Semiautomatic dental recognition using a graph-based segmentation algorithm and teeth shapes features

Dental recognition is very important for forensic human identification, mainly regarding the mass disasters, which have frequently happened due to tsunamis, airplanes crashes, etc. Algorithms for automatic, precise, and robust teeth segmentation from radiograph images are crucial for dental recognition. In this work we propose the use of a graph-based algorithm to extract the teeth contours from panoramic dental radiographs that are used as dental features. In order to assess our proposal, we have carried out experiments using a database of 1126 tooth images, obtained from 40 panoramic dental radiograph images from 20 individuals. The results of the graph-based algorithm was qualitatively assessed by a human expert who reported excellent scores. For dental recognition we propose the use of the teeth shapes as biometric features, by the means of BAS (Bean Angle Statistics) and Shape Context descriptors. The BAS descriptors showed, on the same database, a better performance (EER 14%) than the Shape Context (EER 20%). © 2012 IEEE.

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.

JSEG-based image segmentation in computer vision for agricultural mobile robot navigation

This project aims to apply image processing techniques in computer vision featuring an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained.

Unsupervised segmentation method for cuboidal cell nuclei in histological prostate images based on minimum cross entropy

This paper presents a novel segmentation method for cuboidal cell nuclei in images of prostate tissue stained with hematoxylin and eosin. The proposed method allows segmenting normal, hyperplastic and cancerous prostate images in three steps: pre-processing, segmentation of cuboidal cell nuclei and post-processing. The pre-processing step consists of applying contrast stretching to the red (R) channel to highlight the contrast of cuboidal cell nuclei. The aim of the second step is to apply global thresholding based on minimum cross entropy to generate a binary image with candidate regions for cuboidal cell nuclei. In the post-processing step, false positives are removed using the connected component method. The proposed segmentation method was applied to an image bank with 105 samples and measures of sensitivity, specificity and accuracy were compared with those provided by other segmentation approaches available in the specialized literature. The results are promising and demonstrate that the proposed method allows the segmentation of cuboidal cell nuclei with a mean accuracy of 97%. © 2013 Elsevier Ltd. All rights reserved.

IFTrace: Video segmentation of deformable objects using the Image Foresting Transform

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

The Bauru Group: A continental Cretaceous unit in Brazil - Concepts based on micropaleontological, oxygen isotope and stratigraphical data

The integration of outcrop and subsurface information, including micropaleontological data, facies and sequence stratigraphic studies, and oxygen isotope analysis, allow us to present a new stratigraphic model for the Cretaceous continental deposits of the Bauru Group, Brazil. Thirty-eight fossil taxa were recovered from these deposits, including 29 species of ostracodes and 9 species of charophytes. Seven of these ostracode species and three subspecies are new and formally described here. The associations of Chara barbosai - Ilyocypris cf. riograndensis, found in the Adamantina Formation, and Amblyochara sp. - Neuquenocypris minor mineira nov. subsp., found in the Marília Formation. Ponte Alta Member, represent two distinct groups that are respectively Turonian-Santonian and Maastrichtian (probably Late Maastrichtian) in age. Therefore, a hiatus, encompassing more than 11 Ma, separates those two formations. From bottom to top, four depositional cycles were recognized in the Bauru Group in western São Paulo: cycles 1 and 2 belong to Caiuá Formation (fluvio-lacustrine and lacustrine deposits in the Presidente Prudente region), cycle 3 to the Santo Anastácio and lower Adamantina Formation (respectively fluvial and lacustrine deposits), and cycle 4 to the upper Adamantina Formation (fluvio-lacustrine facies). An erosional unconformity separates the Caiuá and Santo Anastácio Formations (between cycles 2 and 3). The Marília Formation is a distinct unit from the underlying succession; it does not occur in western São Paulo, but is found in restricted areas of São Paulo, Minas Gerais, Mato Grosso do Sul and Goiás States. During the deposition of the Bauru Group (Aptian? to Maastrichtian) the climate was hot and arid-semiarid. Shallow lakes underwent fluctuations in expansion (wet phases) and contraction (dry phases), as well as variations in salinity. During the deposition of the Adamantina Formation (Turonian-Santonian) there were long, dry periods that caused segmentation of large lakes (due to topographic irregularities in the basaltic substrate) and sometimes exposures of the lake floors; when flooded these lake floors were colonized by extensive meadows of single species of charophytes. Small ephemeral ponds, that were hydrochemically unstable and colonized by multiple species of charophytes, were the depositional sites for the marls and mudstones of Ponte Alta Member (Maastrichtian, Late Maastrichtian?). Our micropaleontological age control, combined with the Late Cretaceous ages of volcanic ashes found in the southeastern Brazil coastal basins, and the stratigraphic position of analcimites from the Jaboticabal-SP region, suggest a Late Coniacian-Santonian age for important magmatic events occurred in the interior of Brazil (north-central São Paulo State, Triângulo Mineiro, and southwestern Goiás State).

