The news correspondents obtained a quote from the research from Bioengineering Unit, "An adaptive scale invariant feature transform (SIFT) was developed from the integration of a standard 3D SIFT approach with a local image-based contrast definition. The robustness and invariance of the proposed method to shape-preserving and deformable transforms were analyzed in a CT phantom study. The application of contrast transforms to the phantom images was also tested, in order to verify the variation of the local adaptive measure in relation to the modification of image contrast. The method was also applied to a lung 4D CT dataset, relying on manual feature identification by an expert user as ground truth. The 3D residual distance between matches obtained in adaptive-SIFT was then computed to verify the internal motion quantification with respect to the expert user. Extracted corresponding features in the lungs were used as regularization landmarks in a multistage deformable image registration (DIR) mapping the inhale vs exhale phase. The residual distances between the warped manual landmarks and their reference position in the inhale phase were evaluated, in order to provide a quantitative indication of the registration performed with the three different point sets. The phantom study confirmed the method invariance and robustness properties to shape-preserving and deformable transforms, showing residual matching errors below the voxel dimension. The adapted SIFT algorithm on the 4D CT dataset provided automated and accurate motion detection of peak to peak breathing motion. The proposed method resulted in reduced residual errors with respect to standard SIFT, providing a motion description comparable to expert manual identification, as confirmed by DIR. The application of the method to a 4D lung CT patient dataset demonstrated adaptive-SIFT potential as an automatic tool to detect landmarks for DIR regularization and internal motion quantification."
According to the news reporters, the research concluded: "Future works should include the optimization of the computational cost and the application of the method to other anatomical sites and image modalities."
For more information on this research see: Quantification of organ motion based on an adaptive image-based scale invariant feature method. Medical Physics, 2013;40(11):47-58. Medical Physics can be contacted at: Amer Assoc Physicists Medicine Amer Inst Physics, Ste 1 No 1, 2 Huntington Quadrangle,
Our news journalists report that additional information may be obtained by contacting
Keywords for this news article include:
Our reports deliver fact-based news of research and discoveries from around the world. Copyright 2014, NewsRx LLC
Most Popular Stories
- Obama Administration Releases Proposal to Regulate For-Profit Colleges
- Apple, HP, Intel May Take a Hit from Slowdown in Smartphone Sales Growth
- Elizabeth Vargas' Husband Marc Cohn Addresses Rumors
- Keurig Adds Peet's coffee, Alters Starbucks deal
- U.S. to Relinquish Gov't Control Over Internet
- Motley Crue's Nikki Sixx Marries Model Courtney Bingham
- FDIC Files Lawsuit on Behalf of Banks Allegedly Hurt by Libor Scandal
- Chinese e-Commerce Giant Alibaba Gears for IPO in U.S.
- Some California Cities Seeking Water Independence
- Quiznos Files for Chapter 11