91 resultados para Análise de imagens digitais

em Universidade Federal do Rio Grande do Norte(UFRN)


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This work proposes the development of a Computer System for Analysis of Mammograms SCAM, that aids the doctor specialist in the identification and analysis of existent lesions in digital mammograms. The computer system for digital mammograms processing will make use of a group of techniques of Digital Image Processing (DIP), with the purpose of aiding the medical professional to extract the information contained in the mammogram. This system possesses an interface of easy use for the user, allowing, starting from the supplied mammogram, a group of processing operations, such as, the enrich of the images through filtering techniques, the segmentation of areas of the mammogram, the calculation the area of the lesions, thresholding the lesion, and other important tools for the medical professional's diagnosis. The Wavelet Transform will used and integrated into the computer system, with the objective of allowing a multiresolution analysis, thus supplying a method for identifying and analyzing microcalcifications

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Several tests that evaluate the quality of seeds are destructive and require time, which is considered long and expensive in the processes that involves the production and marketing of seed. Thus, techniques that allow reducing the time related to assess the quality of seed lots is very favorable, considering the technical, economic and scientific point of view. The techniques images of seed analyzed both by X-ray such as digital images, represent alternative for this sector, and are considered reproducible and fast, giving greater flexibility and autonomy to the activities of production systems. Summarily, the objective was to analyze the internal morphology of seeds of this species through x-rayed images and the efficiency of weed seed area increased during soaking through image analysis and compare them with the results of germination tests and force the evaluation of physiological seed quality. For X-ray tests, the seeds were exposed for 0.14 seconds at radiation 40kV and 2.0 mAs. Were analyzed images using the ImageJ program and subsequently put to germinate in B.O.D chamber at 27 ° C, in which there was the comparison of results for germination. To determine the test area increase (% IA), seeds were used with and without seed coat, maintained the B.O.D chamber at 15 ° to 20 ° C, the seeds were photographed before and after the soaking period, the results were compared to the germination rates. For the X-ray test, it was observed that seeds with empty area greater than 20%, showed a higher percentage of abnormal seedlings. And the area increment analysis showed that it is possible to rank the batch after 8 hours of imbibition at 15 ° C according to the germination and vigor tests

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This work aims to develop a methodology for analysis of images using overlapping, which assists in identification of microstructural features in areas of titanium, which may be associated with its biological response. That way, surfaces of titanium heat treated for 08 (eight) different ways have been subjected to a test culture of cells. It was a relationship between the grain, texture and shape of grains of surface of titanium (attacked) trying to relate to the process of proliferation and adhesion. We used an open source software for cell counting adhered to the surface of titanium. The juxtaposition of images before and after cell culture was obtained with the aid of micro-hardness of impressions made on the surface of samples. From this image where there is overlap, it is possible to study a possible relationship between cell growth with microstructural characteristics of the surface of titanium. This methodology was efficient to describe a set of procedures that are useful in the analysis of surfaces of titanium subjected to a culture of cells

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In this work, we propose a multi agent system for digital image steganalysis, based on the poliginic bees model. Such approach aims to solve the problem of automatic steganalysis for digital media, with a case study on digital images. The system architecture was designed not only to detect if a file is suspicious of covering a hidden message, as well to extract the hidden message or information regarding it. Several experiments were performed whose results confirm a substantial enhancement (from 67% to 82% success rate) by using the multi-agent approach, fact not observed in traditional systems. An ongoing application using the technique is the detection of anomalies in digital data produced by sensors that capture brain emissions in little animals. The detection of such anomalies can be used to prove theories and evidences of imagery completion during sleep provided by the brain in visual cortex areas

