Artificial Neural Network-Statistical Approach for PET Volume Analysis and Classification. (22nd March 2012)

• Record Type: Journal Article
• Authors: Sharif, Mhd Saeed; Abbod, Maysam; Amira, Abbes; Zaidi, Habib
• Other Names: Shieh Jiann-Shing Academic Editor.
• Is Part Of: Advances in fuzzy systems. Volume 2012(2012)
• Publication Date: 2012-03-22
• Subjects: Fuzzy systems -- Periodicals; Systèmes flous; Fuzzy systems; Periodicals; 511.313
• DOI: 10.1155/2012/327861
• Languages: English
• ISSNs: 1687-7101

Abstract

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.