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Three-dimensional biomedical image s...

Huang, Hui-Yang.

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Three-dimensional biomedical image segmentation and visualization: A shape-based approach.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Three-dimensional biomedical image segmentation and visualization: A shape-based approach./
Author:	Huang, Hui-Yang.
Description:	92 p.
Notes:	Source: Dissertation Abstracts International, Volume: 64-03, Section: B, page: 1334.
Contained By:	Dissertation Abstracts International64-03B.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3084639

Three-dimensional biomedical image segmentation and visualization: A shape-based approach.
Huang, Hui-Yang.

Three-dimensional biomedical image segmentation and visualization: A shape-based approach. - 92 p.

Source: Dissertation Abstracts International, Volume: 64-03, Section: B, page: 1334.

Thesis (Ph.D.)--Arizona State University, 2003.

The goal of this dissertation is to provide new effective algorithms for extracting thin structures, such as lines and sheets, from three-dimensional biomedical images. Of particular interest is the capability to recover cellular structures, such as microtubule spindle fibers and plasma membranes, from laser scanning confocal microscopy data. These piecewise linear and planar structures with a Gaussian-like intensity profile can be differentiated from others by their distinctive shape characteristics. Shape-based methods are developed to enhance and segment line and sheet structures. Mathematical functions are formulated to derive quantitative relationships between shape characteristics and the eigenvalues of the Hessian matrix. Novel noise-resistant differentiation estimation methods, based on a function fitting strategy, are developed to improve the accuracy in extracting eigenvalue-related geometric features from the Hessian matrix. The segmentation algorithm is implemented in an enhancement/thresholding type of edge operators. Line and sheet structures are enhanced by their shape features and segmented by thresholding. The methods are tested on synthetic, angiography, MRI, and confocal microscopy data. A significant improvement in thin structure segmentation and visualization is demonstrated by the resultant images. They show that the new methods are robust and suitable for different types of data and a broad range of noise levels.Subjects--Topical Terms:

626642
Computer Science.

Three-dimensional biomedical image segmentation and visualization: A shape-based approach.
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035 $a (UnM)AAI3084639
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040 $a UnM $c UnM
100 1 $a Huang, Hui-Yang. $3 1940743
245 1 0 $a Three-dimensional biomedical image segmentation and visualization: A shape-based approach.
300 $a 92 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-03, Section: B, page: 1334.
500 $a Chair: Gregory Nielson.
502 $a Thesis (Ph.D.)--Arizona State University, 2003.
520 $a The goal of this dissertation is to provide new effective algorithms for extracting thin structures, such as lines and sheets, from three-dimensional biomedical images. Of particular interest is the capability to recover cellular structures, such as microtubule spindle fibers and plasma membranes, from laser scanning confocal microscopy data. These piecewise linear and planar structures with a Gaussian-like intensity profile can be differentiated from others by their distinctive shape characteristics. Shape-based methods are developed to enhance and segment line and sheet structures. Mathematical functions are formulated to derive quantitative relationships between shape characteristics and the eigenvalues of the Hessian matrix. Novel noise-resistant differentiation estimation methods, based on a function fitting strategy, are developed to improve the accuracy in extracting eigenvalue-related geometric features from the Hessian matrix. The segmentation algorithm is implemented in an enhancement/thresholding type of edge operators. Line and sheet structures are enhanced by their shape features and segmented by thresholding. The methods are tested on synthetic, angiography, MRI, and confocal microscopy data. A significant improvement in thin structure segmentation and visualization is demonstrated by the resultant images. They show that the new methods are robust and suitable for different types of data and a broad range of noise levels.
590 $a School code: 0010.
650 4 $a Computer Science. $3 626642
650 4 $a Health Sciences, Radiology. $3 1019076
690 $a 0984
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710 2 0 $a Arizona State University. $3 1017445
773 0 $t Dissertation Abstracts International $g 64-03B.
790 1 0 $a Nielson, Gregory, $e advisor
790 $a 0010
791 $a Ph.D.
792 $a 2003
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3084639