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Title

Artificial Neural Network-Based System for PET Volume Segmentation

Authors
Sharif, Mhd Saeed
Abbod, Maysam
Amira, Abbes
Published in International Journal of Biomedical Imaging. 2010, vol. 2010
Abstract 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.
Identifiers
PMID: 20936152
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Research group Imagerie Médicale (TEP et TEMP) (542)
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SHARIF, Mhd Saeed et al. Artificial Neural Network-Based System for PET Volume Segmentation. In: International Journal of Biomedical Imaging, 2010, vol. 2010. https://archive-ouverte.unige.ch/unige:21310

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Deposited on : 2012-05-23

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