Using kinetic parameters analysis of dynamic PET studies for liver image segmentation

For practical clinical researches and applications, image segmentation is capable of extracting desired region information plays an important and essential role on a number of medical image preprocessing, including image visualization, malignant tissue recognition, multi-modalities image registration, and so forth. To classify the tissues by physiological characteristics is not satisfactory in high noise functional medical images. In this study, we incorporated both tissue time-activity curves (TACs) and derived "kinetic parametric curves (KPCs)" information to develop a novel image segmentation method for liver tissues classification in dynamic FDG-PET studies. Validation of proposed method, four common clustering techniques, include K-mean (KM), Fuzzy C-mean (FCM), Isodata (ISO), Markov Random Fields (MRF), and proposed method were compared to evaluate its precision of segmentation performance. As results, 35.6% and 6.7% less mean errors in mean difference for KPCs and TACs are performed, respectively, than other methods. With combined KPCs and TACs based clustering method can provide the ability to diagnose ill liver tissues exactly.