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Image Foresting Transform: On-the-fly Computation of Segmentation Boundaries
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
2011 (English)In: Image Analysis: 17th Scandinavian Conference, SCIA 2011, Springer , 2011Conference paper, Published paper (Refereed)
Abstract [en]

The Image Foresting Transform (IFT) is a framework for seeded image segmentation, based on the computation of minimal cost paths in a discrete representation of an image. In two recent publications, we have shown that the segmentations obtained by the IFT may be improved by refining the segmentation locally around the boundariesbetween segmented regions. Since these methods operate on a small subset of the image elements only, they may be implemented efficiently if the set of boundary elements is known. Here, we show that this set maybe obtained on-the-fly, at virtually no additional cost, as a by-product of the IFT algorithm.

Place, publisher, year, edition, pages
Springer , 2011.
Series
Lecture notes in computer science
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Analysis; Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-149260OAI: oai:DiVA.org:uu-149260DiVA: diva2:404333
Available from: 2011-03-16 Created: 2011-03-16 Last updated: 2011-05-05Bibliographically 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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