Adaptive kernelized evidential clustering for automatic 3D tumor segmentation in FDG-PET images

摘要

Automatically and reliably delineating tumor contours in noisy and blurring PET images is a challenging work in clinical oncology. In this paper, we introduce a specific unsupervised learning method to this end. More specifically, a robust clustering algorithm with spatial knowledge enhancement is developed in the framework of belief functions, a formal and powerful tool for modeling and reasoning with uncertain and/or imprecise information. Diverse patch-based image features are extracted to comprehensively describe PET image voxels. Then, informative input features are iteratively selected to learn an adaptive kernel-induced metric in an unsupervised way, so as to precisely grouping voxels into different clusters. The effectiveness of the proposed method has been evaluated on FDG-PET images for lung tumor patients.