Prevalence and risk factors of radiographic vertebral fracture in Brazilian community-dwelling elderly

The prevalence and risk factors of radiographic vertebral fracture were determined among Brazilian community-dwelling elderly. Vertebral fractures were a common condition in this elderly population, and lower hip bone mineral density was a significant risk factor for vertebral fractures in both genders. The aim of the study was to estimate the prevalence of radiographic vertebral fracture and investigate factors associated with this condition in Brazilian community-dwelling elderly. This cross-sectional study included 943 elderly subjects (561 women and 382 men) living in So Paulo, Brazil. Thoracic and lumbar spine radiographs were obtained, and vertebral fractures were evaluated using Genant`s semiquantitative method. Bone mineral density (BMD) was measured by dual X-ray absorptiometry, and bone biochemical markers were also evaluated. Female and male subjects were analyzed independently, and each gender was divided into two groups based on whether vertebral fractures were present. The prevalence of vertebral fracture was 27.5% (95% CI 23.8-31.1) in women and 31.8% in men (95% CI 27.1-36.5) (P = 0.116). Cox regression analyses using variables that were significant in the univariate analysis showed that age (prevalence ratio = 1.03, 95% CI 1.01-1.06; p = 0.019) and total femur BMD (PR = 0.27, 95% CI 0.08-0.98; p = 0.048) were independent factors in predicting vertebral fracture for the female group. In the male group, Cox regression analyses demonstrated that femoral neck BMD (PR = 0.26, 95% CI 0.07-0.98; p = 0.046) was an independent parameter in predicting vertebral fractures. Our results suggest that radiographic vertebral fractures are common in Brazilian community-dwelling elderly and that a low hip BMD was an important risk factor for this condition in both genders. Age was also significantly correlated with the presence of vertebral fractures in women.

Dental anomalies of number in the permanent dentition of patients with bilateral cleft lip: Radiographic study

Objective: To radiographically evaluate the prevalence of dental anomalies in patients with complete and incomplete bilateral cleft lip, comparing the prevalence of anomalies between genders and cleft types. Setting: Hospital for Rehabilitation of Craniofacial Anomalies, University of Sao Paulo (HRAC/USP), Bauru, Sao Paulo, Brazil. Participants: 150 randomly selected panoramic radiographs of individuals with complete or incomplete bilateral cleft lip; patient age 12 to 25 years (mean age 13.8 years). Results: Data were statistically analyzed to compare the possible association between presence of hypodontia and supernumerary teeth, according to gender and cleft type. Among the 150 patients evaluated, 80 (53.3%) were male and 70 (46.6%) were female. Since no statistically significant difference was found in hypodontia between genders, data were grouped for analysis, revealing prevalence of 31.6% for complete clefts and 26.8% for incomplete clefts. Concerning supernumerary teeth, the prevalence for the male patients was 28.2% for complete cleft lip and 29.2% for incomplete cleft lip. For female patients, the prevalence was significantly (p = .006) lower for complete cleft lip (17.5%) than for incomplete cleft lip (46.6%). Conclusions: The present results suggest that the prevalence of hypodontia was higher in patients with complete cleft lip, and the prevalence of supernumerary teeth was higher in patients with incomplete cleft lip, in agreement with previous studies.

MODELING VERY LONG BASELINE INTERFEROMETRIC IMAGES WITH THE CROSS-ENTROPY GLOBAL OPTIMIZATION TECHNIQUE

We present a new technique for obtaining model fittings to very long baseline interferometric images of astrophysical jets. The method minimizes a performance function proportional to the sum of the squared difference between the model and observed images. The model image is constructed by summing N(s) elliptical Gaussian sources characterized by six parameters: two-dimensional peak position, peak intensity, eccentricity, amplitude, and orientation angle of the major axis. We present results for the fitting of two main benchmark jets: the first constructed from three individual Gaussian sources, the second formed by five Gaussian sources. Both jets were analyzed by our cross-entropy technique in finite and infinite signal-to-noise regimes, the background noise chosen to mimic that found in interferometric radio maps. Those images were constructed to simulate most of the conditions encountered in interferometric images of active galactic nuclei. We show that the cross-entropy technique is capable of recovering the parameters of the sources with a similar accuracy to that obtained from the very traditional Astronomical Image Processing System Package task IMFIT when the image is relatively simple (e. g., few components). For more complex interferometric maps, our method displays superior performance in recovering the parameters of the jet components. Our methodology is also able to show quantitatively the number of individual components present in an image. An additional application of the cross-entropy technique to a real image of a BL Lac object is shown and discussed. Our results indicate that our cross-entropy model-fitting technique must be used in situations involving the analysis of complex emission regions having more than three sources, even though it is substantially slower than current model-fitting tasks (at least 10,000 times slower for a single processor, depending on the number of sources to be optimized). As in the case of any model fitting performed in the image plane, caution is required in analyzing images constructed from a poorly sampled (u, v) plane.

