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We have developed a multistage image analysis technique for the simultaneous segmentation of blood vessels and hypoxic regions in dual-stained tumor tissue sections. The algorithm, which is integrated in a task-oriented image analysis system developed on-site, initially uses the K-nearest neighbor classification rule in order to label the image pixels. Classification is based on a training set selected from manually drawn regions corresponding to the areas of interest. If the output image contains a significant number of misclassified pixels, the user has the option to apply a series of specific problem-designed routines (texture analysis, fuzzy c-means clustering, and edge detection) in order to improve the final segmentation result. Validation experiments indicate that the algorithm can robustly detect these biological features, even in tissue sections with a very low quality of staining. This approach has also been combined with other image analysis based procedures in order to objectively obtain quantitative measurements of potential clinical interest.


Journal article


Ann N Y Acad Sci

Publication Date





125 - 138


Algorithms, Carcinoma, Cell Hypoxia, Endothelium, Vascular, Fuzzy Logic, Humans, Image Processing, Computer-Assisted, Microscopy, Urinary Bladder Neoplasms