摘要：Tumour detection, classification, and quantification in positron
emission tomography (PET) imaging at early stage of disease are important
issues for clinical diagnosis, assessment of response to treatment, and radiotherapy
planning. Many techniques have been proposed for segmenting medical imaging
data; however, some of the approaches have poor performance, large inaccuracy,
and require substantial computation time for analysing large medical volumes.
Artificial intelligence (AI) approaches can provide improved accuracy and save
decent amount of time. Artificial neural networks (ANNs), as one of the best
AI techniques, have the capability to classify and quantify precisely lesions and
model the clinical evaluation for a specific problem. This paper presents a
novel application of ANNs in the wavelet domain for PET volume segmentation.
ANN performance evaluation using different training algorithms in both spatial
and wavelet domains with a different number of neurons in the hidden layer is
also presented. The best number of neurons in the hidden layer is determined
according to the experimental results, which is also stated Levenberg-Marquardt
backpropagation training algorithm as the best training approach for the proposed
application. The proposed intelligent system results are compared with those
obtained using conventional techniques including thresholding and clustering
based approaches. Experimental and Monte Carlo simulated PET phantom data
sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised
to validate the proposed algorithm which has demonstrated promising results.