Generating Segmented Quality Meshes from Images

Techniques devoted to generating triangular meshes from intensity images either take as input a segmented image or generate a mesh without distinguishing individual structures contained in the image. These facts may cause difficulties in using such techniques in some applications, such as numerical simulations. In this work we reformulate a previously developed technique for mesh generation from intensity images called Imesh. This reformulation makes Imesh more versatile due to an unified framework that allows an easy change of refinement metric, rendering it effective for constructing meshes for applications with varied requirements, such as numerical simulation and image modeling. Furthermore, a deeper study about the point insertion problem and the development of geometrical criterion for segmentation is also reported in this paper. Meshes with theoretical guarantee of quality can also be obtained for each individual image structure as a post-processing step, a characteristic not usually found in other methods. The tests demonstrate the flexibility and the effectiveness of the approach.

Topological triangle characterization with application to object detection from images

A novel mathematical framework inspired on Morse Theory for topological triangle characterization in 2D meshes is introduced that is useful for applications involving the creation of mesh models of objects whose geometry is not known a priori. The framework guarantees a precise control of topological changes introduced as a result of triangle insertion/removal operations and enables the definition of intuitive high-level operators for managing the mesh while keeping its topological integrity. An application is described in the implementation of an innovative approach for the detection of 2D objects from images that integrates the topological control enabled by geometric modeling with traditional image processing techniques. (C) 2008 Published by Elsevier B.V.

An automatic correction method for the heel effect in digitized mammography images

The most significant radiation field nonuniformity is the well-known Heel effect. This nonuniform beam effect has a negative influence on the results of computer-aided diagnosis of mammograms, which is frequently used for early cancer detection. This paper presents a method to correct all pixels in the mammography image according to the excess or lack on radiation to which these have been submitted as a result of the this effect. The current simulation method calculates the intensities at all points of the image plane. In the simulated image, the percentage of radiation received by all the points takes the center of the field as reference. In the digitized mammography, the percentages of the optical density of all the pixels of the analyzed image are also calculated. The Heel effect causes a Gaussian distribution around the anode-cathode axis and a logarithmic distribution parallel to this axis. Those characteristic distributions are used to determine the center of the radiation field as well as the cathode-anode axis, allowing for the automatic determination of the correlation between these two sets of data. The measurements obtained with our proposed method differs on average by 2.49 mm in the direction perpendicular to the anode-cathode axis and 2.02 mm parallel to the anode-cathode axis of commercial equipment. The method eliminates around 94% of the Heel effect in the radiological image and the objects will reflect their x-ray absorption. To evaluate this method, experimental data was taken from known objects, but could also be done with clinical and digital images.

Application of computed tomography images in the evaluation of magnetic nanoparticles biodistribution

In this work we evaluate the effectiveness of computed tomography images as a tool to determine magnetic nanoparticle biodistribution over biological tissues. For this purpose, tomography images for magnetic nanoparticles, composed of Fe(3)O(4), coated with 2,3-dimercaptosuccinic acid (DMSA), were generated at several material concentrations. The comparison of CT numbers, calculated from these images generated at clinical conditions, with typical CT numbers for biological tissues, shows that the detection of nanoparticle in most tissues is only possible for high material concentrations. (C) 2010 Elsevier B.V. All rights reserved.

