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Computer vision algorithms for retin...

Fritzsche, Kenneth H.

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Computer vision algorithms for retinal vessel detection and width change detection.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Computer vision algorithms for retinal vessel detection and width change detection./
Author:	Fritzsche, Kenneth H.
Description:	163 p.
Notes:	Source: Dissertation Abstracts International, Volume: 65-02, Section: B, page: 0835.
Contained By:	Dissertation Abstracts International65-02B.
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3122987

Computer vision algorithms for retinal vessel detection and width change detection.
Fritzsche, Kenneth H.

Computer vision algorithms for retinal vessel detection and width change detection. - 163 p.

Source: Dissertation Abstracts International, Volume: 65-02, Section: B, page: 0835.

Thesis (Ph.D.)--Rensselaer Polytechnic Institute, 2004.

Detection of width changes in blood vessels of the retina may be indicative of eye or systemic disease. However, fundus images taken at different times have different scales and are difficult to compare side by side. This research presents automated techniques for detection of vessel width change from two images acquired at different points in time.Subjects--Topical Terms:

626642
Computer Science.

Computer vision algorithms for retinal vessel detection and width change detection.
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035 $a (UnM)AAI3122987
035 $a AAI3122987
040 $a UnM $c UnM
100 1 $a Fritzsche, Kenneth H. $3 1950051
245 1 0 $a Computer vision algorithms for retinal vessel detection and width change detection.
300 $a 163 p.
500 $a Source: Dissertation Abstracts International, Volume: 65-02, Section: B, page: 0835.
500 $a Advisers: Charles V. Stewart; Badrinath Roysam.
502 $a Thesis (Ph.D.)--Rensselaer Polytechnic Institute, 2004.
520 $a Detection of width changes in blood vessels of the retina may be indicative of eye or systemic disease. However, fundus images taken at different times have different scales and are difficult to compare side by side. This research presents automated techniques for detection of vessel width change from two images acquired at different points in time.
520 $a In order to detect vessel width change; vessels must first be identified in available images. This research starts by making numerous improvements to an existing vessel tracing algorithm. However, even the improved results exhibit too much variance, primarily attributable to the discrete nature of the tracing algorithm. Thus new methods for estimating vessels are explored to address the limitations of the vessel tracing algorithm. These methods are designed to provide smooth, continuous boundaries. Five ribbon-like objects are put into an active contour framework, all initialized using the results from tracing. These ribbons are shown to provide more repeatable vessel boundaries than tracing with an innovative technique named in this research as cross section snakes selected as best.
520 $a In addition to estimating vessels from a single image, a technique is explored that uses information from multiple images acquired in a single sitting to estimate vessel boundaries in a single image with greater accuracy. This technique was subjectively evaluated by five people and was preferred at a rate that is double the sum of two other single image estimation techniques.
520 $a In detecting change, two approaches are tried. One that alternately tests vessel boundaries in the other image before determining change, and one that uses a statistical hypothesis test as the basis for the determination of change. The first method is the better of the two based on three criteria. First, method 1 achieves a positive predictive power of 78% and method 2, 57%. Second, method 1 correctly identifies 15 out of 20 vessel segments as changed compared with only 7 in method 2. Finally, method 1 is shown to be superior at correctly identifying no change (i.e. less false positives) over method 2.
590 $a School code: 0185.
650 4 $a Computer Science. $3 626642
650 4 $a Health Sciences, Ophthalmology. $3 1019445
690 $a 0984
690 $a 0381
710 2 0 $a Rensselaer Polytechnic Institute. $3 1019062
773 0 $t Dissertation Abstracts International $g 65-02B.
790 1 0 $a Stewart, Charles V., $e advisor
790 1 0 $a Roysam, Badrinath, $e advisor
790 $a 0185
791 $a Ph.D.
792 $a 2004
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3122987