NDLI: 3D Oncological PET volume analysis using CNN and LVQNN

Content Provider	IEEE Xplore Digital Library
Author	Sharif, M.S. Amira, A. Zaidi, H.
Copyright Year	2010
Description	Author affiliation: Division of Nuclear Medicine, Geneva University Hospital, CH-1211 Geneva, Switzerland (Zaidi, H.) \|\| School of Engineering and Design Brunei University, West London, United Kingdom (Sharif, M.S.; Amira, A.)
Abstract	The increasing numbers of patient scans and the prevailing application of positron emission tomography (PET) in clinical oncology have led to a need for efficient PET volume handling and the development of new volume analysis approaches to aid clinicians in the diagnosis of disease and planning of treatment. A novel automated system for oncological PET volume segmentation is proposed in this paper. The proposed intelligent system is using competitive neural network (CNN) and learning vector quantisation neural network (LVQNN) for clustering and quantifying phantom and real PET volumes. Bayesian information criterion (BIC) has been used in this system to assess the optimal number of clusters for each PET data set. The experimental study using phantom PET volume was conducted for quantitative evaluation of the performance of the proposed segmentation algorithm. The analysis of the resulting segmentation of clinical oncological PET data seems to confirm that this approach shows promise and can successfully segment patient lesion.
Starting Page	1783
Ending Page	1786
File Size	191609
Page Count	4
File Format	PDF
ISBN	9781424453085
DOI	10.1109/ISCAS.2010.5537649
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-30
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Positron emission tomography Cellular neural networks Neural networks Imaging phantoms Oncology Diseases Medical treatment Intelligent systems Intelligent networks Competitive intelligence Bayesian Information Criterion Medical Volume Analysis Positron Emission Tomography Tumour
Content Type	Text
Resource Type	Article

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