Table of Contents
ISRN Signal Processing
Volume 2012, Article ID 914232, 9 pages
Research Article

Semiautonomous Medical Image Segmentation Using Seeded Cellular Automaton Plus Edge Detector

Department of Engineering Technology and Industrial Distribution, Texas A&M University, College Station, TX 77843, USA

Received 27 January 2012; Accepted 15 March 2012

Academic Editors: C. Alberola-Lopez, Y. H. Ha, C. S. Lin, C. Sun, and B. Yuan

Copyright © 2012 Ryan A. Beasley. 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.


Segmentations of medical images are required in a number of medical applications such as quantitative analyses and patient-specific orthotics, yet accurate segmentation without significant user attention remains a challenge. This work presents a novel segmentation algorithm combining the region-growing Seeded Cellular Automata with a boundary term based on an edge-detected image. Both single processor and parallel processor implementations are developed and the algorithm is shown to be suitable for quick segmentations (2.2 s for 2 5 6 × 2 5 6 × 1 2 4 voxel brain MRI) and interactive supervision (2–220 Hz). Furthermore, a method is described for generating appropriate edge-detected images without requiring additional user attention. Experiments demonstrate higher segmentation accuracy for the proposed algorithm compared with both Graphcut and Seeded Cellular Automata, particularly when provided minimal user attention.