RBF circuits based on folded cascode differential pairs

We propose new circuits for the implementation of Radial Basis Functions such as Gaussian and Gaussian-like functions. These RBFs are obtained by the subtraction of two differential pair output currents in a folded cascode configuration. We also propose a multidimensional version based on the unidimensional circuits. SPICE simulation results indicate good functionality. These circuits are intended to be applied in the implementation of radial basis function networks. One possible application of these networks is transducer signal conditioning in aircraft and spacecraft vehicles onboard telemetry systems. Copyright 2008 ACM.

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.

A graph-based framework for thermal faceprint characterization

Thermal faceprint has been paramount in the last years. Since we can handle with face recognition using images acquired in the infrared spectrum, an unique individual's signature can be obtained through the blood vessels network of the face. In this work, we propose a novel framework for thermal faceprint extraction using a collection of graph-based techniques, which were never used to this task up to date. A robust method of thermal face segmentation is also presented. The experiments, which were conducted over the UND Collection C dataset, have showed promising results. © 2011 Springer-Verlag.

Automatic segmentation of latent fingerprints

Latent fingerprints are routinely found at crime scenes due to the inadvertent contact of the criminals' finger tips with various objects. As such, they have been used as crucial evidence for identifying and convicting criminals by law enforcement agencies. However, compared to plain and rolled prints, latent fingerprints usually have poor quality of ridge impressions with small fingerprint area, and contain large overlap between the foreground area (friction ridge pattern) and structured or random noise in the background. Accordingly, latent fingerprint segmentation is a difficult problem. In this paper, we propose a latent fingerprint segmentation algorithm whose goal is to separate the fingerprint region (region of interest) from background. Our algorithm utilizes both ridge orientation and frequency features. The orientation tensor is used to obtain the symmetric patterns of fingerprint ridge orientation, and local Fourier analysis method is used to estimate the local ridge frequency of the latent fingerprint. Candidate fingerprint (foreground) regions are obtained for each feature type; an intersection of regions from orientation and frequency features localizes the true latent fingerprint regions. To verify the viability of the proposed segmentation algorithm, we evaluated the segmentation results in two aspects: a comparison with the ground truth foreground and matching performance based on segmented region. © 2012 IEEE.

Estimation of the metabolic equivalent (MET) of an exercise protocol based on indirect calorimetry

Objective: This study aimed to determine the energy expenditure (EE) in terms of caloric cost and metabolic equivalents (METs) of two sessions of an exercise protocol. Methods: Fifteen subjects (51.0 ± 5.5years) performed the exercise sessions (80min), which were composed by (warming, walking and flexibility exercises; Session A) and (warming, walking and local muscular endurance exercises; Session B). Heart hate (HR) was measured during each part of the sessions. In laboratory environment, maximal oxygen consumption (VO2max) and oxygen uptake in rest and exercise conditions (using mean HR obtained in classes) were measured on different days, using indirect calorimetry. Exercise METs were obtained by dividing VO2 in exercise (mL.kg-1.min-1) by VO2 in rest (mL.kg-1.min-1). The EE of the exercises was calculated by the formula: MET x Weight(kg) x Time(min)/60. The results were analyzed by ANOVA with Tuckey post hoc test (p < 0.05). Results: One MET for this group was 2.7 ± 0.1mL.kg-1.min-1. The mean METs of exercises were 4,7 ± 0,8 (warming), 5,8 ± 0,9 (walking) and 3,6 ± 0,7 (flexibility) on session A, and 4,6 ± 1,2 (warming), 5,6 ± 1,0 (walking) and 4.8 ± 1,0 (local muscular endurance exercises) on Session B. The training sessions showed similar energy cost (A: 398 ± 86.72 kcal and B: 404 ± 38.85 kcal; p > 0,05). None of activities were classified into vigorous intensity (> 7 METs). There were no differences on VO2 between walking (15,6 ± 2,8 or 15,4 ± 2,6 mL.kg-1.min-1) and local muscular endurance exercises (13,2 ± 2,9 mL.kg-1.min-1), although both were higher (p > 0.05) than flexibility exercises (10.1 ± 2.2 mL.kg-1.min-1). Conclusion: The proposed protocol achieves the physical activity needed by healthy adults to improve and maintain health, by their structure, moderate intensity, duration, frequency and caloric expenditure.