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Synthesis of heterocyclic compounds, as quinoxaline derivatives, has being shown to be relevant and promissor due to expressive applications in biological and technological areas. This work was dedicated to the synthesis, characterization and reactivity of quinoxaline derivatives in order to obtain new chemosensors. (L)-Ascorbic acid (1) and 2,3-dichloro-6,7- dinitroquinoxalina (2) were explored as synthetic precursors. Starting from synthesis of 1 and characterization of compounds derived from (L)-ascorbic acid, studies were performed investigating the application of products as chemosensors, in which compound 36 demonstrated selective affinity for Cu2+ íons in methanolic solution, by naked-eye (colorimetric) and UVvisible analyses. Further, initial analysis suggests that 39 a Schiff’s base derived from 36 also presents this feature. Five quinoxaline derivatives were synthesized from building block 2 through nucleophilic aromatic substitution by aliphatic amines, in which controlling the experimental conditions allows to obtain both mono- and di-substituted derivatives. Reactivity studies were carried out with two purposes: i) investigate the possibility of 47 compound being a chemosensor for anion, based on its interaction with sodium hydroxide in DMSO, using image analysis and UV-visible spectroscopy; ii) characterize kinetically the conversion of compound 44 into 46 based on RGB and multivariate image analysis from TLC data, as a simple and inexpensive qualitative and quantitative tool.

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Several are the areas in which digital images are used in solving day-to-day problems. In medicine the use of computer systems have improved the diagnosis and medical interpretations. In dentistry it’s not different, increasingly procedures assisted by computers have support dentists in their tasks. Set in this context, an area of dentistry known as public oral health is responsible for diagnosis and oral health treatment of a population. To this end, oral visual inspections are held in order to obtain oral health status information of a given population. From this collection of information, also known as epidemiological survey, the dentist can plan and evaluate taken actions for the different problems identified. This procedure has limiting factors, such as a limited number of qualified professionals to perform these tasks, different diagnoses interpretations among other factors. Given this context came the ideia of using intelligent systems techniques in supporting carrying out these tasks. Thus, it was proposed in this paper the development of an intelligent system able to segment, count and classify teeth from occlusal intraoral digital photographic images. The proposed system makes combined use of machine learning techniques and digital image processing. We first carried out a color-based segmentation on regions of interest, teeth and non teeth, in the images through the use of Support Vector Machine. After identifying these regions were used techniques based on morphological operators such as erosion and transformed watershed for counting and detecting the boundaries of the teeth, respectively. With the border detection of teeth was possible to calculate the Fourier descriptors for their shape and the position descriptors. Then the teeth were classified according to their types through the use of the SVM from the method one-against-all used in multiclass problem. The multiclass classification problem has been approached in two different ways. In the first approach we have considered three class types: molar, premolar and non teeth, while the second approach were considered five class types: molar, premolar, canine, incisor and non teeth. The system presented a satisfactory performance in the segmenting, counting and classification of teeth present in the images.

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Several are the areas in which digital images are used in solving day-to-day problems. In medicine the use of computer systems have improved the diagnosis and medical interpretations. In dentistry it’s not different, increasingly procedures assisted by computers have support dentists in their tasks. Set in this context, an area of dentistry known as public oral health is responsible for diagnosis and oral health treatment of a population. To this end, oral visual inspections are held in order to obtain oral health status information of a given population. From this collection of information, also known as epidemiological survey, the dentist can plan and evaluate taken actions for the different problems identified. This procedure has limiting factors, such as a limited number of qualified professionals to perform these tasks, different diagnoses interpretations among other factors. Given this context came the ideia of using intelligent systems techniques in supporting carrying out these tasks. Thus, it was proposed in this paper the development of an intelligent system able to segment, count and classify teeth from occlusal intraoral digital photographic images. The proposed system makes combined use of machine learning techniques and digital image processing. We first carried out a color-based segmentation on regions of interest, teeth and non teeth, in the images through the use of Support Vector Machine. After identifying these regions were used techniques based on morphological operators such as erosion and transformed watershed for counting and detecting the boundaries of the teeth, respectively. With the border detection of teeth was possible to calculate the Fourier descriptors for their shape and the position descriptors. Then the teeth were classified according to their types through the use of the SVM from the method one-against-all used in multiclass problem. The multiclass classification problem has been approached in two different ways. In the first approach we have considered three class types: molar, premolar and non teeth, while the second approach were considered five class types: molar, premolar, canine, incisor and non teeth. The system presented a satisfactory performance in the segmenting, counting and classification of teeth present in the images.

