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Efficient automatic correction and s...

Milchenko, Mikhail V.

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Efficient automatic correction and segmentation based three-dimensional visualization of magnetic resonance images.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Efficient automatic correction and segmentation based three-dimensional visualization of magnetic resonance images./
Author:	Milchenko, Mikhail V.
Description:	130 p.
Notes:	Source: Dissertation Abstracts International, Volume: 66-11, Section: B, page: 6073.
Contained By:	Dissertation Abstracts International66-11B.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3199747
ISBN:	9780542440502

Efficient automatic correction and segmentation based three-dimensional visualization of magnetic resonance images.
Milchenko, Mikhail V.

Efficient automatic correction and segmentation based three-dimensional visualization of magnetic resonance images. - 130 p.

Source: Dissertation Abstracts International, Volume: 66-11, Section: B, page: 6073.

Thesis (Ph.D.)--Louisiana State University and Agricultural & Mechanical College, 2005.

In the recent years, the demand for automated processing techniques for digital medical image volumes has increased substantially. Existing algorithms, however, still often require manual interaction, and newly developed automated techniques are often intended for a narrow segment of processing needs.

ISBN: 9780542440502Subjects--Topical Terms:

626642
Computer Science.

Efficient automatic correction and segmentation based three-dimensional visualization of magnetic resonance images.
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100 1 $a Milchenko, Mikhail V. $3 1909498
245 1 0 $a Efficient automatic correction and segmentation based three-dimensional visualization of magnetic resonance images.
300 $a 130 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-11, Section: B, page: 6073.
500 $a Director: John M. Tyler.
502 $a Thesis (Ph.D.)--Louisiana State University and Agricultural & Mechanical College, 2005.
520 $a In the recent years, the demand for automated processing techniques for digital medical image volumes has increased substantially. Existing algorithms, however, still often require manual interaction, and newly developed automated techniques are often intended for a narrow segment of processing needs.
520 $a The goal of this research was to develop algorithms suitable for fast and effective correction and advanced visualization of digital MR image volumes with minimal human operator interaction. This research has resulted in a number of techniques for automated processing of MR image volumes, including a novel MR inhomogeneity correction algorithm derivative surface fitting (dsf), automatic tissue detection algorithm (atd), and a new fast technique for interactive 3D visualization of segmented volumes called gravitational shading (gs).
520 $a These newly developed algorithms provided a foundation for the automated MR processing pipeline incorporated into the UniViewer medical imaging software developed in our group and available to the public. This allowed the extensive testing and evaluation of the proposed techniques.
520 $a Dsf was compared with two previously published methods on 17 digital image volumes. Dsf demonstrated faster correction speeds and uniform image quality improvement in this comparison. Dsf was the only algorithm that did not remove anatomic detail. Gs was compared with the previously published algorithm fsvr and produced rendering quality improvement while preserving real-time frame-rates.
520 $a These results show that the automated pipeline design principles used in this dissertation provide necessary tools for development of a fast and effective system for the automated correction and visualization of digital MR image volumes.
590 $a School code: 0107.
650 4 $a Computer Science. $3 626642
650 4 $a Health Sciences, Radiology. $3 1019076
650 4 $a Biophysics, Medical. $3 1017681
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710 2 0 $a Louisiana State University and Agricultural & Mechanical College. $3 783779
773 0 $t Dissertation Abstracts International $g 66-11B.
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791 $a Ph.D.
792 $a 2005
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