Pedro Pedrosa Rebouças Filho

Novel and powerful 3D adaptive crisp active contour method applied in the segmentation of CT lung images

&NA; The World Health Organization estimates that 300 million people have asthma, 210 million people have Chronic Obstructive Pulmonary Disease (COPD), and, according to WHO, COPD will become the third major cause of death worldwide in 2030. Computational Vision systems are commonly used in pulmonology to address the task of image segmentation, which is essential for accurate medical diagnoses. Segmentation defines the regions of the lungs in CT images of the thorax that must be further analyzed by the system or by a specialist physician. This work proposes a novel and powerful technique named 3D Adaptive Crisp Active Contour Method (3D ACACM) for the segmentation of CT lung images. The method starts with a sphere within the lung to be segmented that is deformed by forces acting on it towards the lung borders. This process is performed iteratively in order to minimize an energy function associated with the 3D deformable model used. In the experimental assessment, the 3D ACACM is compared against three approaches commonly used in this field: the automatic 3D Region Growing, the level‐set algorithm based on coherent propagation and the semi‐automatic segmentation by an expert using the 3D OsiriX toolbox. When applied to 40 CT scans of the chest the 3D ACACM had an average F‐measure of 99.22%, revealing its superiority and competency to segment lungs in CT images. HighlightsThe 3D Adaptive Crisp Active Contour Method (3D ACACM) is proposed for the segmentation of CT lung images.A new 3D adaptive internal energy is defined to optimize the segmentation.The automatic initialization of the 3D ACACM is described.The 3D ACACM is compared against three methods commonly used in this domain.The results confirm the superior effectiveness of the 3D ACACM. Graphical abstract Figure. No caption available.

Brinell and Vickers Hardness Measurement Using Image Processing and Analysis Techniques

Mechanical hardness testing is fundamental in the evaluation of the mechanical properties of metallic materials due to the fact that the hardness values allow one to determine the wear resistance of the material involved, as well as the approximate values of its ductility and flow tension, among a number of other key characteristics. As a result, the main objective of the present work has been the development and analysis of a computational methodology capable of determining the Brinell and Vickers hardness values from hardness indentation images, which are based on image processing and analysis algorithms. In order to validate the methodology that has been developed, comparisons of the results resulting from the consideration of ten indentation image samples obtained through the conventional manual hardness measurement approach and a computational methodology have been carried out. This analysis allows one to conclude that the semi-automatic measurement of Vickers and Brinell hardnesses by the computational approach is easier, faster, and less dependent on the operator’s subjectivity.

A novel mobile robot localization approach based on topological maps using classification with reject option in omnidirectional images

Novel method for localization via classification with reject option using omnidirectional images.Evaluation of feature extraction and machine learning techniques in omnidirectional images.Autonomous system based on mobile robot topological map localization.Validation in real and virtual environment.Novel virtual simulation environment and two data sets (virtual and real data). Mobile robot localization, which allows a robot to identify its position, is one of main challenges in the field of Robotics. In this work, we provide an evaluation of consolidated feature extractions and machine learning techniques from omnidirectional images focusing on topological map and localization tasks. The main contributions of this work are a novel method for localization via classification with reject option using omnidirectional images, as well as two novel omnidirectional image data sets. The localization system was analyzed in both virtual and real environments. Based on the experiments performed, the Minimal Learning Machine with Nearest Neighbors classifier and Local Binary Patterns feature extraction proved to be the best combination for mobile robot localization with accuracy of 96.7% and an Fscore of 96.6%.

Ultrasonic sensor signals and optimum path forest classifier for the microstructural characterization of thermally-aged inconel 625 alloy.

Secondary phases, such as laves and carbides, are formed during the final solidification stages of nickel-based superalloy coatings deposited during the gas tungsten arc welding cold wire process. However, when aged at high temperatures, other phases can precipitate in the microstructure, like the γ” and δ phases. This work presents an evaluation of the powerful optimum path forest (OPF) classifier configured with six distance functions to classify background echo and backscattered ultrasonic signals from samples of the inconel 625 superalloy thermally aged at 650 and 950 °C for 10, 100 and 200 h. The background echo and backscattered ultrasonic signals were acquired using transducers with frequencies of 4 and 5 MHz. The potentiality of ultrasonic sensor signals combined with the OPF to characterize the microstructures of an inconel 625 thermally aged and in the as-welded condition were confirmed by the results. The experimental results revealed that the OPF classifier is sufficiently fast (classification total time of 0.316 ms) and accurate (accuracy of 88.75% and harmonic mean of 89.52) for the application proposed.

Novel Adaptive Balloon Active Contour Method based on internal force for image segmentation - A systematic evaluation on synthetic and real images

Many studies have been conducted on digital image segmentation, seeking to overcome the limitations of different methods for specific applications. Thus, existing techniques are improved and new methods created. This paper proposes a new Active Contour Method (ACM) applied to the segmentation of objects in digital images. The proposed method is called Adaptive Balloon ACM and its main contribution is the new internal Adaptive Balloon energy that minimizes the energy of each point on the curve using the topology of its neighboring points, and thus moves the curve toward the object of interest. The method can be initialized inside or outside the object of interest, and can even segment objects that have complex shapes. There are no limitations as to its startup location. This work evaluates the proposed method in several applications and compares it with other ACMs in the literature. This new method obtained superior results, especially when the objects to be segmented were tubular and had bifurcations. Thus the proposed method can be considered effective in segmenting complex shapes in digital images and gave promising results in various applications.