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This work aims to develop a methodology for analysis of images using overlapping, which assists in identification of microstructural features in areas of titanium, which may be associated with its biological response. That way, surfaces of titanium heat treated for 08 (eight) different ways have been subjected to a test culture of cells. It was a relationship between the grain, texture and shape of grains of surface of titanium (attacked) trying to relate to the process of proliferation and adhesion. We used an open source software for cell counting adhered to the surface of titanium. The juxtaposition of images before and after cell culture was obtained with the aid of micro-hardness of impressions made on the surface of samples. From this image where there is overlap, it is possible to study a possible relationship between cell growth with microstructural characteristics of the surface of titanium. This methodology was efficient to describe a set of procedures that are useful in the analysis of surfaces of titanium subjected to a culture of cells

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This work embraces the application of Landsat 5-TM digital images, comprising August 2 1989 and September 22 1998, for temporal mapping and geoenvironmental analysis of the dynamic of Piranhas-Açu river mouth, situated in the Macau (RN) region. After treatment using several digital processing techniques (e.g. colour composition in RGB, ratio of bands, principal component analysis, index methods, among others), it was possible to generate several image products and multitemporal maps of the coastal morphodynamics of the studied area. Using the image products it was possible the identification and characterization of the principal elements of interest (vegetation, soil, geology and water) in the surface of the studied area, associating the spectral characteristics of these elements to that presented by the image products resulting of the digital processing. Thus, it was possible to define different types of soils: Amd, AQd6, SK1 and LVe4; vegetation grouping: open arboreal-shrubby caatinga, closed arborealshrubby caatinga, closed arboreal caatinga, mangrove vegetation, dune vegetation and areas predominately constituted by juremas; geological units: quaternary units beach sediments, sand banks, dune flats, barrier island, mobile dunes, fixed dunes, alluvium, tidal and inundation flats, and sandy facies of the Potengi Formation; tertiary-quaternary units Barreiras Formation grouped to the clayey facies of the Potengi Formation, Macau Formation grouped to the sediments of the Tibau Formation; Cretaceous units Jandaíra Formation; moreover it was to identify the sea/land limit, shallow submersed areas and suspended sediments. The multitemporal maps of the coastal morphodynamics allowed the identification and a semi-quantitative evoluation of regions which were submitted to erosive and constructive processes in the last decade. This semi-quantitative evoluation in association with an geoenvironmental characterization of the studied area are important data to the elaboration of actions that may minimize the possible/probable impacts caused by the implantation of the Polo Gas/Sal and to the monitoring of areas explorated by the petroleum and salt industries

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This work proposes the development of an intelligent system for analysis of digital mammograms, capable to detect and to classify masses and microcalcifications. The digital mammograms will be pre-processed through techniques of digital processing of images with the purpose of adapting the image to the detection system and automatic classification of the existent calcifications in the suckles. The model adopted for the detection and classification of the mammograms uses the neural network of Kohonen by the algorithm Self Organization Map - SOM. The algorithm of Vector quantization, Kmeans it is also used with the same purpose of the SOM. An analysis of the performance of the two algorithms in the automatic classification of digital mammograms is developed. The developed system will aid the radiologist in the diagnosis and accompaniment of the development of abnormalities

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Image segmentation is one of the image processing problems that deserves special attention from the scientific community. This work studies unsupervised methods to clustering and pattern recognition applicable to medical image segmentation. Natural Computing based methods have shown very attractive in such tasks and are studied here as a way to verify it's applicability in medical image segmentation. This work treats to implement the following methods: GKA (Genetic K-means Algorithm), GFCMA (Genetic FCM Algorithm), PSOKA (PSO and K-means based Clustering Algorithm) and PSOFCM (PSO and FCM based Clustering Algorithm). Besides, as a way to evaluate the results given by the algorithms, clustering validity indexes are used as quantitative measure. Visual and qualitative evaluations are realized also, mainly using data given by the BrainWeb brain simulator as ground truth

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There has been an increasing tendency on the use of selective image compression, since several applications make use of digital images and the loss of information in certain regions is not allowed in some cases. However, there are applications in which these images are captured and stored automatically making it impossible to the user to select the regions of interest to be compressed in a lossless manner. A possible solution for this matter would be the automatic selection of these regions, a very difficult problem to solve in general cases. Nevertheless, it is possible to use intelligent techniques to detect these regions in specific cases. This work proposes a selective color image compression method in which regions of interest, previously chosen, are compressed in a lossless manner. This method uses the wavelet transform to decorrelate the pixels of the image, competitive neural network to make a vectorial quantization, mathematical morphology, and Huffman adaptive coding. There are two options for automatic detection in addition to the manual one: a method of texture segmentation, in which the highest frequency texture is selected to be the region of interest, and a new face detection method where the region of the face will be lossless compressed. The results show that both can be successfully used with the compression method, giving the map of the region of interest as an input

