Hepatocellular Carcinoma Recognition in Ultrasound Images Using Textural Descriptors and Classical Machine Learning

Abstract

The classification of the liver tissue based on the analysis of the ultrasound images is an important task in the field of computer aided diagnosis, the ultrasonography providing a non-invasive, low-cost, imaging solution. An early, accurate diagnosis of the malignant tumours, based on those images, would be very useful for both patients and doctors. Within ultrasound images, the malignant tumours can be hardly distinguished by the human eye, the golden standard for diagnosis being the biopsy, an invasive, dangerous method. In our research, we develop computerized, non-invasive methods, based on advanced image analysis and recognition techniques, for performing tumour detection within ultrasound images. This paper proposes a method for hepatocellular carcinoma recognition in ultrasound images, by employing textural features obtained from the Gray Level Co-occurence Matrix (GLCM), combined with Local Binary Patterns (LBP). At the end, a true positive rate of about 72% was obtained for HCC, using ensembles of Adaptive Boosted classifiers.