Análise morfométrica do colágeno dérmico a partir da segmentação por conglomerados (clusters) de cor

Análise morfométrica do colágeno dérmico pode fornecer subsídio quantitativo para a pesquisa em dermatologia. Os autores demonstram uma técnica de análise de imagem digital que permite a identificação de estruturas microscópicas, a partir da segmentação por conglomerados (clusters), de cor aplicada à estimativa da intensidade e densidade das fibras colágenas da derme.

Effect of polymerization and accelerated aging on iris color stability of ocular prosthesis

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

Evaluation of the color stability of two techniques for reproducing artificial irides after microwave polymerization

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

Genetic divergence among Psidium accessions based on biochemical and agronomic variables

Sessenta e nove acessos de Psidium, coletados em seis estados brasileiros, foram analisados para dois métodos não hierárquicos de agrupamento e por componentes principais (CP), visando orientar programas de melhoramento. Foram analisadas as variáveis ácido ascórbico, β-caroteno, licopeno, fenóis totais, flavonóides totais, atividade antioxidante, acidez titulável, sólidos solúveis, açúcares solúveis totais, teor de umidade, diâmetro lateral e transversal do fruto, peso da polpa e das sementes/fruto, número e produção de frutos/planta. Foram observados agrupamentos específicos para os acessos de araçazeiros no método de Tocher e do k-means e na dispersão tridimensional dos quatro CPs. Os acessos de araçazeiros foram separados dos de goiabeira. Não foi observado nenhum agrupamento específico por estado de coleta, indicando a inexistência de barreiras na propagação dos acessos de goiabeira. As análises sugerem a prospecção de maior número de amostras de germoplasma num menor número de regiões, bem como acessos divergentes com alto teor de compostos nutricionais.

Differentiation of floral color and odor in two fly pollinated species of Metrodorea (Rutaceae) from Brazil

We investigated if differences in morphological characters in two species of Metrodorea (Rutaceae) from Brazilian semideciduous forests correspond to some pollination divergence. M. nigra and M. stipularis are sympatric species, display a similar floral morphology, are protandrous, self-incompatible, their flower periods overlap, and both are pollinated by flies. M. nigra main pollinators are Pseudoptiloleps nigripoda (Muscidae) and Fannia sp. (Fanniidae); M. stipularis major pollinators are Phaenicia eximia (Calliphoridae), Palpada sp. and Ornidia obesa (Syrphidae). The distinct floral odor (disagreeable in M. nigra and sweet in M. stipularis) and color (brownish violet vs. pale yellow) determine the differences on type and number of floral visitors observed. Several species from semideciduous forests initially considered to be pollinated by diverse insects, present flies as main pollinators, stressing the importance of fly pollination in such habitats.

Genetic divergence in Eucalyptus spp. clones by molecular RAPD markers

The genetic divergence in 20 Eucalyptus spp. clones was evaluated by multivariate techniques based on 167 RAPD markers, of which 155 were polymorphic and 12 monomorphic. The measures of genetic distances were obtained by the arithmetic complement of the coefficients of Jaccard and of Sorenso-Nei and Li and evaluated by the hierarchical methods of Single Linkage clustering and Unweighted Pair Group Method with Arithmetic Mean (UPGMA). Independent of the dissimilarity coefficient, the greatest divergence was found between clones 7 and 17 and the smallest between the clones 11 and 14. Clone clustering was little influenced by the applied procedure so that, adopting the same percentage of divergence, the UPGMA identified two groups less for the coefficient of Sorenso-Nei and Li. The clones evidenced considerable genetic divergence, which is partly associated to the origin of the study material. The clusters formed by the UPGMA clustering algorithm associated to the arithmetic complement of Jaccard were most consistent.

Background interference on the color of dental composite materials with different thickness by digital contrast

In this work the color dependence of resin composites with the background color was evaluated. The objective was to measure since what thickness the color of the sample stops being influenced by the color of the background over which the resin is placed and the methodology used in experiment was based in analyzing the contrast of digital images of the sample over a black background. The results shown that since 0.8 mm the images contrast becomes almost constant; it prove that since this thickness the color of resin composite depends on the optical resin properties only. The experiment was repeated under three conditions of luminosity to evaluate the influence of it on the image contrast and the results obtained were identical.

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.

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.

Automatic segmentation of the secondary austenite-phase island precipitates in a superduplex stainless steel weld metal

Duplex and superduplex stainless steels are class of materials of a high importance for engineering purposes, since they have good mechanical properties combination and also are very resistant to corrosion. It is known as well that the chemical composition of such steels is very important to maintain some desired properties. In the past years, some works have reported that γ 2 precipitation improves the toughness of such steels, and its quantification may reveals some important information about steel quality. Thus, we propose in this work the automatic segmentation of γ 2 precipitation using two pattern recognition techniques: Optimum-Path Forest (OPF) and a Bayesian classifier. To the best of our knowledge, this if the first time that machine learning techniques are applied into this area. The experimental results showed that both techniques achieved similar and good recognition rates. © 2012 Taylor & Francis Group.

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.

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.

Automatic visual dictionary generation through Optimum-Path Forest clustering

Image categorization by means of bag of visual words has received increasing attention by the image processing and vision communities in the last years. In these approaches, each image is represented by invariant points of interest which are mapped to a Hilbert Space representing a visual dictionary which aims at comprising the most discriminative features in a set of images. Notwithstanding, the main problem of such approaches is to find a compact and representative dictionary. Finding such representative dictionary automatically with no user intervention is an even more difficult task. In this paper, we propose a method to automatically find such dictionary by employing a recent developed graph-based clustering algorithm called Optimum-Path Forest, which does not make any assumption about the visual dictionary's size and is more efficient and effective than the state-of-the-art techniques used for dictionary generation. © 2012 IEEE.

Improving image classification through descriptor combination

The efficiency in image classification tasks can be improved using combined information provided by several sources, such as shape, color, and texture visual properties. Although many works proposed to combine different feature vectors, we model the descriptor combination as an optimization problem to be addressed by evolutionary-based techniques, which compute distances between samples that maximize their separability in the feature space. The robustness of the proposed technique is assessed by the Optimum-Path Forest classifier. Experiments showed that the proposed methodology can outperform individual information provided by single descriptors in well-known public datasets. © 2012 IEEE.