Computer-aided detection (CAD) of hepatocellular carcinoma on multiphase CT images

Primary malignant liver tumor, including hepatocellular carcinoma (HCC), caused 1.25 million deaths per year worldwide. Multiphase CT images offer clinicians important information about hepatic cancer. The presence of HCC is indicated by high-intensity regions in arterial phase images and low-intensity regions in equilibrium phase images following enhancement with contrast material. We propose an automatic method for detecting HCC based on edge detection and subtraction processing. Within a liver area segmented according to our scheme, black regions are selected by subtracting the equilibrium phase images to the corresponding registrated arterial phase images. From these black regions, the HCC candidates are extracted as the areas without edges by using Sobel and LoG edge detection filters. The false-positive (FP) candidates are eliminated by using six features extracted from the cancer and liver regions. Other FPs are further eliminated by opening processing. Finally, an expansion process is applied to acquire the 3D shape of the HCC. The cases used in this experiment were from the CT images of 44 patients, which included 44 HCCs. We extracted 97.7% (43/44) HCCs successfully by our proposed method, with an average number of 2.1 FPs per case. The result demonstrates that our edge-detection-based method is effective in locating the cancer region by using the information obtained from different phase images.