-Specific Fundus Images …………………….. 187
D. Liu, N.B. Wood, X.Y. Xu, N. Witt, A.D. Hughes, and Thom SAMcG
12 Microscale Flow Dynamics of Red Blood Cells in Microchannels:
An Experimental and Numerical Analysis ………………….. 203
R. Lima, M. Nakamura, T. Omori, T. Ishikawa, S. Wada,
and T. Yamaguchi
13 Efficiency of Spherical Filters on Detection of Isotropic Defects
in Textured Backgrounds ………………………………. 221
Celine Goutti ´ ere and Jo ` el De Coninck ¨
14 Spontaneous Intracerebral Hemorrhage Image Analysis Methods:
A Survey …………………………………………… 235
Noel Perez, Jose Vald ´ es, Miguel Guevara, and Augusto Silva ´
15 Fluid-Structure Interaction Applied to Blood Flow Simulations ….. 253
Eduardo Soudah, Eugenio Onate, Jos ˜ e Garc ´ ´ıa, Jorge S. Perez, Andr ´ es´
Mena, Elvio Heindenreich, Jose F ´ elix Rodr ´ ´ıguez, Miguel Angel
Mart´ınez, and Manuel Doblare´
16 Validity of Paranasal CT Image Reconstruction for Finite
Element Models in Otorhinolaryngology …………………… 273
Maria Elizete Kunkel, Analia I. Moral, Kathrin Tingelhoff, Friedrich
Bootz, and Friedrich Wahl
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287

Advances in Computational Vision and Medical Image Processing: Methods and Applications 2009th Edition – Chapter 1

Modeling Cardiovascular Anatomy from Patient-Specific Imaging

Chandrajit Bajaj and Samrat Goswami

1.1 Introduction

The importance of modern imaging techniques for capturing detailed structural information of a biological system cannot be understated. Unfortunately images do not reveal the “full functional story” and a spatially realistic computer model is often necessary for a comprehensive  understanding of the complicated structural and physiological properties of the biological system’s entities under investigation [1].

Deeper insights into structure-to-function relationships of different entities is achieved via finite element simulations of the modeled biomedical process. A 3D (three dimensional) finite element meshed computer model of the biological system is therefore a first step to perform such simulations.

The behavioral attributes of a biological entity or the physiological interaction between different participating components of a biological system are often modeled mathematically via a coupled set of differential and integral equations, and quite often numerically evaluated using finite element (or boundary element) simulations.

To further emphasize the premise of cardiac modeling from imaging data, we state a few computational biomedical modeling and simulation examples: 3D computational modeling of the human heart for a quantitative analysis of cyclical electrical conductance on the heart membrane [2–6]; the biomechanical properties (stress-strain, elasticity) of the heart ventricular walls [7–12]; 3D modeling and simulation of pulsatile blood flow through human arteries/veins for vascular by-pass surgery pre-planning on a patient specific basis [13–18].

A finite element decomposition of the geometric domain, capturing the detailed spatial features that can be gleaned from the imaging, is therefore the essential first step toward performing the
necessary numerical simulations [19–22]…………

Leave a Reply

Your email address will not be published. Required fields are marked *