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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Vydané v:	Multimedia systems Ročník 25; číslo 2; s. 127 - 133
Hlavní autori:	Wang, Fan, Lian, Chunfeng, Vera, Pierre, Ruan, Su
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2019 Springer Nature B.V Springer Verlag
Predmet:	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
On-line prístup:	Získať plný text
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