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Tumor Hypoxia and Blood Vessel Detection
Author(s) -
LOUKAS CONSTANTINOS G.,
WILSON GEORGE D.,
VOJNOVIC BORIVOJ,
LINNEY ALF
Publication year - 2002
Publication title -
annals of the new york academy of sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.712
H-Index - 248
eISSN - 1749-6632
pISSN - 0077-8923
DOI - 10.1111/j.1749-6632.2002.tb04893.x
Subject(s) - artificial intelligence , pixel , segmentation , computer science , computer vision , pattern recognition (psychology) , image segmentation , cluster analysis , region of interest , image texture
A bstract : 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.