This paper presents a road distress detection system involving the phases needed to properly deal with fully automatic road distress assessment. A vehicle equipped with line scan cameras, laser illumination and acquisition HW-SW is used to storage the digital images that will be further processed to identify road cracks. Pre-processing is firstly carried out to both smooth the texture and enhance the linear features. Non-crack features detection is then applied to mask areas of the images with joints, sealed cracks and white painting, that usually generate false positive cracking. A seed-based approach is proposed to deal with road crack detection, combining Multiple Directional Non-Minimum Suppression (MDNMS) with a symmetry check. Seeds are linked by computing the paths with the lowest cost that meet the symmetry restrictions. The whole detection process involves the use of several parameters. A correct setting becomes essential to get optimal results without manual intervention. A fully automatic approach by means of a linear SVM-based classifier ensemble able to distinguish between up to 10 different types of pavement that appear in the Spanish roads is proposed. The optimal feature vector includes different texture-based features. The parameters are then tuned depending on the output provided by the classifier. Regarding non-crack features detection, results show that the introduction of such module reduces the impact of false positives due to non-crack features up to a factor of 2. In addition, the observed performance of the crack detection system is significantly boosted by adapting the parameters to the type of pavement.

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BACKGROUND: Left-ventricular ejection fraction (LVEF), the most frequently used parameter to evaluate left ventricular (LV) systolic function, depends not only on LV contractility, but also on different variables such as pre-load and after-load. Three-dimensional wall motion tracking echocardiography (3D-WMT) is a new technique that provides information regarding different new parameters of LV systolic function. Our aim was to evaluate whether the new 3D-WMT-derived LV systolic function parameters are less dependent on load conditions than LVEF. METHODS: In order to modify the load conditions to study the dependence of the different LV systolic function parameters on them, a group of renal failure patients under chronic hemodialysis treatment was selected. The echocardiographic studies, including the 3D-WMT analysis, were performed immediately before and immediately after the hemodialysis session. RESULTS: Thirty-one consecutive patients were enrolled (mean age 65.5 ± 17.0 years; 74.2% men). There was a statistically significant change in predialysis and postdialysis, pre-load and after-load conditions (E/È ratio and systolic blood pressure) and in the LV end-diastolic volume and LVEF. Nevertheless, the findings did not show any significant change before and after dialysis in the 3D-WMT-derived parameters. CONCLUSIONS: LV 3D-wall motion tracking-derived systolic function parameters are less dependent on load conditions than LVEF. They might measure myocardial contractility in a more direct way than LVEF. Thus, hypothetically, they might be useful to detect early and subtle contractility impairments in a wide number of cardiac patients and they could help to optimize the clinical management of such patients.

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