Intelligent approach for PET volume analysis

Tumour classification and quantification in positron emission tomography (PET) imaging at early stage of illness are important for radiotherapy planning, tumour diagnosis, and fast recovery. Analysing large medical volumes using traditional techniques requires a decent amount of time, and in some approaches poor accuracy is achieved. Artificial intelligence (AI) technologies can provide better accuracy and save decent amount of time. Artificial neural network (ANN), as one of the best AI technologies, has the capability to classify, measure precisely the region of interest, and model the clinical evaluation for a specific problem. This paper presents a novel application of the ANN in the wavelet domain for PET volume segmentation. ANN performance evaluation using different number of hidden neurons is also considered. The proposed intelligent system outputs are compared with the outputs of thresholding, and clustering based approaches. Two PET phantom data sets and real PET volumes have been utilised to validate the proposed system which has shown promising results.