Modelagem dos efeitos da irradiação luminosa no cérebro de camundongos e rastreamento de neurônios durante experimentos de microscopia de fluorescência

The fluorescent proteins are an essential tool in many fields of biology, since they allow us to watch the development of structures and dynamic processes of cells in living tissue, with the aid of fluorescence microscopy. Optogenectics is another technique that is currently widely used in Neuroscience. In general, this technique allows to activate/deactivate neurons with the radiation of certain wavelengths on the cells that have ion channels sensitive to light, at the same time that can be used with fluorescent proteins. This dissertation has two main objectives. Initially, we study the interaction of light radiation and mice brain tissue to be applied in optogenetic experiments. In this step, we model absorption and scattering effects using mice brain tissue characteristics and Kubelka-Munk theory, for specific wavelengths, as a function of light penetration depth (distance) within the tissue. Furthermore, we model temperature variations using the finite element method to solve Pennes’ bioheat equation, with the aid of COMSOL Multiphysics Modeling Software 4.4, where we simulate protocols of light stimulation tipically used in optogenetics. Subsequently, we develop some computational algorithms to reduce the exposure of neuron cells to the light radiation necessary for the visualization of their emitted fluorescence. At this stage, we describe the image processing techniques developed to be used in fluorescence microscopy to reduce the exposure of the brain samples to continuous light, which is responsible for fluorochrome excitation. The developed techniques are able to track, in real time, a region of interest (ROI) and replace the fluorescence emitted by the cells by a virtual mask, as a result of the overlay of the tracked ROI and the fluorescence information previously stored, preserving cell location, independently of the time exposure to fluorescent light. In summary, this dissertation intends to investigate and describe the effects of light radiation in brain tissue, within the context of Optogenetics, in addition to providing a computational tool to be used in fluorescence microscopy experiments to reduce image bleaching and photodamage due to the intense exposure of fluorescent cells to light radiation.

LCD-OpenPACS: sistema integrado de telerradiologia com auxílio ao diagnóstico de nódulos pulmonares em exames de tomografia computadorizada

Lung cancer is one of the most common types of cancer and has the highest mortality rate. Patient survival is highly correlated with early detection. Computed Tomography technology services the early detection of lung cancer tremendously by offering aminimally invasive medical diagnostic tool. However, the large amount of data per examination makes the interpretation difficult. This leads to omission of nodules by human radiologist. This thesis presents a development of a computer-aided diagnosis system (CADe) tool for the detection of lung nodules in Computed Tomography study. The system, called LCD-OpenPACS (Lung Cancer Detection - OpenPACS) should be integrated into the OpenPACS system and have all the requirements for use in the workflow of health facilities belonging to the SUS (Brazilian health system). The LCD-OpenPACS made use of image processing techniques (Region Growing and Watershed), feature extraction (Histogram of Gradient Oriented), dimensionality reduction (Principal Component Analysis) and classifier (Support Vector Machine). System was tested on 220 cases, totaling 296 pulmonary nodules, with sensitivity of 94.4% and 7.04 false positives per case. The total time for processing was approximately 10 minutes per case. The system has detected pulmonary nodules (solitary, juxtavascular, ground-glass opacity and juxtapleural) between 3 mm and 30 mm.

Sistema inteligente para o processamento de imagens digitais intrabucais oclusais

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.

Sistema inteligente para o processamento de imagens digitais intrabucais oclusais

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.

Plane detection from monocular image sequences

AIRES, Kelson R. T. ; ARAÚJO, Hélder J. ; MEDEIROS, Adelardo A. D. . Plane Detection from Monocular Image Sequences. In: VISUALIZATION, IMAGING AND IMAGE PROCESSING, 2008, Palma de Mallorca, Spain. Proceedings..., Palma de Mallorca: VIIP, 2008

Avaliação ecológico-econômica do manguezal de Macau/RN e a importância da aplicação de práticas preservacionistas pela indústria petrolífera local

This work presents the results of a survey in oil-producing region of the Macau City, northern coast of Rio Grande do Norte. All work was performed under the Project for Monitoring Environmental Change and the Influence of Hydrodynamic forcing on Morphology Beach Grass Fields, Serra Potiguar in Macau, with the support of the Laboratory of Geoprocessing, linked to PRH22 - Training Program in Geology Geophysics and Information Technology Oil and Gas - Department of Geology/CCET/UFRN and the Post-Graduation in Science and Engineering Oil/PPGCEP/UFRN. Within the economic-ecological context, this paper assesses the importance of mangrove ecosystem in the region of Macau and its surroundings as well as in the following investigative exploration of potential areas for projects involving reforestation and / or Environmental Restoration. At first it was confirmed the ecological potential of mangrove forests, with primary functions: (i) protection and stabilization of the shoreline, (ii) nursery of marine life, and (iii) source of organic matter to aquatic ecosystems, (iv) refuge of species, among others. In the second phase, using Landsat imagery and techniques of Digital Image Processing (DIP), I came across about 18,000 acres of land that can be worked on environmental projects, being inserted in the rules signed the Kyoto Protocol to the market carbon. The results also revealed a total area of 14,723.75 hectares of activity of shrimp production and salting that can be harnessed for the social, economic and environmental potential of the region, considering that over 60% of this area, ie, 8,800 acres, may be used in the planting of the genus Avicennia considered by the literature that the species best sequesters atmospheric carbon, reaching a mean value of 59.79 tons / ha of mangrove