Comparison of direct digital and conventional imaging with Ekta Speed Plus and INSIGHT films for the detection of approximal caries

Purpose: To evaluate the accuracy of approximal caries detection comparing enhanced and unenhanced Sidexis CCD-based digital image with Ektaspeed Plus and INSIGHT films. Methods: Fifty-two extracted premolars were imaged under identical standardized geometric and exposure conditions. Four observers, using five points confidence scale, rated 104 approximal surfaces for the presence or absence of carious lesions by means of four image modalities: (1) observer enhanced; (2) unenhanced Sidexis displays; (3) E speed films and (4) F speed film. Histologic sections served as validating criterion for the presence and depth of carious lesions. Diagnostic accuracy was measured as the area beneath the ROC curve. Results: Mean ROC (receiver operating characteristic) curve areas for approximal surfaces were 0.865 (E speed), 0.856 (F speed), 0.816 (unenhanced Sidexis) and 0.776 (observer enhanced). There were no significant differences between unenhanced digital Sidexis and films. Observer enhanced Sidexis images exhibited a statistically significant lower diagnostic accuracy than the film images for two of the observers.

An image processing method for morphology characterization and pitting corrosion evaluation

A digital image processing and analysis method has been developed to classify shape and evaluate size and morphology parameters of corrosion pits. This method seems to be effective to analyze surfaces with low or high degree of pitting formation. Theoretical geometry data have been compared against experimental data obtained for titanium and aluminum alloys subjected to different corrosion tests. (C) 2002 Elsevier B.V. B.V. All rights reserved.

Niobium filtration of conventional and high-frequency x-ray generator beams for intraoral radiography. Effects on absorbed doses, image density and contrast, and photon spectra

We have studied the effects of niobium beam filtration on absorbed doses, on image density and contrast, and on photon spectra with conventional and high-frequency dental x-ray generators. Added niobium reduced entry and superficial absorbed doses in periapical radiography by 9% to 40% with film and digital image receptors, decreased the radiation necessary to produce a given image density on E-speed film and reduced image contrast on D- and E-speed films. As shown by increased half-value layers for aluminum, titanium, and copper and by pulse-height analyses of beam spectra, niobium increased average beam energy by 6% to 19%. Despite the benefits of adding niobium on patient dose reduction and on narrowing the beams' energy spectra, the beam can be overhardened. Adding niobium, therefore, strikes the best balance between radiation dose reduction and beam attenuation, with its risks of increased exposure times, motion blur, and diminished image contrast, when it is used at modest thicknesses (30 μm) and at lower kVp (70) settings. © 1995 Mosby-Year Book, Inc.

Dynamic programming approach for road centerline extraction from digital images

This paper presents a dynamic programming approach for semi-automated road extraction from medium-and high-resolution images. This method is a modified version of a pre-existing dynamic programming method for road extraction from low-resolution images. The basic assumption of this pre-existing method is that roads manifest as lines in low-resolution images (pixel footprint> 2 m) and as such can be modeled and extracted as linear features. On the other hand, roads manifest as ribbon features in medium- and high-resolution images (pixel footprint ≤ 2 m) and, as a result, the focus of road extraction becomes the road centerlines. The original method can not accurately extract road centerlines from medium- and high- resolution images. In view of this, we propose a modification of the merit function of the original approach, which is carried out by a constraint function embedding road edge properties. Experimental results demonstrated the modified algorithm's potential in extracting road centerlines from medium- and high-resolution images.

Classificação automática de padrões projetados por um sistema de luz estruturada

One of the main problems in Computer Vision and Close Range Digital Photogrammetry is 3D reconstruction. 3D reconstruction with structured light is one of the existing techniques and which still has several problems, one of them the identification or classification of the projected targets. Approaching this problem is the goal of this paper. An area based method called template matching was used for target classification. This method performs detection of area similarity by correlation, which measures the similarity between the reference and search windows, using a suitable correlation function. In this paper the modified cross covariance function was used, which presented the best results. A strategy was developed for adaptative resampling of the patterns, which solved the problem of deformation of the targets due to object surface inclination. Experiments with simulated and real data were performed in order to assess the efficiency of the proposed methodology for target detection. The results showed that the proposed classification strategy works properly, identifying 98% of targets in plane surfaces and 93% in oblique surfaces.

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.

