Spatial Evidential Clustering With Adaptive Distance Metric for Tumor Segmentation in FDG-PET Images

While the accurate delineation of tumor volumes in FDG-positron emission tomography (PET) is a vital task for diverse objectives in clinical oncology, noise and blur due to the imaging system make it a challenging work. In this paper, we propose to address the imprecision and noise inherent in PET u...

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Vydané v:	IEEE transactions on biomedical engineering Ročník 65; číslo 1; s. 21 - 30
Hlavní autori:	Lian, Chunfeng, Ruan, Su, Denoeux, Thierry, Li, Hua, Vera, Pierre
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.01.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Predmet:	Adaptive distance metric Algorithms Carcinoma, Non-Small-Cell Lung - diagnostic imaging Cluster Analysis Clustering Clustering algorithms dempster–Shafer theory (DST) Electronic countermeasures Electronic mail Engineering Sciences evidential <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">c -means Feature extraction feature selection Fluorodeoxyglucose F18 - therapeutic use Humans Image Interpretation, Computer-Assisted - methods Image processing Image quality Image segmentation Lung Neoplasms - diagnostic imaging lung tumor segmentation Measurement Oncology Positron emission Positron emission tomography positron emission tomography (PET) images Positron-Emission Tomography - methods Regularization Signal and Image processing spatial regularization Tomography Tumors evidential c-means dempster–Shafer theory (DST) positron emission tomography (PET) images lung tumor segmentation spatial regularization Adaptive distance metric feature selection
ISSN:	0018-9294, 1558-2531, 1558-2531
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