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 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.
Highlights
Segmentation, known as labeling, of medical images is an important task for quantitative analyses, custom intervention planning such as localized radiotherapy, and design of patient-specific tools such as orthotics or jigs for joint replacement
Many of the Graphcut 3slice seed segmentations suffer from the same issue, while the others are on par with the Seeded Cellular Automata (SCA) segmentation accuracies
This paper presents a novel segmentation algorithm, Seeded Cellular Automata Plus Edge detector (SCAPE), combining a region-growing algorithm with edge-detected images to improve behavior at weak borders
Summary
Segmentation, known as labeling, of medical images is an important task for quantitative analyses, custom intervention planning such as localized radiotherapy, and design of patient-specific tools such as orthotics or jigs for joint replacement. Popular supervised segmentation algorithms include active contours (snakes) [1], Level Sets [2], intelligent scissors (live wire) [3, 4], Graphcut [5], and to some degree Seeded Cellular Automata (SCA) [6]. For active contours and Level Sets, the user initializes the boundary near the desired contour and the algorithm moves the boundary to a local minimum determined by an energy functional. This approach requires that the user solve two optimization problems: setting the impact of the terms in the functional, and placing the initial boundary such that the results settle into a desirable minimum [7]. SCA grows regions based on local information making it well suited to parallel hardware [6], and has a constant computation time with respect to the number of labels, but SCA is prone to bleed through at weak boundaries [9]
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