Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
A viscosity solutions approach to shape-from-shading
SIAM Journal on Numerical Analysis
Variational methods in image segmentation
Variational methods in image segmentation
Signal Processing - Special issue on deformable models and techniques for image and signal processing
International Journal of Computer Vision
Gradient flows and geometric active contour models
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
A Real-Time Algorithm for Medical Shape Recovery
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Morphological multiscale decomposition of connected regions with emphasis on cell clusters
Computer Vision and Image Understanding
Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies
On the decomposition of cell clusters
Journal of Mathematical Imaging and Vision
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In medical microscopy, image analysis offers to pathologist a modern tool, which can be applied to several problems in cancerology: quantification of DNA content, quantification of immunostaining, nuclear mitosis counting, characterization of tumor tissue architecture. However, these problems need an accurate and automatic segmentation. In most cases, the segmentation is concerned with the extraction of cell nuclei or cell clusters. In this paper, we address the problem of the fully automatic segmentation of grey level intensity or color images from medical microscopy. An automatic segmentation method combining fuzzy clustering and multiple active contour models is presented. Automatic and fast initialization algorithm based on fuzzy clustering and morphological tools are used to robustly identify and classify all possible seed regions in the color image. These seeds are propagated outward simultaneously to refine contours of all objects. A fast level set formulation is used to model the multiple contour evolution. Our method is illustrated through two representative problems in cytology and histology.