The Nuclear medicine, as a specialty to obtain medical images is very important, and it has became one of the main procedures utilized in Health Care Centers to analyze the metabolic behavior of the patient. This project was based on medical images obtained by the PET modality (Positron Emission Tomography). Thus, we developed a framework for processing Nuclear Medicine three-dimensional images of the PET modality, which is composed of consecutive steps that start with the generation of standard images (gold standard) by using simulated images of the Left Ventricular Heart, such as phantoms obtained from the NCAT-4D software. Then, Poisson quantum noise was introduced into the whole volume to simulate the characteristic noises in PET images. Subsequently, the pre-processing step was executed by using specific 3D filters, such as the median filter, the weighted Gaussian filter, and the Anscombe/Wiener filter. Then the segmentation process, which is based on the Fuzzy Connectedness theory, was implemented. For that purpose four different 3D approaches were implemented: Generic, LIFO, kTetaFOEMS, and Dynamic Weight algorithm. Finally, an assessment procedure was used as a measurement tool to quantify three parameters (True Positive, False Positive and Maximum Distance) that determined the level of efficiency and precision of our process. It was found that the pair filter - segmenter formed by the Anscombe/Wiener filter together with the Fuzzy segmenter based on Dynamic Weights provided the best results, with VP and FP rates of 98.49 ± 0.27% and 2.19 ± 0.19%, respectively, for the simulation of the Left Ventricular volume. Along with the set of choices made during the processing structure, the project was finished with the analysis of a small number of volumes that belonged to a real PET test, thus the quantification of the volumes was obtained.
Nuclear medicine; Positron Emission Tomography (PET); 3D medical image processing; Segmentation of 3D images; Volume quantification
