Semiautonomous Medical Image Segmentation Using Seeded Cellular Automaton Plus Edge Detector
Author
Source
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-05-17
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Electronic engineering
Information Technology and Computer Science
Abstract EN
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 256×256×124 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.
American Psychological Association (APA)
Beasley, Ryan A.. 2012. Semiautonomous Medical Image Segmentation Using Seeded Cellular Automaton Plus Edge Detector. ISRN Signal Processing،Vol. 2012, no. 2012, pp.1-9.
https://search.emarefa.net/detail/BIM-507661
Modern Language Association (MLA)
Beasley, Ryan A.. Semiautonomous Medical Image Segmentation Using Seeded Cellular Automaton Plus Edge Detector. ISRN Signal Processing No. 2012 (2012), pp.1-9.
https://search.emarefa.net/detail/BIM-507661
American Medical Association (AMA)
Beasley, Ryan A.. Semiautonomous Medical Image Segmentation Using Seeded Cellular Automaton Plus Edge Detector. ISRN Signal Processing. 2012. Vol. 2012, no. 2012, pp.1-9.
https://search.emarefa.net/detail/BIM-507661
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-507661