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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.

Original publication




Conference paper

Publication Date





52 - 57