Robust cell image segmentation methods.
2004 (English)In: Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications, ISSN 1054-6618, Vol. 14, no 2, 157-167 p.Article in journal (Refereed) Published
Biomedical cell image analysis is one of the main application fields of computerized image analysis. This paper outlines the field and the different analysis steps related to it. Relative advantages of different approaches to the crucial step of image segmentation are discussed. Cell image segmentation can be seen as a modeling problem where different approaches are more or less explicitly based on cell models. For example, thresholding methods can be seen as being based on a model stating that cells have an intensity that is different from the surroundings. More robust segmentation can be obtained if a combination of features, such as intensity, edge gradients, and cellular shape, is used. The seeded watershed transform is proposed as the most useful tool for incorporating such features into the cell model. These concepts are illustrated by three real-world problems.
Place, publisher, year, edition, pages
2004. Vol. 14, no 2, 157-167 p.
Computer Vision and Robotics (Autonomous Systems)
IdentifiersURN: urn:nbn:se:uu:diva-67593OAI: oai:DiVA.org:uu-67593DiVA: diva2:95504