Abstract  Cirrhosis is an endemic diseases across the world that leads to observed liver contour irregularities in the Ultrasound images, which can be used to detect and confirm the pathologic condition.  In this work these irregularities are semi-automatically segmented and quantified in order to help the physician in the diagnosis. Results obtained from real data have shown the ability of the method to detect the disease.  The ultimate goal is to use these irregularity features jointly with other features extracted from the liver parenchyma to design a highly discriminative classifier for liver cirrhosis, steatosis and other diffuse liver diseases. Morphologic cirrhosis diagnosis from Ultrasound RecPad th edition of the Portuguese Conference on Pattern Recognition, UTAD University, Vila Real city, October 29th Ricardo Ribeiro 1,3 Rui Marinho 2 and J.M. Sanches 1 1 Institute for Systems and Robotics / Instituto Superior Técnico 2 Faculdade de Medicina da Universidade de Lisboa 3 Escola Superior de Tecnologia da Saúde de Lisboa Lisboa, Portugal Problem Formulation I.Estimation of the RF envelope and denoised anatomic images This is performed using the Bayesian methods proposed by [8], where the use of total variation techniques allows the preservation of major transitions, as seen in the case of liver capsule and overlying structures. II.Using the de-noised US image, the liver surface contour is obtain using a snake technique which computes one iteration of the energy-minimization of active contour models. III.Based on the detect contour, the following features were extracted: a)Root Mean Square of the different angles produced by the points that characterize the contour (rms α ). b)Root Mean Square of the variation of the points of the contour in the y axis (rms y ). c)Mean and Variance of the calculated angles (m α and v α ). d)Variance of the y axis coordinates at each point (v y ). IV.Feature selection - forward selection method with the criterion 1 - Nearest Neighbor leave-one-out classification performance. V.The features extracted by the preceding methods are used for classification, where a support vector machine (SVM) classifier [3] is used. Experimental Results  Sixty-nine US liver images were obtained from 69 patients:  40 patients had cirrhosis (Ω C )  29 had normal liver (Ω N )  Image Processing Results:  Classification Results: (a) Normal Liver (b) Cirrhotic liver without ascites (c) Cirrhotic liver with ascites Gold Standard ΩNΩN ΩCΩC Classifier ΩNΩN 17(24.6%)7(10.1%) ΩCΩC 12(17.4%)33(47.9%) Conclusions  In this work a semi-automatic detection of liver surface is proposed to help in the diagnosis of the cirrhosis.  Results obtained showed an overall accuracy of 72,46%, a high sensitivity, 82,5%, and a low specificity, 58,6%.  In the future the authors intend to include other features to increase the accuracy of the method, as well as use more state-of-the-art automatic snakes, in order to create a fully automatic method.