Automatic liver tumor detection from CT

This paper proposes an automatic system which can perform the entire diagnostic process from the extraction of the liver to the recognition of a tumor. In particular, the proposed technique uses shape information to identify and recognize a lesion adjacent to the border of the liver, which can otherwise be missed. Because such an area is concave like a bay, morphological operations can be used to find the bay. In addition, since the intensity of a lesion can vary greatly according to the patient and the slice taken, a decision on the threshold for extraction is not easy. Accordingly, the proposed method extracts the lesion by means of a Fuzzy c-Means clustering technique, which can determine the threshold regardless of a changing intensity. Furthermore, in order to decrease any erroneous diagnoses, the proposed system performs a 3-D consistency check based on three-dimensional information that a lesion mass cannot appear in a single slice independently. Based on experimental results, these processes produced a high recognition rate above 91%.