作者: Brent Foster , Ulas Bagci , Awais Mansoor , Ziyue Xu , Daniel J. Mollura
DOI: 10.1016/J.COMPBIOMED.2014.04.014
关键词: Segmentation 、 Magnetic resonance imaging 、 Artificial intelligence 、 Abnormal cell 、 Computer vision 、 Thresholding 、 Radiology 、 Positron emission tomography 、 Image segmentation 、 Functional imaging 、 PET-CT 、 Medicine
摘要: Positron Emission Tomography (PET), a non-invasive functional imaging method at the molecular level, images distribution of biologically targeted radiotracers with high sensitivity. PET provides detailed quantitative information about many diseases and is often used to evaluate inflammation, infection, cancer by detecting emitted photons from radiotracer localized abnormal cells. In order differentiate tissue surrounding areas in images, image segmentation methods play vital role; therefore, accurate necessary for proper disease detection, diagnosis, treatment planning, follow-ups. this review paper, we present state-of-the-art methods, as well recent advances techniques. make manuscript self-contained, also briefly explain fundamentals imaging, challenges diagnostic analysis, effects these on results.