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The objective of this work is to identify, to chart and to explain the evolution of the soil occupation and the envirionment vulnerability of the areas of Canto do Amaro and Alto da Pedra, in the city of Mossoró-RN, having as base analyzes it multiweather of images of orbital remote sensors, the accomplishment of extensive integrated works of field to a Geographic Information System (GIS). With the use of inserted techniques of it analyzes space inserted in a (GIS), and related with the interpretation and analyzes of products that comes from the Remote Sensoriamento (RS.), make possible resulted significant to reach the objectives of this works. Having as support for the management of the information, the data set gotten of the most varied sources and stored in digital environment, it comes to constitute the geographic data base of this research. The previous knowledge of the spectral behavior of the natural or artificial targets, and the use of algorithms of Processing of Digital images (DIP), it facilitates the interpretation task sufficiently and searchs of new information on the spectral level. Use as background these data, was generated a varied thematic cartography was: Maps of Geology, Geomorfológicals Units soils, Vegetation and Use and Occupation of the soil. The crossing in environment SIG, of the above-mentioned maps, generated the maps of Natural and Vulnerability envirionmental of the petroliferous fields of I Canto do Amaro and Alto da Pedra-RN, working in an ambient centered in the management of waters and solid residuos, as well as the analysis of the spatial data, making possible then a more complex analysis of the studied area

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The aim of this study is to investigate the eco-environmental vulnerability, its changes, and its causes to develop a management system for application of eco-environmental vulnerability and risk assessment in the Apodi-Mossory estuary, Northeast Brazil. This analysis is focused on the interference of the landscape conditions, and its changes, due to the following factors: the oil and natural gas industry, tropical fruits industry, shrimp farms, marine salt industry, occupation of the sensitive areas; demand for land, vegetation degradation, siltation in rivers, severe flooding, sea level rise (SLR), coastal dynamics, low and flat topography, high ecological value and tourism in the region and the rapid growth of urbanization. Conventional and remote sensing data were analyzed using modeling techniques based on ArcGIS, ER-Mapper, ERDAS Imagine and ENVI software. Digital images were initially processed by Principal Component Analysis and transformation of the maximum fraction of noise, and then all bands were normalized to reduce errors caused by bands of different sizes. They were integrated in a Geographic Information System analysis to detect changes, to generate digital elevation models, geomorphic indices and other variables of the study area. A three band color combination of multispectral bands was used to monitor changes of land and vegetation cover from 1986 to 2009. This task also included the analysis of various secondary data, such as field data, socioeconomic data, environmental data and prospects growth. The main objective of this study was to improve our understanding of eco-environmental vulnerability and risk assessment; it´s causes basically show the intensity, its distribution and human-environment effect on the ecosystem, and identify the high and low sensitive areas and area of inundation due to future SLR, and the loss of land due to coastal erosion in the Apodi-Mossoró estuary in order to establish a strategy for sustainable land use. The developed model includes some basic factors such as geology, geomorphology, soils, land use / land cover, vegetation cover, slope, topography and hydrology. The numerical results indicate that 9.86% of total study area was under very high vulnerability, 29.12% high vulnerability, 52.90% moderate vulnerability and 2.23% were in the category of very low vulnerability. The analysis indicates that 216.1 km² and 362.8 km² area flooded on 1m and 10m in sea levels respectively. The sectors most affected were residential, industrial and recreational areas, agricultural land, and ecosystems of high environmental sensitivity. The results showed that changes in eco-environmental vulnerability have a significant impact on the sustainable development of the RN state, since the indicator is a function of sensitivity, exposure and status in relation to a level of damage. The model were presented as a tool to assist in indexing vulnerability in order to optimize actions and assess the implications of decisions makers and policies regarding the management of coastal and estuarine areas. In this context aspects such as population growth, degradation of vegetation, land use / land cover, amount and type of industrialization, SLR and government policies for environmental protection were considered the main factors that affect the eco-environmental changes over the last three decades in the Apodi-Mossoró estuary.