Skip to main content
Duke University Libraries
DukeSpace Scholarship by Duke Authors
  • Login
  • Ask
  • Menu
  • Login
  • Ask a Librarian
  • Search & Find
  • Using the Library
  • Research Support
  • Course Support
  • Libraries
  • About
View Item 
  •   DukeSpace
  • Theses and Dissertations
  • Masters Theses
  • View Item
  •   DukeSpace
  • Theses and Dissertations
  • Masters Theses
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

High-resolution, anthropomorphic, computational breast phantom: fusion of rule-based structures with patient-based anatomy

Thumbnail
View / Download
4.2 Mb
Date
2017
Author
Chen, Xinyuan
Advisor
Lo, Joseph Yuan-Chieh
Repository Usage Stats
154
views
322
downloads
Abstract

While patient-based breast phantoms are realistic, they are limited by low resolution due to the image acquisition and segmentation process. The purpose of this study is to restore the high frequency components for the patient-based phantoms by adding power law noise (PLN) and breast structures generated based on mathematical models. First, 3D radial symmetric PLN with β=3 was added at the boundary between adipose and glandular tissue to connect broken tissue and create a high frequency contour of the glandular tissue. Next, selected high-frequency features from the FDA rule-based computational phantom (Cooper’s ligaments, ductal network, and blood vessels) were fused into the phantom. The effects of enhancement in this study were demonstrated by 2D mammography projections and digital breast tomosynthesis (DBT) reconstruction volumes. The addition of PLN and rule-based models leads to a continuous decrease in β. The new β is 2.76, which is similar to what typically found for reconstructed DBT volumes. The new combined breast phantoms retain the realism from segmentation and gain higher resolution after restoration.

Type
Master's thesis
Department
Medical Physics
Subject
Medical imaging
computational breast phantoms
digital breast tomosynthesis
Laplacian fractional entropy
mammograms
power-law noise
segmentation
Permalink
https://hdl.handle.net/10161/15285
Citation
Chen, Xinyuan (2017). High-resolution, anthropomorphic, computational breast phantom: fusion of rule-based structures with patient-based anatomy. Master's thesis, Duke University. Retrieved from https://hdl.handle.net/10161/15285.
Collections
  • Masters Theses
More Info
Show full item record
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.

Rights for Collection: Masters Theses

Make Your Work Available Here

How to Deposit

Browse

All of DukeSpaceCommunities & CollectionsAuthorsTitlesTypesBy Issue DateDepartmentsAffiliations of Duke Author(s)SubjectsBy Submit DateThis CollectionAuthorsTitlesTypesBy Issue DateDepartmentsAffiliations of Duke Author(s)SubjectsBy Submit Date

My Account

LoginRegister

Statistics

View Usage Statistics
Duke University Libraries

Contact Us

411 Chapel Drive
Durham, NC 27708
(919) 660-5870
Perkins Library Service Desk

Digital Repositories at Duke

  • Report a problem with the repositories
  • About digital repositories at Duke
  • Accessibility Policy
  • Deaccession and DMCA Takedown Policy

TwitterFacebookYouTubeFlickrInstagramBlogs

Sign Up for Our Newsletter
  • Re-use & Attribution / Privacy
  • Support the Libraries
Duke University