Semiautonomous Medical Image Segmentation Using Seeded Cellular Automaton Plus Edge Detector. (17th May 2012)

• Record Type: Journal Article
• Authors: Beasley, Ryan A.
• Other Names: Alberola-Lopez C. Academic Editor.; Ha Y. H. Academic Editor.; Lin C. S. Academic Editor.; Sun C. Academic Editor.; Yuan B. Academic Editor.
• Is Part Of: ISRN signal processing. Volume 2012(2012)
• Publication Date: 2012-05-17
• Subjects: Signal processing -- Periodicals; Signal processing; Periodicals; 621.3822
• DOI: 10.5402/2012/914232
• Languages: English
• ISSNs: 2090-5041

Abstract

Abstract : 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.