ECG arrhythmia classification based on optimum-path forest

An important tool for the heart disease diagnosis is the analysis of electrocardiogram (ECG) signals, since the non-invasive nature and simplicity of the ECG exam. According to the application, ECG data analysis consists of steps such as preprocessing, segmentation, feature extraction and classification aiming to detect cardiac arrhythmias (i.e.; cardiac rhythm abnormalities). Aiming to made a fast and accurate cardiac arrhythmia signal classification process, we apply and analyze a recent and robust supervised graph-based pattern recognition technique, the optimum-path forest (OPF) classifier. To the best of our knowledge, it is the first time that OPF classifier is used to the ECG heartbeat signal classification task. We then compare the performance (in terms of training and testing time, accuracy, specificity, and sensitivity) of the OPF classifier to the ones of other three well-known expert system classifiers, i.e.; support vector machine (SVM), Bayesian and multilayer artificial neural network (MLP), using features extracted from six main approaches considered in literature for ECG arrhythmia analysis. In our experiments, we use the MIT-BIH Arrhythmia Database and the evaluation protocol recommended by The Association for the Advancement of Medical Instrumentation. A discussion on the obtained results shows that OPF classifier presents a robust performance, i.e.; there is no need for parameter setup, as well as a high accuracy at an extremely low computational cost. Moreover, in average, the OPF classifier yielded greater performance than the MLP and SVM classifiers in terms of classification time and accuracy, and to produce quite similar performance to the Bayesian classifier, showing to be a promising technique for ECG signal analysis. © 2012 Elsevier Ltd. All rights reserved.

Wavelets based Algorithm for the Evaluation of Enhanced Liver Areas

Hepatocellular carcinoma (HCC) is a primary tumor of the liver. After local therapies, the tumor evaluation is based on the mRECIST criteria, which involves the measurement of the maximum diameter of the viable lesion. This paper describes a computed methodology to measure through the contrasted area of the lesions the maximum diameter of the tumor by a computational algorithm 63 computed tomography (CT) slices from 23 patients were assessed. Non-contrasted liver and HCC typical nodules were evaluated, and a virtual phantom was developed for this purpose. Optimization of the algorithm detection and quantification was made using the virtual phantom. After that, we compared the algorithm findings of maximum diameter of the target lesions against radiologist measures. Computed results of the maximum diameter are in good agreement with the results obtained by radiologist evaluation, indicating that the algorithm was able to detect properly the tumor limits A comparison of the estimated maximum diameter by radiologist versus the algorithm revealed differences on the order of 0.25 cm for large-sized tumors (diameter > 5 cm), whereas agreement lesser than 1.0cm was found for small-sized tumors. Differences between algorithm and radiologist measures were accurate for small-sized tumors with a trend to a small increase for tumors greater than 5 cm. Therefore, traditional methods for measuring lesion diameter should be complemented with non-subjective measurement methods, which would allow a more correct evaluation of the contrast-enhanced areas of HCC according to the mRECIST criteria.

Comparative analysis of two techniques for detection and segmentation of moving bodies

This paper makes a comparative analysis of results produced by the application of two techniques for the detection and segmentation of bodies in motion captured in images sequence, namely: 1) technique based on the temporal average of the values of each pixel recorded in N consecutive image frames and, 2) technique based on historical values associated with pixels recorded in different frames of an image sequence.