Semiautonomous Medical Image Segmentation Using Seeded Cellular Automaton Plus Edge Detector

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.