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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Vydáno v:Multimedia systems Ročník 25; číslo 2; s. 127 - 133
Hlavní autoři: Wang, Fan, Lian, Chunfeng, Vera, Pierre, Ruan, Su
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2019
Springer Nature B.V
Springer Verlag
Témata:
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
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ISSN:0942-4962
1432-1882
DOI:10.1007/s00530-017-0579-0