24 June 1998 Automatic object selection in computer-assisted microscopy
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Abstract
The characterization of tumor aggressiveness is a very important step in cancer diagnosis and treatment. This can be achieved by examining the cells nuclei of the tissue. In order to perform a statistical study on a population of such nuclei, we have to characterize nuclei one by one. The problem is that the nuclei often appear in clusters and that other elements (totally unrepresentative of the studied tissue) can be present on the image. Moreover, we have to discard nuclei which do not guarantee valid data (broken nuclei, folded nuclei...). The purpose of our work is to separate clusters of nuclei into single-cell nuclei and eliminate undesirable elements of the image. Until now, this task was made by hand and was extremely slow and repetitive. Moreover, it implied a subjective basis, depending on the human operator. The method we developed is totally automatic. It is based on the elaboration of a binary mask containing objects which will be examined separately. Our algorithm has been tested on a large set of images coming from different tissues and the results are compared with the same task performed by human operators.
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Thierry Leloup, Thierry Leloup, Nadine Lasudry, Nadine Lasudry, Robert Kiss, Robert Kiss, Philippe Van Ham, Philippe Van Ham, } "Automatic object selection in computer-assisted microscopy", Proc. SPIE 3338, Medical Imaging 1998: Image Processing, (24 June 1998); doi: 10.1117/12.310842; https://doi.org/10.1117/12.310842
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