Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/7222
Title: Artificial neural network-statistical approach for PET volume analysis and classification
Authors: Sharif, MS
Abbod, MF
Amira, A
Zaidi, H
Issue Date: 2012
Publisher: Hindawi Publishing Corporation
Citation: Advances in Fuzzy Systems, 2012: 327861, Jan 2012
Abstract: The increasing number of imaging studies and the prevailing application of positron emission tomography (PET) in clinical oncology have led to a real need for efficient PET volume handling and the development of new volume analysis approaches to aid the clinicians in the clinical diagnosis, planning of treatment, and assessment of response to therapy. A novel automated system for oncological PET volume analysis is proposed in this work. The proposed intelligent system deploys two types of artificial neural networks (ANNs) for classifying PET volumes. The first methodology is a competitive neural network (CNN), whereas the second one is based on learning vector quantisation neural network (LVQNN). Furthermore, Bayesian information criterion (BIC) is used in this system to assess the optimal number of classes for each PET data set and assist the ANN blocks to achieve accurate analysis by providing the best number of classes. The system evaluation was carried out using experimental phantom studies (NEMA IEC image quality body phantom), simulated PET studies using the Zubal phantom, and clinical studies representative of nonsmall cell lung cancer and pharyngolaryngeal squamous cell carcinoma. The proposed analysis methodology of clinical oncological PET data has shown promising results and can successfully classify and quantify malignant lesions.
Description: Copyright © 2012 The Authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
This article has been made available through the Brunel Open Access Publishing Fund.
URI: http://www.hindawi.com/journals/afs/2012/327861/
http://bura.brunel.ac.uk/handle/2438/7222
DOI: http://dx.doi.org/10.1155/2012/327861
ISSN: 1687-7101
Appears in Collections:Electronic and Computer Engineering
Publications
Brunel OA Publishing Fund
Dept of Electronic and Computer Engineering Research Papers

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