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

Article


Sharif, M., Abbod, Maysam, Amira, Abbes and Zaidi, Habib 2012. Artificial Neural Network-Statistical Approach for PET Volume Analysis and Classification. Advances in Fuzzy Systems. 2012 (327861). https://doi.org/10.1155/2012/327861
AuthorsSharif, M., Abbod, Maysam, Amira, Abbes and Zaidi, Habib
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.

JournalAdvances in Fuzzy Systems
Journal citation2012 (327861)
ISSN1687-7101
1687-711X
Year2012
PublisherHindawi Publishing Corporation
Publisher's version
License
CC BY
Digital Object Identifier (DOI)https://doi.org/10.1155/2012/327861
Publication process dates
Deposited06 Mar 2017
Accepted12 Jan 2012
Accepted12 Jan 2012
FunderSwiss National Science Foundation
Geneva Cancer League
Indo Swiss Joint Research Programme (ISJRP)
Swiss National Science Foundation
Geneva Cancer League
Indo Swiss Joint Research Programme
Copyright information© 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.
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