title: Segmentation of cells with partial occlusion and part configuration constraint using evolutionary computation.
abstract: We propose a method for targeted segmentation that identifies and delineates only those spatially-recurring objects that conform to specific geometrical, topological and appearance priors. By adopting a "tribes"-based, global genetic algorithm, we show how we incorporate such priors into a faithful objective function unconcerned about its convexity. We evaluated our framework on a variety of histology and microscopy images to segment potentially overlapping cells with complex topology. Our experiments confirmed the generality, reproducibility and improved accuracy of our approach compared to competing methods.
authors: Medical Image Analysis Lab., Simon Fraser University, BC, Canada. smn6@sfu.ca
authors: Masoud S
authors: MS
authors: Nosrati
authors: Medical Image Analysis Lab., Simon Fraser University, BC, Canada.
authors: Ghassan
authors: G
authors: Hamarneh
mesh_terms: D000465:Algorithms; D000818:Animals; D058948:Cell Tracking / Q000379:methods*; D002478:Cells, Cultured / Q000166:cytology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D013382:Subtraction Technique