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© Springer International Publishing AG 2016. Early detection and diagnosis of Hepatocellular Carcinoma (HCC) is the most discriminating step in liver cancer management. Image processing is primarily used, where fast and accurate Computed Tomography (CT) liver image segmentation is required for effective clinical studies and treatment plans. The purpose of this research is to develop an automated HCC detection and diagnosis system, able to work with HCC lesions from liver CT images, with maximum sensitivity and minimum specificity. Our proposed system carried out automated segmentation of HCC lesions from 3D liver CT images. First, based on chosen histogram thresholds, we create a mask to predict the segmentation area by exploiting prior knowledge of the location and shape. Next, we obtain a 3D HCC lesion using an appropriate combination of cancer area pixel density calculations, histogram analysis and morphological processing. To demonstrate the feasibility of our approach, we carried out a series of experiments using 31 CT cases, comprised of 18 HCC lesions and 13 non HCC lesions. The acquired CT images (in DICOM format) had 128 channels of 512; 512 pixels, each with pixel space varying between 0.54 and 0.85. Simulation results showed 92.68% accuracy and a false positive incidence of 9.75%. These were also compared and validated against manual segmentation carried out by a radiologist and other widely used image segmentation methods. Fully automated HCC detection can be efficiently used to aid medical professionals in diagnosing HCC. A limitation of this research is that the performance was evaluated on a small dataset, which does not allow us to confirm robustness of this system. For future work, we will collect additional clinical and CT image data to ensure comprehensive evaluation and clinical validation. We also intend to apply this automated HCC detection and diagnosis system to Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI) datasets, as well as adapting it for diagnosing different liver diseases using state-of-the-art feature extraction and selection, and machine learning classification techniques.

Original publication




Conference paper

Publication Date



10023 LNAI


240 - 250