Towards an automatic tumor segmentation using iterative watersheds

Matei Mancas; Bernard Gosselin

doi:10.1117/12.535017

12 May 2004 Towards an automatic tumor segmentation using iterative watersheds

Matei Mancas, Bernard Gosselin

Proceedings Volume 5370, Medical Imaging 2004: Image Processing; (2004) https://doi.org/10.1117/12.535017
Event: Medical Imaging 2004, 2004, San Diego, California, United States

Abstract

This paper introduces a simple knowledge model on CT (Computed Tomography) images which provides high level information. A novel method called iterative watersheds is then used in order to segment the tumors. Moreover, a fully automatic tumor segmentation method was tested by using image registration. Some preliminary results are very encouraging and give us hope to obtain an interesting tool for the clinic. Tests were made on head and neck images, nevertheless, this is a generic method working on all kinds of tumors. The iterative watersheds and our model are first introduced, then PET (Positron Emission Tomography) images registration on CT is described. Some results of iterative watersheds are compared using either the semi-automatic or fully automatic mode. Finally we conclude by a discussion about operator's interaction and important future work.

Citation Download Citation

Matei Mancas and Bernard Gosselin "Towards an automatic tumor segmentation using iterative watersheds", Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); https://doi.org/10.1117/12.535017

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