1. Experimental Results 20 liver ultrasound images from 10 patients (5 with fatty livers and 5 with normal livers). Radiologists made the classification and complementary laboratorial analysis indicators were obtained. 1.Establish the optimal AR order for classification purposes and then use the AR coefficients has features. The first order AR model is therefore adopted. 2.Distribution of liver classes (normal and fatty) according to the selected features (AR coefficients from the 1 st order AR model): 2.Overlapping the original US image and the map of colors corresponding to the Bayes factor categories: (a) and (c) Original US images from steatotic and healthy livers respectively; (b) and (d) Bayes factor map images of (a) and (c) respectively. RECPAD - 14ª Conferência Portuguesa de Reconhecimento de Padrões, Aveiro, 23 de Outubro de 2009 1,2 Ricardo Ribeiro (ricardo.ribeiro@estesl.ipl.pt) and 1,3 João Sanches (jmrs@isr.ist.utl.pt) 1 Institute for Systems and Roboticsricardo.ribeiro@estesl.ipl.ptjmrs@isr.ist.utl.pt 2 Escola Superior de Tecnologia da Saúde de Lisboa 3 Instituto Superior Técnico Lisbon, Portugal Automatic Diagnosis of Liver Steatosis by Ultrasound using Autoregressive Tissue Characterization Introduction Disease processes in organs have been shown to be accompanied by changes in ultrasonic scattering properties Steatosis is a diffuse liver disease that leads to the increasing of hepatocytes size which have characteristic diffraction patterns, reflected in the speckle field. Nevertheless, US based diagnosis is highly subjective The objective of this work is to design a classifier for automatic diagnosis of liver steatosis from US images where robust autoregressive parameter estimation is used. Problem Formulation 1.The estimation of the RF envelope(a), despeckled anatomic images (b) and speckle field (c): a), b) are performed using the Bayesian methods proposed in [6] and [7], respectively. b)The speckle field generation model may be formulated as follows where ƒ is a pixel intensity of the despeckled image, y is the pixel intensity of the RF image and ƞ is the corresponding speckle intensity. 2. Features were extracted from the first order autoregressive model (AR) coefficients of the texture image, where x(m,n) denotes the value of the speckle field at the location (m,n). The coefficients a ij must be estimated. a 00 is assumed to be one. 3. A Bayes classifier based on these features, was trained with data classified in: Normal, ω N, and Fatty, ω N. It’s assumed that the vector of features are multivariate normal distributed. 4.The Bayes factor (B) was used to measure the confidence level in the classification. To access the severity of the steatosis, a map image was computed. In this image, S={s i,j }, each pixel, s i,j, is where is the Bayes factor Conclusions In this work a textural analysis of the liver parenchyma is proposed to help in the diagnosis of the steatosis. The vector of the estimated coefficients was used to discriminate healthy and pathologic regions. The Bayes factor was mapped in the original image providing useful information about the confidence of the classification. Results obtained from real data have shown the ability of the method to detect the disease. AR ModelError ProbAccuracy Prob. 1 st order0%100% 2 nd order0%100% 3 rd order25%75% 4 th order50% 5 th order40%60% 6 th order20%80%