Hepatocellular Carcinoma Recognition in Ultrasound Images Using Textural Descriptors and Classical Machine Learning 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 noninvasive, lowcost, 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, noninvasive 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 Cooccurence 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.

Detection of Early Signs of Diabetic Retinopathy Based on Textural and Morphological Information in Fundus Images. Estimated blind people in the world will exceed 40 million by 2025. To develop novel algorithms based on fundus image descriptors that allow the automatic classification of retinal tissue into healthy and pathological in early stages is necessary. In this paper, we focus on one of the most common pathologies in the current society: diabetic retinopathy. The proposed method avoids the necessity of lesion segmentation or candidate map generation before the classification stage. Local binary patterns and granulometric profiles are locally computed to extract texture and morphological information from retinal images. Different combinations of this information feed classification algorithms to optimally discriminate bright and dark lesions from healthy tissues. Through several experiments, the ability of the proposed system to identify diabetic retinopathy signs is validated using different public databases with a large degree of variability and without image exclusion.