CLC number: TN911.73
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 0000-00-00
Cited: 4
Clicked: 5983
Zhang Zhao, Zhang Su, Zhang Chen-xi, Chen Ya-zhu. SVM for density estimation and application to medical image segmentation[J]. Journal of Zhejiang University Science B, 2006, 7(5): 365-372.
@article{title="SVM for density estimation and application to medical image segmentation",
author="Zhang Zhao, Zhang Su, Zhang Chen-xi, Chen Ya-zhu",
journal="Journal of Zhejiang University Science B",
volume="7",
number="5",
pages="365-372",
year="2006",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2006.B0365"
}
%0 Journal Article
%T SVM for density estimation and application to medical image segmentation
%A Zhang Zhao
%A Zhang Su
%A Zhang Chen-xi
%A Chen Ya-zhu
%J Journal of Zhejiang University SCIENCE B
%V 7
%N 5
%P 365-372
%@ 1673-1581
%D 2006
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2006.B0365
TY - JOUR
T1 - SVM for density estimation and application to medical image segmentation
A1 - Zhang Zhao
A1 - Zhang Su
A1 - Zhang Chen-xi
A1 - Chen Ya-zhu
J0 - Journal of Zhejiang University Science B
VL - 7
IS - 5
SP - 365
EP - 372
%@ 1673-1581
Y1 - 2006
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2006.B0365
Abstract: A method of medical image segmentation based on support vector machine (SVM) for density estimation is presented. We used this estimator to construct a prior model of the image intensity and curvature profile of the structure from training images. When segmenting a novel image similar to the training images, the technique of narrow level set method is used. The higher dimensional surface evolution metric is defined by the prior model instead of by energy minimization function. This method offers several advantages. First, SVM for density estimation is consistent and its solution is sparse. Second, compared to the traditional level set methods, this method incorporates shape information on the object to be segmented into the segmentation process. Segmentation results are demonstrated on synthetic images, MR images and ultrasonic images.
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