Computer-aided diagnosis of carotid atherosclerosis based on ultrasound image statistics, laws' texture and neural networks

Quantitative characterisation of carotid atherosclerosis and classification into symptomatic or asymptomatic is crucial in planning optimal treatment of atheromatous plaque. The computer-aided diagnosis (CAD) system described in this paper can analyse ultrasound (US) images of carotid artery and classify them into symptomatic or asymptomatic based on their echogenicity characteristics. The CAD system consists of three modules: a) the feature extraction module, where first-order statistical (FOS) features and Laws' texture energy can be estimated, b) the dimensionality reduction module, where the number of features can be reduced using analysis of variance (ANOVA), and c) the classifier module consisting of a neural network (NN) trained by a novel hybrid method based on genetic algorithms (GAs) along with the back propagation algorithm. The hybrid method is able to select the most robust features, to adjust automatically the NN architecture and to optimise the classification performance. The performance is measured by the accuracy, sensitivity, specificity and the area under the receiver-operating characteristic (ROC) curve. The CAD design and development is based on images from 54 symptomatic and 54 asymptomatic plaques. This study demonstrates the ability of a CAD system based on US image analysis and a hybrid trained NN to identify atheromatous plaques at high risk of stroke.

Prevalence study of Staphylococcus aureus in quarter milk samples of dairy cows in the Canton of Bern, Switzerland

In the present study, the prevalence of S. aureus in mammary gland quarters of dairy cows in Switzerland was estimated and a risk factor analysis was carried out. Dairy cows were selected by one-step-cluster sampling with stratification by herd size. Forty-seven of 50 randomly chosen farms participated in the study, resulting in 603 cows and 2388 quarter samples. Milk samples were collected in all herds on two occasions two weeks apart. In 6% of cows (95% CI: 2.7-9.3%) at least one milk sample was positive for S. aureus and from 2% (0.8-3.2%) of all quarters, S. aureus was cultured at least once. In four quarters a latent S. aureus infection (agent detected and somatic cell count (SCC) <100,000cell/ml) was diagnosed. Multivariable hierarchic logistical regression analysis yielded five significant risk factors for observing S. aureus in a milk sample: high SCC, a S. aureus-positive neighbouring quarter, a palpable induration in the quarter, and a wound, scar tissue or crush injury affecting the teat. The type of housing (P=0.1596) was also a factor that remained in the model. The mentioned risk factors must be considered during the evaluation of herds with S. aureus problems. The occurrence of latent S. aureus infections emphasises that not only quarters with a high SCC but all quarters of all cows must be cultured for control measures to be effective.