In this study, we propose the extraction methods of the high SUV regions based on the FDG-PET images and perform feature analysis of the cancers. Since FDG accumulates in a large amount in cancer cells, FDG-PET becomes possible to image areas suspected of cancer. SUV shows the degree of FDG accumulation in the body and it is one of the significant criteria for diagnosis. Therefore, extraction of the high SUV regions is considered to be effective. To extract them, we calculate the curvatures of four dimensional hyper-surface of FDG-PET images. There are three curvatures through this calculation, and they express original structures such as the liner shape and isolation degree. We confirm these features using the phantom data and the anatomical images. Then, we extract high SUV regions based on these curvatures of the FDG-PET images. However, since FDG remain not only in cancer cells but also in the brain, cardiac muscle, bladder, and so on, the high SUV regions cannot be defined as malignant. Therefore, we perform feature analysis on the extracted regions and evaluate these regions quantitatively from the viewpoint of the functional indicators and the morphological indicators. As functional indicators, we evaluate these regions quantitatively from average of SUV, maximum of SUV and variance of SUV. As morphological indicators, we evaluate these regions quantitatively from degree of sphericity and average of third curvature. In this paper, we apply the above methods to six cancer cases and analyze the features unique to cancer.