Comparative analysis of the relationship between anterior and posterior corneal shape analyzed by Scheimpflug photography in normal and keratoconus eyes

Background To analyze and compare the relationship between anterior and posterior corneal shape evaluated by a tomographic system combining the Scheimpflug photography and Placido-disc in keratoconus and normal healthy eyes, as well as to evaluate its potential diagnostic value. Methods Comparative case series including a sample of 161 eyes of 161 subjects with ages ranging from 7 to 66 years and divided into two groups: normal group including 100 healthy eyes of 100 subjects, and keratoconus group including 61 keratoconus eyes of 61 patients. All eyes received a comprehensive ophthalmologic examination including an anterior segment analysis with the Sirius system (CSO). Antero-posterior ratios for corneal curvature (k ratio) and shape factor (p ratio) were calculated. Logistic regression analysis was used to evaluate if some antero–posterior ratios combined with other clinical parameters were predictors of the presence of keratoconus. Results No statistically significant differences between groups were found in the antero–posterior k ratios for 3-, 5- and 7-mm diameter corneal areas (p ≥ 0.09). The antero–posterior p ratio for 4.5- and 8-mm diameter corneal areas was significantly higher in the normal group than in the keratoconus group (p < 0.01). The k ratio for 3, 5, and 7 mm was significantly higher in the keratoconus grade IV subgroup than in the normal group (p < 0.01). Furthermore, significant differences were found in the p ratio between the normal group and the keratoconus grade II subgroup (p ≤ 0.01). Finally, the logistic regression analysis identified as significant independent predictors of the presence of keratoconus (p < 0.01) the 8-mm anterior shape factor, the anterior chamber depth, and the minimal corneal thickness. Conclusions The antero-posterior k and p ratios are parameters with poor prediction ability for keratoconus, in spite of the trend to the presence of more prolate posterior corneal surfaces compared to the anterior in keratoconus eyes.

Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps

Feature selection is an important and active issue in clustering and classification problems. By choosing an adequate feature subset, a dataset dimensionality reduction is allowed, thus contributing to decreasing the classification computational complexity, and to improving the classifier performance by avoiding redundant or irrelevant features. Although feature selection can be formally defined as an optimisation problem with only one objective, that is, the classification accuracy obtained by using the selected feature subset, in recent years, some multi-objective approaches to this problem have been proposed. These either select features that not only improve the classification accuracy, but also the generalisation capability in case of supervised classifiers, or counterbalance the bias toward lower or higher numbers of features that present some methods used to validate the clustering/classification in case of unsupervised classifiers. The main contribution of this paper is a multi-objective approach for feature selection and its application to an unsupervised clustering procedure based on Growing Hierarchical Self-Organising Maps (GHSOMs) that includes a new method for unit labelling and efficient determination of the winning unit. In the network anomaly detection problem here considered, this multi-objective approach makes it possible not only to differentiate between normal and anomalous traffic but also among different anomalies. The efficiency of our proposals has been evaluated by using the well-known DARPA/NSL-KDD datasets that contain extracted features and labelled attacks from around 2 million connections. The selected feature sets computed in our experiments provide detection rates up to 99.8% with normal traffic and up to 99.6% with anomalous traffic, as well as accuracy values up to 99.12%.