Project

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Delineating Trees in Noisy 2D Images and 3D Image-Stacks

English title Delineating Trees in Noisy 2D Images and 3D Image-Stacks
Applicant Fua Pascal
Number 134734
Funding scheme Project funding (Div. I-III)
Research institution Laboratoire de vision par ordinateur EPFL - IC - ISIM - CVLAB
Institution of higher education EPF Lausanne - EPFL
Main discipline Information Technology
Start/End 01.09.2011 - 30.09.2013
Approved amount 108'615.00
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Keywords (3)

Delineation; Dendritic Trees; Computer Vision

Lay Summary (English)

Lead
Lay summary
Tree-like structures appear at many different scales and in many different contexts. They can be micrometer scale dendrites in light microscopy image-stacks, centimeter-scale blood vessels in retinal scans, or meter-scale road networks in aerial images. Extracting them automatically and robustly is therefore of fundamental relevance to many scientific disciplines. However, even though the topic has received sustained attention ever since the inception of the field of Computer Vision, both robustness and automation remain elusive. Fully automated techniques exist but require very clean data; substantial amounts of manual intervention is required for any other kind.
In this project, we will therefore develop a fully automated approach to addressing these shortcomings. We will first develop an approach to finding optimal trees that accounts both for global image and geometric properties. We will then implement practical algorithms to build near-optimal ones in an acceptably short time, even though the underlying problem is closely related to one known to be NP-Hard.
Direct link to Lay Summary Last update: 21.02.2013

Responsible applicant and co-applicants

Employees

Publications

Publication
Reconstructing Loopy Tubular Structures Using Integer Programming
E. Turetken, F. Benmansour, B. Andres, H. Pfister, P. Fua (2013), Reconstructing Loopy Tubular Structures Using Integer Programming, in Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.
Automated Reconstruction of Tree Structures using Path Classifiers and Mixed Integer Programming
E. Turetken F. Benmansour and P. Fua (2012), Automated Reconstruction of Tree Structures using Path Classifiers and Mixed Integer Programming, in onference on Computer Vision and Pattern Recognition, Providence, RI, USA.
Automated Reconstruction of Dendritic and Axonal Trees by Global Optimization With Geomet
E. Turetken G. Gonzalez C. Blum and P. Fua (2011), Automated Reconstruction of Dendritic and Axonal Trees by Global Optimization With Geomet, in Neuroinformatics, 9(2-3), 279-302.

Collaboration

Group / person Country
Types of collaboration
University of Geneva Switzerland (Europe)
- in-depth/constructive exchanges on approaches, methods or results
- Publication
Harvard University United States of America (North America)
- in-depth/constructive exchanges on approaches, methods or results
- Publication
- Exchange of personnel

Associated projects

Number Title Start Funding scheme
149866 Delineating Trees in Noisy 2D Images and 3D Image-Stacks 01.10.2013 Project funding (Div. I-III)
121737 Automating Delineation of Dendritic Networks in Noisy Image Stacks 01.09.2009 Project funding (Div. I-III)

Abstract

Tree-like structures appear at many different scales and in many different contexts. They can be micrometer scale dendrites in light microscopy image-stacks, centimeter-scale blood vessels in retinal scans, or meter-scale road networks in aerial images. Extracting them automatically and robustly is therefore of fundamental relevance to many scientific disciplines. However, even though the topic has received sustained attention ever since the inception of the field of Computer Vision, both robustness and automation remain elusive. Fully automated techniques exist but require very clean data; substantial amounts of manual intervention is required for any other kind.In this project, we will therefore develop a fully automated approach to addressing these shortcomings. We will first develop an approach to finding optimal trees that accounts both for global image and geometric properties. We will then implement a practical algorithm to build near-optimal ones in an acceptably short time, even though the underlying problem is closely related to one known to be NP-Hard.
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