Radiopacity of restorative materials using digital images

The radiopacity of esthetic restorative materials has been established as an important requirement, improving the radiographic diagnosis. The aim of this study was to evaluate the radiopacity of six restorative materials using a direct digital image system, comparing them to the dental tissues (enamel-dentin), expressed as equivalent thickness of aluminum (millimeters of aluminum). Five specimens of each material were made. Three 2-mm thick longitudinal sections were cut from an intact extracted permanent molar tooth (including enamel and dentin). An aluminum step wedge with 9 steps was used. The samples of different materials were placed on a phosphor plate together with a tooth section, aluminum step wedge and metal code letter, and were exposed using a dental x-ray unit. Five measurements of radiographic density were obtained from each image of each item assessed (restorative material, enamel, dentin, each step of the aluminum step wedge) and the mean of these values was calculated. Radiopacity values were subsequently calculated as equivalents of aluminum thickness. Analysis of variance (ANOVA) indicated significant differences in radiopacity values among the materials (P<0.0001). The radiopacity values of the restorative materials evaluated were, in decreasing order: TPH, F2000, Synergy, Prisma Flow, Degufill, Luxat. Only Luxat had significantly lower radiopacity values than dentin. One material (Degufill) had similar radiopacity values to enamel and four (TPH, F2000, Synergy and Prisma Flow) had significantly higher radiopacity values than enamel. In conclusion, to assess the adequacy of posterior composite restorations it is important that the restorative material to be used has enough radiopacity, in order to be easily distinguished from the tooth structure in the radiographic image. Knowledge on the radiopacity of different materials helps professionals to select the most suitable material, along with other properties such as biocompatibility, adhesion and esthetic.

Implementation of filters for image pre-processing for leaf analyses in plantations

In this work an image pre-processing module has been developed to extract quantitative information from plantation images with various degrees of infestation. Four filters comprise this module: the first one acts on smoothness of the image, the second one removes image background enhancing plants leaves, the third filter removes isolated dots not removed by the previous filter, and the fourth one is used to highlight leaves' edges. At first the filters were tested with MATLAB, for a quick visual feedback of the filters' behavior. Then the filters were implemented in the C programming language. At last, the module as been coded in VHDL for the implementation on a Stratix II family FPGA. Tests were run and the results are shown in this paper. © 2008 Springer-Verlag Berlin Heidelberg.

Edge detection in digital images using fuzzy numbers

The purpose of this paper is to introduce a new approach for edge detection in grey shaded images. The proposed approach is based on the fuzzy number theory. The idea is to deal with the uncertainties concerning the grey shades making up the image and, thus, calculate the appropriateness of the pixels in relation to a homogeneous region around them. The pixels not belonging to the region are then classified as border pixels. The results have shown that the technique is simple, computationally efficient and with good results when compared with both the traditional border detectors and the fuzzy edge detectors. Copyright © 2009, Inderscience Publishers.

Fast automatic microstructural segmentation of ferrous alloy samples using optimum-path forest

In this work we propose a novel automatic cast iron segmentation approach based on the Optimum-Path Forest classifier (OPF). Microscopic images from nodular, gray and malleable cast irons are segmented using OPF, and Support Vector Machines (SVM) with Radial Basis Function and SVM without kernel mapping. Results show accurate and fast segmented images, in which OPF outperformed SVMs. Our work is the first into applying OPF for automatic cast iron segmentation. © 2010 Springer-Verlag.

Automatic segmentation of digital images applied in cardiac medical images

The digital image processing has been applied in several areas, especially where it is necessary use tools for feature extraction and to get patterns of the studied images. In an initial stage, the segmentation is used to separate the image in parts that represents a interest object, that may be used in a specific study. There are several methods that intends to perform such task, but is difficult to find a method that can easily adapt to different type of images, that often are very complex or specific. To resolve this problem, this project aims to presents a adaptable segmentation method, that can be applied to different type of images, providing an better segmentation. The proposed method is based in a model of automatic multilevel thresholding and considers techniques of group histogram quantization, analysis of the histogram slope percentage and calculation of maximum entropy to define the threshold. The technique was applied to segment the cell core and potential rejection of tissue in myocardial images of biopsies from cardiac transplant. The results are significant in comparison with those provided by one of the best known segmentation methods available in the literature. © 2010 IEEE.

Optimizing optimum-path forest classification for huge datasets

Traditional pattern recognition techniques can not handle the classification of large datasets with both efficiency and effectiveness. In this context, the Optimum-Path Forest (OPF) classifier was recently introduced, trying to achieve high recognition rates and low computational cost. Although OPF was much faster than Support Vector Machines for training, it was slightly slower for classification. In this paper, we present the Efficient OPF (EOPF), which is an enhanced and faster version of the traditional OPF, and validate it for the automatic recognition of white matter and gray matter in magnetic resonance images of the human brain. © 2010 IEEE.

Identification of foliar diseases in cotton crop

The pathogens manifestation in plantations are the largest cause of damage in several cultivars, which may cause increase of prices and loss of crop quality. This paper presents a method for automatic classification of cotton diseases through feature extraction of leaf symptoms from digital images. Wavelet transform energy has been used for feature extraction while Support Vector Machine has been used for classification. Five situations have been diagnosed, namely: Healthy crop, Ramularia disease, Bacterial Blight, Ascochyta Blight, and unspecified disease. © 2012 Taylor & Francis Group.

Visualização 3D de uma imagem digital

Pós-graduação em Ciências Cartográficas - FCT

Imagem digital em movimento

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)