Region growing and fuzzy C-means algorithm segmentation for PET images of head-neck tumours

The aim of this work, performed at Azienda Ospedialiero Universitaria in Modena, is the implementation and validation of autosegmentation methods of head and neck (H&N) tumor PET images. These autosegmentation processes are important mostly to overcome the problems of manual segmentation, performed by radiotherapist physician, regarding the contouring time (that can reach more than two hours) and the intra-observer and inter-observer variability. Fuzzy C-means (FCM) and Region Growing (RG) algorithms were developed in a MATLAB GUI that allows to choice iteratively the different steps necessary for a good segmentation. Pre-processing operations were previously applied to improve image quality: a gaussian filter to remove noise and an opening morphological operation to uniform background. NEMA IEC body phantom, acquired with four hot spheres and two cold spheres, was firstly used to test the two methods in known condition. The accuracy of processes was evaluated considering the volume change between calculated and theoretical volume that is always null within error and reaches the highest value in the case of the smallest sphere because of partial volume effect, generally decreasing as sphere size increases. Afterwards, 16 PET images studies of H&N tumors were used for clinical test of algorithms. The efficiency was estimated using two quantitative coefficients: Dice Similarity Index (DSC) and Average Hausdorff Distance (AHD). Mean DSC and AHD values, obtained mediating on all cases, are within literature threshold (0.6 for DSC and about 16 mm for AHD). Contouring time, required to segment all slices of each case, changes from few seconds in FCM to some minutes in RG, always remaining inferior to manual segmentation time. The results are satisfactory, however, they could be improved increasing the number of patients and testing the variability between more experts. FCM could be also applied to lymphomas to test the efficiency in the segmentation of displaced regions.