Fast Graph Partitioning Active Contours for Image Segmentation Using Histograms

Open Access
Research Article


We present a method to improve the accuracy and speed, as well as significantly reduce the memory requirements, for the recently proposed Graph Partitioning Active Contours (GPACs) algorithm for image segmentation in the work of Sumengen and Manjunath (2006). Instead of computing an approximate but still expensive dissimilarity matrix of quadratic size, Open image in new window , for a 2D image of size Open image in new window and regular image tiles of size Open image in new window , we use fixed length histograms and an intensity-based symmetric-centrosymmetric extensor matrix to jointly compute terms associated with the complete Open image in new window dissimilarity matrix. This computationally efficient reformulation of GPAC using a very small memory footprint offers two distinct advantages over the original implementation. It speeds up convergence of the evolving active contour and seamlessly extends performance of GPAC to multidimensional images.


Image Processing Pattern Recognition Computer Vision Image Segmentation Active Contour 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Publisher note

To access the full article, please see PDF.

Copyright information

© S. K. Nath and K. Palaniappan. 2009

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of MissouriColumbiaUSA

Personalised recommendations