An intelligent system for pet tumour detection and quantification

Authors:
Mhd Saeed Sharif;Abbes Amira
Affiliations:
Electronic and Computer Engineering, School of Engineering and Design, Brunel University, West London, United Kingdom;Electronic and Computer Engineering, School of Engineering and Design, Brunel University, West London, United Kingdom
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

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. There are many techniques for segmenting medical images, in which some of the approaches have poor accuracy and require a lot of time for analyzing large medical volumes. 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 the region of interest precisely, and model the clinical evaluation. This paper proposes an intelligent system based on multilayer ANN, multiresolution analysis, and thresholding. The system has been evaluated and tested on phantom and real PET images, promising results have been achieved.