The increasing number of imaging studies and the prevailing application of positron emission tomography (PET) in clinical\r\noncology have led to a real need for efficient PET volume handling and the development of new volume analysis approaches to aid\r\nthe clinicians in the clinical diagnosis, planning of treatment, and assessment of response to therapy. A novel automated system for\r\noncological PET volume analysis is proposed in this work. The proposed intelligent system deploys two types of artificial neural\r\nnetworks (ANNs) for classifying PET volumes. The first methodology is a competitive neural network (CNN), whereas the second\r\none is based on learning vector quantisation neural network (LVQNN). Furthermore, Bayesian information criterion (BIC) is used\r\nin this system to assess the optimal number of classes for each PET data set and assist the ANN blocks to achieve accurate analysis\r\nby providing the best number of classes. The system evaluation was carried out using experimental phantom studies (NEMA IEC\r\nimage quality body phantom), simulated PET studies using the Zubal phantom, and clinical studies representative of nonsmall\r\ncell lung cancer and pharyngolaryngeal squamous cell carcinoma. The proposed analysis methodology of clinical oncological PET\r\ndata has shown promising results and can successfully classify and quantify malignant lesions.
Loading....