Publicacions CVC
Home
|
Show All
|
Simple Search
|
Advanced Search
|
Add Record
|
Import
You must login to submit this form!
Login
Quick Search:
Field:
main fields
author
title
publication
keywords
abstract
created_date
call_number
contains:
...
Edit the following record:
Author
...
is Editor
Title
...
Type
Journal Article
Abstract
Book Chapter
Book Whole
Conference Article
Conference Volume
Journal
Magazine Article
Manual
Manuscript
Map
Miscellaneous
Newspaper Article
Patent
Report
Software
Year
...
Publication
...
Abbreviated Journal
...
Volume
...
Issue
...
Pages
...
Keywords
...
Abstract
Medical imaging is very useful in the assessment and treatment of many diseases. To deal with the great amount of data provided by imaging scanners and extract quantitative information that physicians can interpret, many analysis algorithms have been developed. Any process of analysis always consists of a first step of segmenting some particular structure. In medical imaging, structures are not always well defined and suffer from noise artifacts thus, ordinary segmentation methods are not well suited. The ones that seem to give better results are those based on deformable models. Nevertheless, despite their capability of mixing image features together with smoothness constraints that may compensate for image irregularities, these are naturally local methods, i. e., each node of the active contour evolve taking into account information about its neighbors and some other weak constraints about flexibility and smoothness, but not about the global shape that they should find. Due to the fact that structures to be segmented are the same for all cases but with some inter and intra-patient variation, the incorporation of a priori knowledge about shape in the segmentation method will provide robustness to it. Active Shape Models is an algorithm based on the creation of a shape model called Point Distribution Model. It performs a segmentation using only shapes similar than those previously learned from a training set that capture most of the variation presented by the structure. This algorithm works by updating shape nodes along a normal segment which often can be too restrictive. For this reason we propose a generalization of this algorithm that we call Generalized Active Shape Models and fully integrates the a priori knowledge given by the Point Distribution Model with deformable models or any other appropriate segmentation method. Two different applications to cardiac imaging of this generalized method are developed and promising results are shown.
Address
...
Corporate Author
...
Thesis
Bachelor's thesis
Master's thesis
Ph.D. thesis
Diploma thesis
Doctoral thesis
Habilitation thesis
Publisher
...
Place of Publication
...
Editor
...
Language
...
Summary Language
...
Original Title
...
Series Editor
...
Series Title
...
Abbreviated Series Title
...
Series Volume
...
Series Issue
...
Edition
...
ISSN
...
ISBN
...
Medium
...
Area
...
Expedition
...
Conference
...
Notes
...
Approved
yes
no
Location
Call Number
...
Serial
Marked
yes
no
Copy
true
fetch
ordered
false
Selected
yes
no
User Keys
...
User Notes
...
User File
...
User Groups
...
Cite Key
...
Related
...
File
URL
...
DOI
...
Online publication. Cite with this text:
...
Location Field:
don't touch
add
remove
my name & email address
Home
SQL Search
|
Library Search
|
Show Record
|
Extract Citations
Help