RESUMO
INTRODUCTION: Positron emission tomography (PET) is a state-of-the-art functional imaging technique used in the accurate detection of cancer. The main problem with the tumours present in the lungs is that they are non-stationary during each respiratory cycle. Tumours in the lungs can get displaced up to 2.5 cm during respiration. Accurate detection of the tumour enables avoiding the addition of extra margin around the tumour that is usually used during radiotherapy treatment planning. METHODS: This paper presents a novel method to detect and track tumour in respiratory-gated PET images. The approach followed to achieve this task is to automatically delineate the tumour from the first frame using support vector machines. The resulting volume and position information from the first frame is used in tracking its motion in the subsequent frames with the help of level set (LS) deformable model. RESULTS: An excellent accuracy of 97% is obtained using wavelets and support vector machines. The volume calculated as a result of the machine learning (ML) stage is used as a constraint for deformable models and the tumour is tracked in the remaining seven phases of the respiratory cycle. As a result, the complete information about tumour movement during each respiratory cycle is available in relatively short time. CONCLUSIONS: The combination of the LS and ML approach accurately delineated the tumour volume from all frames, thereby providing a scope of using PET images towards planning an accurate and effective radiotherapy treatment for lung cancer.
