Tools

LEADER 03209cam 2200529Ii 4500

001 on1201526232;

003 OCoLC;

005 20210429041833.0;

006 m o d ;

007 cr unu||||||||;

008 201026s2020 ncua obm 000 0 eng d;

035 a| (Sirsi) o1201526232;

035 a| (OCoLC)1201526232;

049 a| EREE;

090 a| R857.O6;

100 1 a| Hempstead, William Henry,e| author.?| UNAUTHORIZED;

245 10 a| Machine learning assisted non-rigid surface tracking in biological systems /c| by William Henry Hempstead.;

264 1 a| [Greenville, N.C.] :b| [East Carolina University],c| 2020.;

300 a| 1 online resource (76 pages) :b| illustrations (chiefly color);

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

347 a| text file;

347 b| PDF;

347 c| 30.29 MB;

538 a| System requirements: Adobe Reader.;

538 a| Mode of access: World Wide Web.;

502 b| Biomedical Engineeringc| East Carolina Universityd| 2020;

500 a| Presented to the faculty of the Department of Engineering.;

500 a| Advisor: Zhen Zhu;

500 a| Title from PDF t.p. (viewed April 22, 2021).;

520 3 a| Occlusions, obstructions, and lighting changes that occur in a camera's field-of-view (FOV) during a medical procedure can cause tracking algorithms to lose track of a particular region-of-interest (ROI). Various approaches to reacquire the tracking of rigid objects have been developed, however, the non-rigid nature of biological structures requires more complicated approaches. The purpose of this research is to improve the performance of an existing non-rigid tracking algorithm under these types of adverse conditions. This existing algorithm was previously shown to be accurate and efficient under ideal conditions but exhibited a high rate of tracking failures due to the aforementioned occlusions, obstructions, and lighting changes. To improve tracking under these conditions, a tissue motion machine learning model was developed to provide predictions of future ROI grid motion. The combination of this machine learning technique along with various improvements to the base algorithm was shown to greatly reduce the number of tracking resets and allow the tracking grid to briefly follow an expected motion pattern during a simulated occlusion.;

504 a| Includes bibliographical references.;

650 0 a| Imaging systems in medicine.=| ^A9538;

650 0 a| Machine learning.=| ^A145211;

650 0 a| Automatic tracking.=| ^A430802;

655 7 a| Academic theses.2| lcgft;

700 1 a| Zhu, Zhene| degree supervisor.=| ^A1347982;

710 2 a| East Carolina University.b| Department of Engineering.=| ^A1231592;

856 40 u| http://hdl.handle.net/10342/8713z| Access via ScholarShip;

949 o| wjh;

994 a| C0b| ERE;

596 a| 1 4;

998 a| 5524874;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5524874-1001l| JNETm| JOYNERr| Ys| Yt| JNE3ETDu| 10/26/2020x| ETDz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5524874-2001l| HSLELECm| HSLr| Ys| Yt| HEETDu| 10/26/2020x| ETDz| HERESOURCE;

Machine learning assisted non-rigid surface tracking in biological systems / by William Henry Hempstead.

Author/creator	Hempstead, William Henry author.
Other author	Zhu, Zhen degree supervisor.
Other author	East Carolina University. Department of Engineering.
Format	Theses and dissertations
Publication	[Greenville, N.C.] : [East Carolina University], 2020.
Description	1 online resource (76 pages) : illustrations (chiefly color)
Supplemental Content	Access via ScholarShip
Subjects	Imaging systems in medicine. Machine learning. Automatic tracking.

Summary	Occlusions, obstructions, and lighting changes that occur in a camera's field-of-view (FOV) during a medical procedure can cause tracking algorithms to lose track of a particular region-of-interest (ROI). Various approaches to reacquire the tracking of rigid objects have been developed, however, the non-rigid nature of biological structures requires more complicated approaches. The purpose of this research is to improve the performance of an existing non-rigid tracking algorithm under these types of adverse conditions. This existing algorithm was previously shown to be accurate and efficient under ideal conditions but exhibited a high rate of tracking failures due to the aforementioned occlusions, obstructions, and lighting changes. To improve tracking under these conditions, a tissue motion machine learning model was developed to provide predictions of future ROI grid motion. The combination of this machine learning technique along with various improvements to the base algorithm was shown to greatly reduce the number of tracking resets and allow the tracking grid to briefly follow an expected motion pattern during a simulated occlusion.
General note	Presented to the faculty of the Department of Engineering.
General note	Advisor: Zhen Zhu
General note	Title from PDF t.p. (viewed April 22, 2021).
Dissertation note	Biomedical Engineering East Carolina University 2020
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.
Genre/form	Academic theses.

Availability

Library	Location	Call Number	Status	Item Actions
	Electronic Resources	Access Content Online	✔ Available

Machine learning assisted non-rigid surface tracking in biological systems / by William Henry Hempstead.

Click here for more information about this title

Availability