Edson Cavalcanti Neto

Automatic 3D pulmonary nodule detection in CT images

This work presents a systematic review of techniques for the 3D automatic detection of pulmonary nodules in computerized-tomography (CT) images. Its main goals are to analyze the latest technology being used for the development of computational diagnostic tools to assist in the acquisition, storage and, mainly, processing and analysis of the biomedical data. Also, this work identifies the progress made, so far, evaluates the challenges to be overcome and provides an analysis of future prospects. As far as the authors know, this is the first time that a review is devoted exclusively to automated 3D techniques for the detection of pulmonary nodules from lung CT images, which makes this work of noteworthy value. The research covered the published works in the Web of Science, PubMed, Science Direct and IEEEXplore up to December 2014. Each work found that referred to automated 3D segmentation of the lungs was individually analyzed to identify its objective, methodology and results. Based on the analysis of the selected works, several studies were seen to be useful for the construction of medical diagnostic aid tools. However, there are certain aspects that still require attention such as increasing algorithm sensitivity, reducing the number of false positives, improving and optimizing the algorithm detection of different kinds of nodules with different sizes and shapes and, finally, the ability to integrate with the Electronic Medical Record Systems and Picture Archiving and Communication Systems. Based on this analysis, we can say that further research is needed to develop current techniques and that new algorithms are needed to overcome the identified drawbacks.

Development control parking access using techniques Digital Image Processing and Applied Computational Intelligence

In the past few years, smart systems for parking lot access control are developed to control and register the car data. These systems might use cameras to identify vehicle and plates using Character Recognition. Therefore, this work has the main goal to develop a system to detect and recognize car plates on Brazilian pattern to control vehicle access, in which the registered users has the permission to entry the place. For this, were used techniques of Digital Image Processing to extract characters and techniques of Pattern Recognition to identify the numbers and letters. The found characters are used to assign a grade to a registered plate, allowing the access only when the plate has the probability of 99%. The system was tested with 700 videos, granting the access only when the plate was registered, getting 98,5% hit rate on the tested cases. The movement detection step is linked to the system, making it faster, allowing its application to run real time. Thus, can be concluded that the system shows a high potential of commercial application development.

3D Lung Fissure Segmentation in TC images based in Textures

Among all cancers, lung cancer (LC) is the most common of all malignant tumors. In order to obtain a more effective segmentation of pulmonary fissures, independent to other structures present in the CT scan, this paper proposes the segmentation of 3D fissures using texture measures and Artificial Neural Networks (ANN). The results of this study are based on voxels classified as fissure through the proposed method compared to the gold standard set by an expert. The results were analyzed using similarity coefficient rate of 95.6%, the rate sensitivity of 71.1% and specificity of 95.6% rate. Thus, it is possible to identify the job has a gain due to not using segmentation of other pulmonary structures and does not require the use of pulmonary atlas.

Techniques of Binarization, Thinning and Feature Extraction Applied to a Fingerprint System

A large volume of images of fingerprints are collected and stored to be used in various systems such as in access control and identification records (ID). Systems for automatic fingerprint recognition perform searches and comparisons with a database. Biometric recognition is based on two fundamental premises: the first is that digital printing must have permanent details, and the second is the information unit. From these premises, a system analyzes the fingerprint image to extract the information and then compares the data in the verification mode or identification mode. , Extraction techniques must be used to obtain the fingerprint data. These techniques use binarization, thinning and features extraction algorithms which are computational methods that can be applied to digital image processing used in scientific research and security issues. This paper presents a comparative analysis of four thresholding techniques (Niblack, Bernsen, Fisher, Fuzzy), two thinning techniques (Stentiford and Holt) and a feature extraction (Cross Number) technique to evaluate the best performance of the algorithms in fingerprint images. To develop this project a set of 160 fingerprint images was used in experiments and analysis. The results point out the positive and negative points of the different algorithms. The system was developed in the C/C++ language.

Battery Charger Lead-Acid using IC BQ2031

Not all chargers lead acid batteries available on the market use appropriate methods of battery power, resulting in a considerable reduction its useful life. Therefore, developing a battery charger Lead-Acid using appropriate methods of battery life is not a simple task. Thus, this study is based on the dedicated integrated circuit from Texas Instruments BQ2031, utilizing three different methods of loading, and the microcontroller PIC16F877A properly monitor and for charging a lead acid battery, increasing its useful life. The research results were fabricated and tested control, monitoring and power circuits.

Autonomous Underwater Vehicle to Inspect Hydroelectric Dams

Driven by the rising demand for underwater operations in the fields of dam structure monitoring, ecosystems of reservoir lakes from Hydropower Plants (HPP) and mining and oil, underwater robotics is increasing rapidly. The increase in exploration, prospecting, monitoring and security in lakes, rivers and sea, both in commercial applications such as scientific applications, has led large companies and research centers to invest in the development of underwater vehicles. The purpose of this work is to develop and evaluate the performance of a dedicated expert system for an Autonomous Underwater Vehicle (AUV) to inspect hydroelectric dams, focusing efforts on mechatronic project based on dimensioning structural elements and machinery and elaborating the sensory part, which includes navigation sensors and sensors of environment conditions, as well as its vision system to detect and measure cracks on hydroelectric dams. The integration of sensors in an intelligent platform provides a satisfactory control of the vehicle, allowing the movement of the submarine on the three spatial axes. Because of the satisfactory fast response of the sensors, it is possible to determine the acceleration and inclination besides his attitude in relation to the trajectory instantaneously taken, and geometry and depth of the cracks. This vehicle will be able to monitor the physical integrity of dams, making acquisition and storage of environment parameter such as temperature, dissolved oxygen, pH and conductivity as well as document images of the biota from reservoir lakes HPP, with minimized cost, high availability and low dependence on a skilled workforce to operate it.

Depth Calculation using Computer Vision and Sift

Robotics has been developed in a high level in the past few years, especially in mobile Robotics. Its main contribution is shown in human entertainment and performance in dangerous works. However, those are not just the only applications they perform. There is a wide field to be developed. Distance calculation is something really important for localization and navigation of mobile robots. The classic way to calculate distances are using sonar and laser radar. But, the use of computer vision in stereo, based on two cameras, is widespread in Robotics. Therefore, algorithms, to detect new points, allows the creation of new methods to execute stereo vision. In those algorithms there is one that has been used, the SIFT (scale-invariant feature transform). It may be used with classic algorithms of stereo vision in the Computer Vision 3D system development. This project’s objective is to develop a distance acquisition system using computer vision in stereo with the help of SIFT method. The results are very close with what we were expecting. The next steps to be done are the execution of quantitative tests to determinate the focal distance of the utilized cameras and to calibrate the system.