An entropy-based approach to automatic image segmentation of satellite images

An entropy-based image segmentation approach is introduced and applied to color images obtained from Google Earth. Segmentation refers to the process of partitioning a digital image in order to locate different objects and regions of interest. The application to satellite images paves the way to automated monitoring of ecological catastrophes, urban growth, agricultural activity, maritime pollution, climate changing and general surveillance. Regions representing aquatic, rural and urban areas are identified and the accuracy of the proposed segmentation methodology is evaluated. The comparison with gray level images revealed that the color information is fundamental to obtain an accurate segmentation. (C) 2010 Elsevier B.V. All rights reserved.

Generating segmented meshes from textured color images

This paper presents a new framework for generating triangular meshes from textured color images. The proposed framework combines a texture classification technique, called W-operator, with Imesh, a method originally conceived to generate simplicial meshes from gray scale images. An extension of W-operators to handle textured color images is proposed, which employs a combination of RGB and HSV channels and Sequential Floating Forward Search guided by mean conditional entropy criterion to extract features from the training data. The W-operator is built into the local error estimation used by Imesh to choose the mesh vertices. Furthermore, the W-operator also enables to assign a label to the triangles during the mesh construction, thus allowing to obtain a segmented mesh at the end of the process. The presented results show that the combination of W-operators with Imesh gives rise to a texture classification-based triangle mesh generation framework that outperforms pixel based methods. Crown Copyright (C) 2009 Published by Elsevier Inc. All rights reserved.

Automatic contour extraction from 2D neuron images

This work describes a novel methodology for automatic contour extraction from 2D images of 3D neurons (e.g. camera lucida images and other types of 2D microscopy). Most contour-based shape analysis methods cannot be used to characterize such cells because of overlaps between neuronal processes. The proposed framework is specifically aimed at the problem of contour following even in presence of multiple overlaps. First, the input image is preprocessed in order to obtain an 8-connected skeleton with one-pixel-wide branches, as well as a set of critical regions (i.e., bifurcations and crossings). Next, for each subtree, the tracking stage iteratively labels all valid pixel of branches, tip to a critical region, where it determines the suitable direction to proceed. Finally, the labeled skeleton segments are followed in order to yield the parametric contour of the neuronal shape under analysis. The reported system was successfully tested with respect to several images and the results from a set of three neuron images are presented here, each pertaining to a different class, i.e. alpha, delta and epsilon ganglion cells, containing a total of 34 crossings. The algorithms successfully got across all these overlaps. The method has also been found to exhibit robustness even for images with close parallel segments. The proposed method is robust and may be implemented in an efficient manner. The introduction of this approach should pave the way for more systematic application of contour-based shape analysis methods in neuronal morphology. (C) 2008 Elsevier B.V. All rights reserved.

Machine vision for the automatic classification of images acquired from Non-destructive tests

This project is based on Artificial Intelligence (A.I) and Digital Image processing (I.P) for automatic condition monitoring of sleepers in the railway track. Rail inspection is a very important task in railway maintenance for traffic safety issues and in preventing dangerous situations. Monitoring railway track infrastructure is an important aspect in which the periodical inspection of rail rolling plane is required.Up to the present days the inspection of the railroad is operated manually by trained personnel. A human operator walks along the railway track searching for sleeper anomalies. This monitoring way is not more acceptable for its slowness and subjectivity. Hence, it is desired to automate such intuitive human skills for the development of more robust and reliable testing methods. Images of wooden sleepers have been used as data for my project. The aim of this project is to present a vision based technique for inspecting railway sleepers (wooden planks under the railway track) by automatic interpretation of Non Destructive Test (NDT) data using A.I. techniques in determining the results of inspection.

Characters Extraction for Traffic Sign Destination boards in video and still images

Traffic Control Signs or destination boards on roadways offer significant information for drivers. Regulation signs tell something like your speed, turns, etc; Warning signs warn drivers of conditions ahead to help them avoid accidents; Destination signs show distances and directions to various locations; Service signs display location of hospitals, gas and rest areas etc. Because the signs are so important and there is always a certain distance from them to drivers, to let the drivers get information clearly and easily even in bad weather or other situations. The idea is to develop software which can collect useful information from a special camera which is mounted in the front of a moving car to extract the important information and finally show it to the drivers. For example, when a frame contains on a destination drive sign board it will be text something like "Linkoping 50",so the software should extract every character of "Linkoping 50", compare them with the already known character data in the database. if there is extracted character match "k" in the database then output the destination name and show to the driver. In this project C++ will be used to write the code for this software.