A segmentation concept for positron emission tomography imaging using multiresolution analysis
Positron emission tomography (PET) imaging is an emerging medical imaging modality. Due to its high sensitivity and ability to model function, it is effective in identifying active regions that may be associated with various types of tumours. Increasing numbers of patient scans have led to an urgent need for efficient data archival and the development of new image analysis techniques to aid clinicians in the diagnosis of disease. Additionally, to handle the large volumes of data generated using complex processing algorithms, it is becoming evident that co-processing solutions are essential. In this paper, an automated system for the segmentation of oncological PET data is developed. Initially, the Bayesian information criterion (BIC) is utilised for optimal segmentation level selection. Expectation maximisation (EM) based mixture modelling is then performed, using a k-means clustering procedure which varies voxel order for initialisation. A multiscale Markov model is then used to refine this segmentation by modelling spatial correlations between neighbouring image voxels. A field programmable gate array (FPGA) based co-processing solution is also proposed to offload the most complex computations onto hardware, in order to achieve high performance.
positron emission tomography (pet) co-processing multiscale markov modelling field programmable gate arrays (fpgas) medical image segmentation gaussian mixture modelling wavelet tumour quantification