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Advances in Fuzzy Systems
Volume 2012 (2012), Article ID 327861, 10 pages
http://dx.doi.org/10.1155/2012/327861
Research Article

Artificial Neural Network-Statistical Approach for PET Volume Analysis and Classification

1Department of Electronic and Computer Engineering, School of Engineering and Design, Brunel University, Uxbridge UB83PH, UK
2Nanotechnology and Integrated BioEngineering Centre, University of Ulster, Newtownabbey BT37OQB, UK
3Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland
4Geneva Neuroscience Center, Geneva University, CH-1205 Geneva, Switzerland
5Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, 9700 RB Groningen, The Netherlands

Received 14 September 2011; Accepted 12 January 2012

Academic Editor: Jiann-Shing Shieh

Copyright © 2012 Mhd Saeed Sharif et al. 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.

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.