NDLI: Curvilinear Structure Tracking by Low Rank Tensor Approximation with Model Propagation

Content Provider	IEEE Xplore Digital Library
Author	Erkang Cheng Yu Pang Ying Zhu Jingyi Yu Haibin Ling
Copyright Year	2014
Description	Author affiliation: Dept. of Comput. & Inf. Sci., Temple Univ., Philadelphia, PA, USA (Erkang Cheng; Yu Pang; Haibin Ling) \|\| Siemens Corp. Technol., Princeton, NJ, USA (Ying Zhu) \|\| Dept. of Comput. & Inf. Sci., Univ. of Delaware, Newark, DE, USA (Jingyi Yu)
Abstract	Robust tracking of deformable object like catheter or vascular structures in X-ray images is an important technique used in image guided medical interventions for effective motion compensation and dynamic multi-modality image fusion. Tracking of such anatomical structures and devices is very challenging due to large degrees of appearance changes, low visibility of X-ray images and the deformable nature of the underlying motion field as a result of complex 3D anatomical movements projected into 2D images. To address these issues, we propose a new deformable tracking method using the tensor-based algorithm with model propagation. Specifically, the deformable tracking is formulated as a multi-dimensional assignment problem which is solved by rank-1 $l_{1}$ tensor approximation. The model prior is propagated in the course of deformable tracking. Both the higher order information and the model prior provide powerful discriminative cues for reducing ambiguity arising from the complex background, and consequently improve the tracking robustness. To validate the proposed approach, we applied it to catheter and vascular structures tracking and tested on X-ray fluoroscopic sequences obtained from 17 clinical cases. The results show, both quantitatively and qualitatively, that our approach achieves a mean tracking error of 1.4 pixels for vascular structure and 1.3 pixels for catheter tracking.
Starting Page	3057
Ending Page	3064
File Size	2476781
Page Count	8
File Format	PDF
ISBN	9781479951185
ISSN	10636919
DOI	10.1109/CVPR.2014.391
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Tensile stress Computational modeling Approximation methods X-ray imaging Trajectory Target tracking
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Multi-target Tracking by Rank-1 Tensor Approximation

Curvilinear structure tracking by low rank tensor approximation with model propagation.

Curvilinear Structure Tracking by Low Rank Tensor Approximation with Model Propagation

Object Tracking by Structure Tensor Analysis

Multi-target Tracking with Motion Context in Tensor Power Iteration

Real-time visual tracking via Incremental Covariance Tensor Learning

Higher Order Matching for Consistent Multiple Target Tracking

Convex Low Rank Approximation

Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination

Curvilinear Structure Tracking by Low Rank Tensor Approximation with Model Propagation

Similar Documents

Multi-target Tracking by Rank-1 Tensor Approximation

Curvilinear structure tracking by low rank tensor approximation with model propagation.

Curvilinear Structure Tracking by Low Rank Tensor Approximation with Model Propagation

Object Tracking by Structure Tensor Analysis

Multi-target Tracking with Motion Context in Tensor Power Iteration

Real-time visual tracking via Incremental Covariance Tensor Learning

Higher Order Matching for Consistent Multiple Target Tracking

Convex Low Rank Approximation

Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination

Curvilinear Structure Tracking by Low Rank Tensor Approximation with Model Propagation