Fully automatic feature localization for medical images using a global vector concentration approach

In this paper, we propose a novel feature localization method based on a global vector concentration approach. Our approach does not rely on the detection of local salient features around feature points. Instead, we exploit global structural information of the object extracted by calculating the con...

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Bibliographic Details
Published in2008 IEEE Conference on Computer Vision and Pattern Recognition pp. 1 - 6
Main Authors Kozakaya, T., Shibata, T., Takeguchi, T., Nishiura, M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2008
Subjects
Active contours
Biomedical imaging
Computer vision
Data mining
Image sampling
Nearest neighbor searches
Object detection
Robustness
Shape
Voting
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Summary:In this paper, we propose a novel feature localization method based on a global vector concentration approach. Our approach does not rely on the detection of local salient features around feature points. Instead, we exploit global structural information of the object extracted by calculating the concentration of directional vectors from sampling points. Those vectors are combined with local pattern descriptors of a query image and selected from preliminarily trained extended templates by nearest neighbor search. Due to the insensitivity of local changes, our method can handle partially occluded and noisy objects. We apply the proposed method to fully automatic feature localization of the left ventricular in echocardiograms. The results show the effectiveness of our method in comparison with a conventional edge-based method in terms of accuracy and robustness.
ISBN:9781424422425
1424422426
ISSN:1063-6919
DOI:10.1109/CVPR.2008.4587397