Image-analysis-based assessment of hypoxia and vasculature in bladder tumors

Constantinos G. Loukas; George D. Wilson; Borivoj Vojnovic; Alfred David Linney

doi:10.1117/12.440254

18 September 2001 Image-analysis-based assessment of hypoxia and vasculature in bladder tumors

Constantinos G. Loukas, George D. Wilson, Borivoj Vojnovic, Alfred David Linney

Proceedings Volume 4549, Medical Image Acquisition and Processing; (2001) https://doi.org/10.1117/12.440254
Event: Multispectral Image Processing and Pattern Recognition, 2001, Wuhan, China

Abstract

Tumour hypoxia is an important biological feature that is very close related to vasculature, and it has been proved to play a crucial role in the radiation response of solid tumours. In this paper we present a novel image analysis technique for simultaneous tumour hypoxia grading and blood vessel detection in dual-stained tissue sections, originated from the bladder region of patients treated by radiotherapy. The K-Nearest Neighbour classification scheme is employed initially in order to label the image colour pixels. Classification is based on a training set selected from manually drawn regions corresponding to the biological patterns being segmented. For tissue section images presenting a low quality staining, some further processing is required to reject misclassified pixels. A series of specific task-oriented routines have been developed (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 very inhomogeneous staining. This approach has also been combined with other image analysis procedures to objectively obtain quantitative measurements of potential clinical interest.

Citation Download Citation

Constantinos G. Loukas, George D. Wilson, Borivoj Vojnovic, and Alfred David Linney "Image-analysis-based assessment of hypoxia and vasculature in bladder tumors", Proc. SPIE 4549, Medical Image Acquisition and Processing, (18 September 2001); https://doi.org/10.1117/12.440254

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