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|University of Cambridge > Engineering Department > Machine Intelligence Lab|
THE "CREEP-AND-MERGE" SEGMENTATION SYSTEM
Antranig Basman, Joan Lasenby and Roberto Cipolla
We present a novel architecture for region-based segmentation of stationary and quasi-stationary statistics, which is designed to function correctly under the widest range of conditions. It is robust to the extremes of region topology and connectivity, and automatically maintains region boundaries sampled to the minimum scale at which the region configuration can be determined with statistical confidence. The algorithm is deterministic, and when operating on images from within its domain of validity, contains no adjustable parameters. In contrast to most other techniques directed at the same problem, the progress of the algorithm cannot be described by the optimisation of a global energy criterion.
We initially describe the system in an implementation--neutral form, and discuss the properties required of a statistical engine suitable for connection to it - the system is modular, and allows for straightforward replacement of the driving statistics by a wide variety of alternatives.
A specific implementation for 2-dimensional images is then presented and illustrated. Quantitative evaluation shows superior results to a collection of other systems. We then illustrate a successful application of the system, together with higher-level processing, to the difficult problem of segmentation of medical images gathered by ultrasound.
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