Metastatic Liver Tumor Detection from 3-D CT Images using a Level Set Algorithm with Liver-edge Term

We developed a metastatic liver tumor detection method using a level set algorithm with a liver-edge term. The level set algorithm is suitable for detection that requires an automated and accurate technique to reduce the time it takes to interpret the results. The conventional detection method, which is based on shape analysis using the Hessian matrix, tends to miss tumors on the edge of liver parenchyma because such tumors have a different shape than those in the center: on the edge they are blob-like and in the center they are step-like. The proposed method, which we call the liver-edge term, improves the accuracy of detection on the edge of liver parenchyma by recognizing step-like shapes on an intensity distribution. We applied the method to five 3-D CT images and evaluated the accuracy. Results showed that the proposed method had an average sensitivity of 92% compared to the 88% of the conventional method.