Despite CA being a fairly abstract topic, its direct applications will be illustrated with practical results. The main objective of the project is to build an innovative suite of techniques for unsupervised medical image segmentation using CA. Three separate strands in approaching this problem will constitute separate objectives:
- Techniques using CA with hyper-connected topologies
- Techniques using CA with sparse topologies
- Techniques using CA with hierarchical topologies
Expected results:
2015: monitoring plan, progress reports, knowledge transfer sessions, scientific sessions, medical image database, access to and knowledge of Millipede, project webpage
2016: progress reports, audit report, at least one survey paper, knowledge transfer sessions, at least one scientific session, at least one research visit to University of Oxford, at least one research visit to Babes-Bolyai University, new image segmentation techniques based on Cellular Automata with hyper-connected topologies, new hybrid techniques that contain elements of Cellular Automata with hyper-connected topologies, new image segmentation techniques based on Cellular Automata with sparse topologies, updated project webpage, publications, presentations or posters, public reports
2017: progress reports, final report, audit report, scientific sessions, research visits to University of Oxford, research visits to Babes-Bolyai University, new hybrid techniques that contain elements of Cellular Automata with sparse topologies, new image segmentation techniques based on Cellular Automata with hierarchical topologies, new hybrid techniques that contain elements of Cellular Automata with hierarchical topologies, updated project webpage, publications, presentations or posters, public reports, workshop
Activity reports:
Scientific report 2015
Scientific report 2015-2016
Scientific report 2015-2017