A classification method of liver tumors based on temporal change of Hounsfield unit in CT images

We present an automatic diagnosis method of liver cancer by using sequential images with contrast material of dynamic CT. Our method identifies and classifies liver tumors by extracting temporal change of CT values [Hounsfield Unit(HU)] of tumors from four kinds of CT images (i.e. plain CT, early phase, portal phase, late phase of dynamic CT images) in addition to morphological features of tumors. Automatic diagnosis of liver tumors is very difficult, because contrast of liver tumors is very small compared with liver background, shapes of tumors are diverse, and extraction of temporal change of CT values is very difficult due to morphological and contrast complexity of temporal change of tumor segments. Our method extracts temporal change of CT values of objects by mapping segments of same objects in different CT phase based on overlap ratio and position adjustment. We also implemented a graphical user interface for searching such images from an image database that include tumors similar to an image given as a search condition with respect to features of morphorogical and temporal change of contrast.