A lung computed tomographic assessment of positive end-expiratory pressure-induced lung overdistension

The aim of this study was to assess positive end-expiratory pressure (PEEP)-induced lung overdistension and alveolar recruitment in six patients with acute lung injury (ALI) using a computed tomographic (CT) scan method. Lung overdistension was first determined in six healthy volunteers in whom CT sections were obtained at FRC and at TLC with a positive airway pressure of 30 cm H2O. In patients, lung volumes were quantified by the analysis of the frequency distribution of CT numbers on the entire lung at zero end-expiratory pressure (ZEEP) and PEEP. In healthy volunteers at FRC, the distribution of the density histograms was monophasic with a peak at -791 ± 12 Hounsfield units (HU). The lowest CT number observed was -912 HU. At TLC, lung volume increased by 79 ± 35% and the peak CT number decreased to -886 ± 26 HU. More than 70% of the increase in lung volume was located below -900 HU, suggesting that this value can be considered as the threshold separating normal aeration from overdistension. In patients with ALI, at ZEEP the distribution of density histograms was either monophasic (n = 3) or biphasic (n = 3). The mean CT number was -319 ± 34 HU. At PEEP 13 ± 3 cm H2O, lung volume increased by 47 ± 19% whereas mean CT number decreased to -538 ± 171 HU. PEEP induced a mean alveolar recruitment of 320 ± 160 ml and a mean lung overdistension of 238 ± 320 ml. In conclusion, overdistended lung parenchyma of healthy volunteers is characterized by a CT number below -900 HU. This threshold can be used in patients with ALI for differentiating PEEP-induced alveolar recruitment from lung overdistension.

Effects of contrast material on computed tomographic measurements of lung volumes in patients with acute lung injury

Background. Intravenous injection of contrast material is routinely performed in order to differentiate nonaerated lung parenchyma from pleural effusion in critically ill patients undergoing thoracic computed tomography (CT). The aim of the present study was to evaluate the effects of contrast material on CT measurement of lung volumes in 14 patients with acute lung injury. Method. A spiral thoracic CT scan, consisting of contiguous axial sections of 10 mm thickness, was performed from the apex to the diaphragm at end-expiration both before and 30 s (group 1; n=7) or 15 min (group 2; n=7) after injection of 80 ml contrast material. Volumes of gas and tissue, and volumic distribution of CT attenuations were measured before and after injection using specially designed software (Lungview®; Institut National des Télécommunications, Evry, France). The maximal artifactual increase in lung tissue resulting from a hypothetical leakage within the lung of the 80 ml contrast material was calculated. Results. Injection of contrast material significantly increased the apparent volume of lung tissue by 83 ± 57 ml in group 1 and 102 ± 80 ml in group 2, whereas the corresponding maximal artifactual increases in lung tissue were 42 ± 52 ml and 31 ± 18 ml. Conclusion. Because systematic injection of contrast material increases the amount of extravascular lung water in patients with acute lung injury, it seems prudent to avoid this procedure in critically ill patients undergoing a thoracic CT scan and to reserve its use for specific indications.

A method for the digital image analysis of ceramic grains based on shape factor segmentation

A very simple and robust method for ceramics grains quantitative image analysis is presented. Based on the use of optimal imaging conditions for reflective light microscopy of bulk samples, a digital image processing routine was developed for shading correction, noise suppressing and contours enhancement. Image analysis was done for grains selected according to their concavities, evaluated by perimeter ratio shape factor, to avoid consider the effects of breakouts and ghost boundaries due to ceramographic preparation limitations. As an example, the method was applied for two ceramics, to compare grain size and morphology distributions. In this case, most of artefacts introduced by ceramographic preparation could be discarded due to the use of perimeter ratio exclusion range.

