Sophie Schüpp

Image segmentation via multiple active contour models and fuzzy clustering with biomedical applications

We address the problem of automatically segmenting cell nuclei or cluster of cell nuclei in image medical microscopy. We present a system of automatic segmentation combining fuzzy clustering and multiple active contour models. An automatic initialization algorithm based on fuzzy clustering is used to robustly identify and classify all possible seed regions in the image. These seeds are propagated outward simultaneously to localize the final contours of all objects. We present examples of quantitative segmentation on biomedical images: segmentation of lobules in color images of histology and segmentation of nuclei in cytological images.

Using active contours and mathematical morphology tools for quantification of immunohistochemical images

An image segmentation method is proposed, which combines mathematical morphology tools and active contours in two stages. First, contours are coarsely approximated by means of morphological operators. Second, these initial contours evolve under the influence of geometric and grey-level information, owing to the model of active contours. The performance of the method is evaluated according to the noise and is compared to the watershed algorithm. Then an application is finally presented for biomedical images of tumour tissue.

Fast Statistical Level Sets Image Segmentation for Biomedical Applications

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.

Fast and simple discrete approach for active contours for biomedical applications

In this paper, we present a fast and simple discrete approach for active contours. It is based on discrete contour evolution, which operates on the boundary of digital shape, by iterative growth processes on the boundary of the shape. We consider a curve to be the boundary of a discrete shape. We attach at each point of the boundary a cost function and deform this shape according to that cost function. The method presents some advantages. It is a discrete method, which takes an implicit representation and uses discrete algorithm with a simple data structure.