Live-Wires on Edges of Presegmented 2D-Data

To read the full-text of this research, you can request a copy directly from the authors.


In this article we present an extension to the Live-Wire segmentation approach. An automatic preprocessing is done before the interactive segmentation and thus provides an additional level of abstraction. The amount of the interactively processed data is reduced and additional problem specific knowledge is included. We achieve a data reduction of a factor of >6 for a typical MRT image of the human brain.

No full-text available

Request Full-text Paper PDF

To read the full-text of this research,
you can request a copy directly from the authors.

ResearchGate has not been able to resolve any citations for this publication.
Conference Paper
Full-text available
This paper presents a user-steered segmentation algorithm based on the livewire paradigm. Livewire is an image-feature driven method that finds the optimal path between user-selected image locations, thus reducing the need to manually define the complete boundary. We introduce an image feature based on local phase, which describes local edge symmetry independent of absolute gray value. Because phase is amplitude invariant, the measurements are robust with respect to smooth variations, such as bias field inhomogeneities present in all MR images. In order to enable validation of our segmentation method, we have created a system that continuously records user interaction and automatically generates a database containing the number of user interactions, such as mouse events, and time stamps from various editing modules. We have conducted validation trials of the system and obtained expert opinions regarding its functionality.
Conference Paper
Full-text available
Segmenting medical structures is mandatory in any computer assisted surgery system. This major field must be addressed in order to build realistic and accurate 3D models of patient individual anatomical structures.Magnetic Resonance Imaging (MRI) is becoming part of daily routine in clinical work. Whereas scanning speed and slice numbers increase each year, segmenting such data is still a challenging problem. Moreover, the segmentation stage remains time limiting in pre-operative planning and intra-operative guidance. Indeed, interactive tools, like live wire or intensity-based thresholding, requires a pre or post-filtering to homogenize areas. Common medical filters, such as median or morphology-based, are actually non adapted for MR noise removal. Their main side effect is to remove boundaries when applied on Gaussian corrupted data. Next, numerous steps spend efforts in reconstructing lost information and current approaches are therefore non interactive.
In the past, we have presented three user-steered image segmentation paradigms: live wire, live lane, and the 3D extension of the live-wire method. In this paper, we introduce an ultra-fast live-wire method, referred to as live-wire-on-the-fly, for further reducing user's time compared to live wire. For both approaches, given a slice and a 2D boundary of interest in this slice, we translate the problem of finding the best boundary segment between any two points specified by the user on this boundary to the problem of finding the minimum-cost path between two vertices in a weighted and directed graph. The entire 2D boundary is identified as a set of consecutive boundary segments, each specified and detected in this fashion. A drawback in live wire is that the speed for optimal path computation depends on image size, compromising the overall segmentation efficiency. In this work, we solve this problem by exploiting some properties of graph theory to avoid unnecessary minimum-cost path computation during segmentation. Based on 164 segmentation experiments from an actual medical application, we demonstrate that live-wire- on-the-fly is about 1.5 to 33 times faster than live wire for actual segmentation, although the pure computational part alone is found to be over a hundred times faster.
We present here a new algorithm for segmentation of intensity images which is robust, rapid, and free of tuning parameters. The method, however, requires the input of a number of seeds, either individual pixels or regions, which will control the formation of regions into which the image will be segmented. In this correspondence, we present the algorithm, discuss briefly its properties, and suggest two ways in which it can be employed, namely, by using manual seed selection or by automated procedures
  • E N Mortensen
  • W Barrett
Mortensen E N, Barrett W A: Interactive Segmentation with Intelligent Scissors. Graphical Models and Image Processing, no. 60, pp. 349-384, 1998.
Interactive Segmentation and Visualization of Volume Data Sets. Late Breaking Hot Topics, Visualization pp
  • G Kühne
  • C Poliwoda
  • J Hesser
  • R Männer
Kühne G, Poliwoda C, Hesser J, Männer R: Interactive Segmentation and Visualization of Volume Data Sets. Late Breaking Hot Topics, Visualization pp. 9-12, 1997.
Simulated Brain Database
  • Brainweb
BrainWeb: Simulated Brain Database.