Road segment delineation using active testing principle and an edge-based post-processing strategy

This paper presents a semi-automated method for extracting road segments from medium-resolution images based on active testing and edge analysis. The method is based on two sequential and independent stages. Firstly, an active testing method is used to extract an approximated road centreline which is based on a sequential and local exploitation of the image. Secondly, an iterative strategy based on edge analysis and the approximated centreline is used to measure precisely the road centreline. Based on the results obtained using medium-resolution test images, the method seems to be very promising. In general, the method proved to be very accurate whenever the roads are characterized by two well-defined anti-parallel edges and robust even in the presence of larger obstacles such as trees and shadows.

A photogrammetric method for single image orientation and measurement

The aim of this paper is to present a photogrammetric method for determining the dimensions of flat surfaces, such as billboards, based on a single digital image. A mathematical model was adapted to generate linear equations for vertical and horizontal lines in the object space. These lines are identified and measured in the image and the rotation matrix is computed using an indirect method. The distance between the camera and the surface is measured using a lasermeter, providing the coordinates of the camera perspective center. Eccentricity of the lasermeter center related to the camera perspective center is modeled by three translations, which are computed using a calibration procedure. Some experiments were performed to test the proposed method and the achieved results are within a relative error of about 1 percent in areas and distances in the object space. This accuracy fulfills the requirements of the intended applications. © 2005 American Society for Photogrammetry and Remote Sensing.

Extração de feições rodoviárias em imagens digitais: Metodologias desenvolvidas pelo GP-F&VC

Several kinds of research in road extraction have been carried out in the last 6 years by the Photogrammetry and Computer Vision Research Group (GPF&VC - Grupo de Pesquisa em Fotogrametria e Visão Computacional). Several semi-automatic road extraction methodologies have been developed, including sequential and optimizatin techniques. The GP-F&VC has also been developing fully automatic methodologies for road extraction. This paper presents an overview of the GP-F&VC research in road extraction from digital images, along with examples of results obtained by the developed methodologies.

Real-time crowd density estimation using images

This paper presents a technique for real-time crowd density estimation based on textures of crowd images. In this technique, the current image from a sequence of input images is classified into a crowd density class. Then, the classification is corrected by a low-pass filter based on the crowd density classification of the last n images of the input sequence. The technique obtained 73.89% of correct classification in a real-time application on a sequence of 9892 crowd images. Distributed processing was used in order to obtain real-time performance. © Springer-Verlag Berlin Heidelberg 2005.

Myxopapillary ependymoma of the posterior mediastinum

A left paravertebral mass discovered incidentally on routine examination in a 39-year-old woman is described. Computerized tomography studies revealed a 7 × 6 cm, well circumscribed, noncalcified soft tissue mass with lobular borders abutting the left inferior pulmonary vein and descending aorta. It was not possible to determine the exact anatomic location of the mass based on the imaging studies as both peripheral lung tumors and posterior mediastinal lesions may exhibit the imaging findings described here. At thoracotomy, the mass was seen to be well circumscribed, focally attached to the pleura but without involvement of lung parenchyma, and situated in the left posterior mediastinum. On histological examination, the lesion showed the classical features of myxopapillary ependymoma. Immunohistochemical studies confirmed this impression by demonstrating strong positivity of the tumor cells for S-100 protein, glial fibrillary acidic protein, and CD99 and negative staining with other differentiation markers. A review of the literature with a discussion of the histologic and radiologic differential diagnosis of these lesions is presented. © 2006 Elsevier Inc. All rights reserved.

Um estudo sobre o osu do modelo de cor RGB no proceso de correspondência por correlação

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Endovascular treatment of pseudoaneurysm of the thoracic aorta from a firearm injury

A 24-year-old male patient was the victim of a firearm wound that penetrated the thorax. He arrived at another hospital hemodynamically unstable and was submitted to exploratory surgery by means of bithoracotomy. A lesion of the left branch of the pulmonary artery was detected and successfully repaired. He was submitted for computer-aided tomography on the fifth postoperative day, and a lesion of the mid-thoracic aorta was detected, which formed a saccular image. Considering that the patient had already been submitted to a bithoracotomy and that a direct approach to repair would involve another thoracotomy within a short period of time, endovascular treatment was chosen in our hospital. The procedure was performed under fluoroscopy. A second computer-aided tomography indicated adequate treatment of the lesion, with no indication of an endoleak. He has undergone ambulatory follow-up for 36 months without any problem related to the procedure. While endovascular treatment of the aorta has developed enormously, multicenter studies are needed to better define the long-term results of this approach. © 2008 Published by European Association for Cardio-Thoracic Surgery. All rights reserved.

