Automatic segmentation of low resolution fet al. cardiac data using snakes with shape priors

I. Dindoyal, T. Lambrou, J. Deng, A. Todd-Pokropek

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

3 Citations (Scopus)

Abstract

This paper presents a level set deformable model to segment all four chambers of the fetal heart simultaneously. We show its results in 2D on 53 images taken from only 8 datasets. Due to our lack of sufficient data we built only a mean template from the training data instead of a full active shape model. Using rigid registration the template was registered to unseen images and the snakes were guided by individual chamber priors as they evolved in unison to segment missing cardiac structures in the presence of high noise. Using a leave one out approach most of the segmentation errors are within 3 pixels of manually traced contours.
Original languageEnglish
Title of host publication2007 5th International Symposium on Image and Signal Processing and Analysis
PublisherIEEE Explore
Pages538-543
DOIs
Publication statusPublished - 2007
Event5th International Symposium on Image and Signal Processing and Analysis - Istanbul, Turkey, Istanbul, Turkey
Duration: 27 Sep 200729 Sep 2007

Conference

Conference5th International Symposium on Image and Signal Processing and Analysis
Country/TerritoryTurkey
CityIstanbul
Period27/09/0729/09/07

Fingerprint

Dive into the research topics of 'Automatic segmentation of low resolution fet al. cardiac data using snakes with shape priors'. Together they form a unique fingerprint.

Cite this