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

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 d...

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Veröffentlicht in:	Multimedia systems Jg. 25; H. 2; S. 127 - 133
Hauptverfasser:	Wang, Fan, Lian, Chunfeng, Vera, Pierre, Ruan, Su
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2019 Springer Nature B.V Springer Verlag
Schlagworte:	Algorithms Blurring Clustering Computer Communication Networks Computer Graphics Computer Science Cryptology Data Storage Representation Feature extraction Health risk assessment Image segmentation Life Sciences Machine learning Operating Systems Positron emission Special Issue Paper Tomography Tumors Automatic tumor segmentation Adaptive kernel metric Theory of belief functions Unsupervised learning PET images, 3D PET images 3D
ISSN:	0942-4962, 1432-1882
Online-Zugang:	Volltext
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