A mechatronic system applied to wear particle analysis

This paper describes the development of a mechatronic system for a predictive maintenance grounded on wear particle analysis. The reckoning of wear particles containing in lubricating industrial oils brings the image acquisition system into being. The ISO 4406:1999 standard is a guide to establish the counting and evaluation processes of particles. The system applied to the acquisition and analysis of the data consists of a digital camera, a monocular microscope and an oil filtering system. A computational program was developed with the application of Visual Microsoft C++ in a way to detain the oil sample image from the microscope slide to the computer screen. Quantitative analyses of the wear debris particles bulk are exploited applying a graphical interface that was developed to render the image processing of the sample test. The implemented system has a reachable cost thus it can be applied for schooling goals and for bolstering laboratories of minor industries and medium size companies.

Perfusão cerebral e análise individual automatizada utilizando SPECT em uma população com transtorno obsessivo-compulsivo

Objective: To make individual assessments using automated quantification methodology in order to screen for perfusion abnormalities in cerebral SPECT examinations among a sample of subjects with OCD. Methods: Statistical parametric mapping (SPM) was used to compare 26 brain SPECT images from patients with OCD individually with an image bank of 32 normal subjects, using the statistical threshold of p < 0.05 (corrected for multiple comparisons at the level of individual voxels or clusters). The maps were analyzed, and regions presenting voxels that remained above this threshold were sought. results: Six patients from a sample of 26 OCD images showed abnormalities at cluster or voxel level, considering the criteria described above, which represented 23.07%. However, seven images from the normal group of 32 were also indicated as cases of perfusional abnormality, representing 21.8% of the sample. Conclusion: The automated quantification method was not considered to be a useful tool for clinical practice, for analyses complementary to visual inspection.

Precipitates segmentation from scanning electron microscope images through machine learning techniques

The presence of precipitates in metallic materials affects its durability, resistance and mechanical properties. Hence, its automatic identification by image processing and machine learning techniques may lead to reliable and efficient assessments on the materials. In this paper, we introduce four widely used supervised pattern recognition techniques to accomplish metallic precipitates segmentation in scanning electron microscope images from dissimilar welding on a Hastelloy C-276 alloy: Support Vector Machines, Optimum-Path Forest, Self Organizing Maps and a Bayesian classifier. Experimental results demonstrated that all classifiers achieved similar recognition rates with good results validated by an expert in metallographic image analysis. © 2011 Springer-Verlag Berlin Heidelberg.

Image categorization through optimum path forest and visual words

Different from the first attempts to solve the image categorization problem (often based on global features), recently, several researchers have been tackling this research branch through a new vantage point - using features around locally invariant interest points and visual dictionaries. Although several advances have been done in the visual dictionaries literature in the past few years, a problem we still need to cope with is calculation of the number of representative words in the dictionary. Therefore, in this paper we introduce a new solution for automatically finding the number of visual words in an N-Way image categorization problem by means of supervised pattern classification based on optimum-path forest. © 2011 IEEE.

BBA: A binary bat algorithm for feature selection

Feature selection aims to find the most important information from a given set of features. As this task can be seen as an optimization problem, the combinatorial growth of the possible solutions may be in-viable for a exhaustive search. In this paper we propose a new nature-inspired feature selection technique based on the bats behaviour, which has never been applied to this context so far. The wrapper approach combines the power of exploration of the bats together with the speed of the Optimum-Path Forest classifier to find the set of features that maximizes the accuracy in a validating set. Experiments conducted in five public datasets have demonstrated that the proposed approach can outperform some well-known swarm-based techniques. © 2012 IEEE.