A novel fuzzy C-means algorithm for unsupervised heterogeneous tumor quantification in PET

Purpose: Accurate and robust image segmentation was identified as one of the most challenging issues facing PET quantification in oncological imaging. This difficulty is compounded by the low spatial resolution and high noise characteristics of PET images. The fuzzy C-means (FCM) clustering algorith...

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Veröffentlicht in:	Medical physics (Lancaster) Jg. 37; H. 3; S. 1309 - 1324
Hauptverfasser:	Belhassen, Saoussen, Zaidi, Habib
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States American Association of Physicists in Medicine 01.03.2010
Schlagworte:	Algorithms anisotropic diffusion filtering Artificial Intelligence cancer Carcinoma, Non-Small-Cell Lung - diagnostic imaging cellular biophysics Computed tomography Diffusion discrete wavelet transforms FCM segmentation Fuzzy Logic Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods image segmentation Integral transforms intensity heterogeneity lung Lung Neoplasms - diagnostic imaging Medical image noise medical image processing Medical image segmentation Medical image spatial resolution Medical imaging Neural networks, fuzzy logic, artificial intelligence Pattern Recognition, Automated - methods phantoms Phantoms, Imaging positron emission tomography positron emission tomography (PET) Positron-Emission Tomography - instrumentation Positron-Emission Tomography - methods Reproducibility of Results Segmentation Sensitivity and Specificity tumours Wavelet transform Wavelets à trous wavelet transform intensity heterogeneity à trous wavelet transform FCM segmentation positron emission tomography (PET) anisotropic diffusion filtering
ISSN:	0094-2405, 2473-4209
Online-Zugang:	Volltext
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