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Relaxed Image Foresting Transforms for Interactive Volume Image Segmentation
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
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2010 (English)In: MEDICAL IMAGING 2010: IMAGE PROCESSING / [ed] Dawant BM, Haynor DR, 2010, Vol. 7623Conference paper, Published paper (Refereed)
Abstract [en]

The image Foresting (IFT) is a framework for image partitioning, commonly used for interactive segmentation. Given an image where a subset of the image elements (seed-points) have been assigned correct segmentation labels, the IFT completes the labeling by computing minimal cost paths from all image elements to the seed-points. Each image element is then given the same label as the closest seed-point. Here, we propose the relaxed IFT (RIFT). This modified version of the IFT features an additional parameter to control the smoothness of the segmentation boundary. The RIFT yields more intuitive segmentation results in the presence of noise and weak edges, while maintaining a low computational complexity. We show an application of the method to the refinement of manual segmentations of a thoracolumbar muscle in magnetic resonance images. The performed study shows that the refined segmentations are qualitatively similar to the manual segmentations, while intra-user variations are reduced by more than 50%.

Place, publisher, year, edition, pages
2010. Vol. 7623
Series
Proceedings of SPIE-The International Society for Optical Engineering, ISSN 0277-786X ; 7623
Keyword [en]
Seeded segmentation, Interactive segmentation, Minimum cost paths, Image Foresting Transform
National Category
Medical and Health Sciences Computer Science
Identifiers
URN: urn:nbn:se:uu:diva-140957DOI: 10.1117/12.840019ISI: 000285048800137ISBN: 978-0-8194-8024-8 (print)OAI: oai:DiVA.org:uu-140957DiVA: diva2:384734
Conference
Conference on Medical Imaging 2010 - Image Processing San Diego, CA, FEB 14-16, 2010
Available from: 2011-01-10 Created: 2011-01-10 Last updated: 2016-01-08Bibliographically approved
In thesis
1. Graph-based Methods for Interactive Image Segmentation
Open this publication in new window or tab >>Graph-based Methods for Interactive Image Segmentation
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The subject of digital image analysis deals with extracting relevant information from image data, stored in digital form in a computer. A fundamental problem in image analysis is image segmentation, i.e., the identification and separation of relevant objects and structures in an image. Accurate segmentation of objects of interest is often required before further processing and analysis can be performed.

Despite years of active research, fully automatic segmentation of arbitrary images remains an unsolved problem. Interactive, or semi-automatic, segmentation methods use human expert knowledge as additional input, thereby making the segmentation problem more tractable. The goal of interactive segmentation methods is to minimize the required user interaction time, while maintaining tight user control to guarantee the correctness of the results. Methods for interactive segmentation typically operate under one of two paradigms for user guidance: (1) Specification of pieces of the boundary of the desired object(s). (2) Specification of correct segmentation labels for a small subset of the image elements. These types of user input are referred to as boundary constraints and regional constraints, respectively.

This thesis concerns the development of methods for interactive segmentation, using a graph-theoretic approach. We view an image as an edge weighted graph, whose vertex set is the set of image elements, and whose edges are given by an adjacency relation among the image elements. Due to its discrete nature and mathematical simplicity, this graph based image representation lends itself well to the development of efficient, and provably correct, methods.

The contributions in this thesis may be summarized as follows:

  • Existing graph-based methods for interactive segmentation are modified to improve their performance on images with noisy or missing data, while maintaining a low computational cost.
  • Fuzzy techniques are utilized to obtain segmentations from which feature measurements can be made with increased precision.
  • A new paradigm for user guidance, that unifies and generalizes regional and boundary constraints, is proposed.

The practical utility of the proposed methods is illustrated with examples from the medical field.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2011. 61 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 813
Keyword
Digital image analysis, Interactive image segmentation, Fuzzy image segmentation, Image foresting transform, Graph labeling, Graph cuts
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-149261 (URN)978-91-554-8037-0 (ISBN)
Public defence
2011-05-06, Häggsalen, Ångströmlaboratoriet, Lägerhyddsvägen 1, Uppsala, 10:15 (English)
Opponent
Supervisors
Available from: 2011-04-14 Created: 2011-03-16 Last updated: 2014-07-21Bibliographically approved

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Nyström, Ingela

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