Tumor hypoxia and blood vessel detection: an image analysis technique for simultaneous tumor hypoxia grading and blood vessel detection in tissue sections.
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.
Loukas
Constantinos G
CG
Gray Cancer Institute, Mount Vernon Hospital, Northwood, Middlesex, United Kingdom. c.loukas@ion.ucl.ac.uk
Wilson
George D
GD
Vojnovic
Borivoj
B
Linney
Alf
A
D000465:Algorithms; D002277:Carcinoma / Q000098:blood supply / Q000473:pathology; D015687:Cell Hypoxia; D004730:Endothelium, Vascular / Q000473:pathology; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008853:Microscopy / Q000379:methods; D001749:Urinary Bladder Neoplasms / Q000098:blood supply* / Q000473:pathology*