NDLI: Combining a bottom up and top down classifiers for the segmentation of the left ventricle from cardiac imagery

Content Provider	IEEE Xplore Digital Library
Author	Nascimento, J.C. Carneiro, G.
Copyright Year	2013
Description	Author affiliation: Australian Centre for Visual Technol., Univ. of Adelaide, Adelaide, SA, Australia (Carneiro, G.) \|\| Inst. de Sist. e Robot., Inst. Super. Tecnico, Lisbon, Portugal (Nascimento, J.C.)
Abstract	The segmentation of anatomical structures is a crucial first stage of most medical imaging analysis procedures. A primary example is the segmentation of the left ventricle (LV), from cardiac imagery. Accuracy in the segmentation often requires a considerable amount of expert intervention and guidance which are expensive. Thus, automating the segmentation is welcome, but difficult because of the LV shape variability within and across individuals. To cope with this difficulty, the algorithm should have the skills to interpret the shape of the anatomical structure (i.e. LV shape) using distinct kinds of information, (i.e. different views of the same feature space). These different views will ascribe to the algorithm a more general capability that surely allows for the robustness in the segmentation accuracy. In this paper, we propose an on-line co-training algorithm using a bottom-up and top-down classifiers (each one having a different view of the data) to perform the segmentation of the LV. In particular, we consider a setting in which the LV shape can be partitioned into two distinct views and use a co-training as a way to boost each of the classifiers, thus providing a principled way to use both views together. We testify the usefulness of the approach on a public data base illustrating that the approach compares favorably with other recent proposed methodologies.
Starting Page	743
Ending Page	746
File Size	647284
Page Count	4
File Format	PDF
ISBN	9781479923410
DOI	10.1109/ICIP.2013.6738153
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-15
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Image segmentation Motion segmentation Shape Standards Ultrasonic imaging Vectors
Content Type	Text
Resource Type	Article

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