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Ultrasound imaging system combined with multi-modality image analysis algorithms to monitor changes in anatomical structures

Revanna Shivaprabhu, Vikas (2015) Ultrasound imaging system combined with multi-modality image analysis algorithms to monitor changes in anatomical structures. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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This dissertation concerns the development and validation of an ultrasound imaging system and novel image analysis algorithms applicable to multiple imaging modalities. The ultrasound imaging system will include a framework for 3D volume reconstruction of freehand ultrasound: a mechanism to register the 3D volumes across time and subjects, as well as with other imaging modalities, and a playback mechanism to view image slices concurrently from different acquisitions that correspond to the same anatomical region. The novel image analysis algorithms include a noise reduction method that clusters pixels into homogenous patches using a directed graph of edges between neighboring pixels, a segmentation method that creates a hierarchical graph structure using statistical analysis and a voting system to determine the similarity between homogeneous patches given their neighborhood, and finally, a hybrid atlas-based registration method that makes use of intensity corrections induced at anatomical landmarks to regulate deformable registration. The combination of the ultrasound imaging system and the image analysis algorithms will provide the ability to monitor nerve regeneration in patients undergoing regenerative, repair or transplant strategies in a sequential, non-invasive manner, including visualization of registered real-time and pre-acquired data, thus enabling preventive and therapeutic strategies for nerve regeneration in Composite Tissue Allotransplantation (CTA). The registration algorithm is also applied to MR images of the brain to obtain reliable and efficient segmentation of the hippocampus, which is a prominent structure in the study of diseases of the elderly such as vascular dementia, Alzheimer’s, and late life depression. Experimental results on 2D and 3D images, including simulated and real images, with illustrations visualizing the intermediate outcomes and the final results are presented.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Revanna Shivaprabhu, Vikasvir16@pitt.eduVIR16
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee CoChairStetten, Georgestetten@pitt.eduSTETTEN
Committee CoChairAizenstein, Howardaizen@pitt.eduAIZEN
Committee MemberDavidson, Lance lad43@pitt.eduLAD43
Committee MemberTudorascu,
Date: 9 June 2015
Date Type: Publication
Defense Date: 27 January 2015
Approval Date: 9 June 2015
Submission Date: 26 March 2015
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 145
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Bioengineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: image analysis, image segmentation, image registration, ultrasound imaging, MRI, Alzheimer's disease, simultaneous visualization, multi-modality imaging, graph theory, graph based segmentation, hippocampus segmentation, nerve fascicles
Date Deposited: 09 Jun 2015 13:42
Last Modified: 15 Nov 2016 14:26


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