New Analysis Method Application in Metallographic Images through the Construction of Mosaics Via Speeded Up Robust Features and Scale Invariant Feature Transform

In many applications in metallography and analysis, many regions need to be considered and not only the current region. In cases where there are analyses with multiple images, the specialist should also evaluate neighboring areas. For example, in metallurgy, welding technology is derived from conventional testing and metallographic analysis. In welding, these tests allow us to know the features of the metal, especially in the Heat-Affected Zone (HAZ); the region most likely for natural metallurgical problems to occur in welding. The expanse of the Heat-Affected Zone exceeds the size of the area observed through a microscope and typically requires multiple images to be mounted on a larger picture surface to allow for the study of the entire heat affected zone. This image stitching process is performed manually and is subject to all the inherent flaws of the human being due to results of fatigue and distraction. The analyzing of grain growth is also necessary in the examination of multiple regions, although not necessarily neighboring regions, but this analysis would be a useful tool to aid a specialist. In areas such as microscopic metallography, which study metallurgical products with the aid of a microscope, the assembly of mosaics is done manually, which consumes a lot of time and is also subject to failures due to human limitations. The mosaic technique is used in the construct of environment or scenes with corresponding characteristics between themselves. Through several small images, and with corresponding characteristics between themselves, a new model is generated in a larger size. This article proposes the use of Digital Image Processing for the automatization of the construction of these mosaics in metallographic images. The use of this proposed method is meant to significantly reduce the time required to build the mosaic and reduce the possibility of failures in assembling the final image; therefore increasing efficiency in obtaining results and expediting the decision making process. Two different methods are proposed: One using the transformed Scale Invariant Feature Transform (SIFT), and the second using features extractor Speeded Up Robust Features (SURF). Although slower, the SIFT method is more stable and has a better performance than the SURF method and can be applied to real applications. The best results were obtained using SIFT with Peak Signal-to-Noise Ratio = 61.38, Mean squared error = 0.048 and mean-structural-similarity = 0.999, and processing time of 4.91 seconds for mosaic building. The methodology proposed shows be more promissory in aiding specialists during analysis of metallographic images.

A novel Vickers hardness measurement technique based on Adaptive Balloon Active Contour Method

New methodology to measure Vickers hardness.Application Balloon Adaptive Active Contours Method.Comparison with results obtained from traditional methods and specialists.Proposed method present results better than conventional measures. Among the forms of assessment of materials to use in a particular application there is the measuring systems of Vickers hardness. This measurement is performed manually by experts, being very interpretative and subjective, which usually leads to variability of the Vickers hardness value between observers and even for the same observer. The experience of the skilled in measurement will determine if the material can be used for an application or not. There are some works use traditional methods to perform the measurement of Vickers hardness for Digital Image Processing (DIP). This works main objective has been to propose a new methodology capable of determining the Vickers Hardness testing values from indentation images by using the Adaptive Balloon Active Contour Methods. The results of the hardness measurement using the Adaptive Balloon Active Contour Method (ABACM) were significant compared to other methods, by observing the MSE obtained from the measured Vickers hardness, the value by obtained ABACM method is three times lower than the Region Growing method, and five times lower than Watershed method. In addition, the measurement method was carried out in 1.2 ? 0.3?s. The proposed method stands out for not requiring pre-processing and post-processing steps, because its mathematical formulation is robust to noise in this application. It is worth highlighting que Significantly cam close to the two specialists, Demonstrating que can be used to aid in measuring the Vickers hardness.

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.

Identification and Quantification of Pulmonary Emphysema through Pseudocolors

Chronic Obstructive Pulmonary Disease (COPD) is a world health problem with high morbidity and mortality. High-Resolution Computed Tomography (HRCT), is an excellent tool for early detection of emphysema component of COPD. Despite this fact, HRCT presents limitations inherent to the subjective analysis of the gray scale image that directly compromises the accuracy for both diagnosis and precise determination of the disease extension. The objective of this paper is present a colored mask algorithm (CMA) to identify and quantify the emphysema, enhancing its visualization through pseudocolors. We studied 21 images of 7 patients with COPD and 1 healthy volunteer. The CMA applies colors to the segmented lungs according to pre-defined ranges of Hounsfield units. CMA automatically calculates the relative area occupied by tomographic densities within the pre-defined ranges, allowing precise quantification of diseased and normal parenchyma. Future works are needed in order to validate the incorporation of the CMA in the image assessment of emphysema in COPD patients.

New approach to detect and classify stroke in skull CT images via analysis of brain tissue densities

BACKGROUND AND OBJECTIVE Cerebral vascular accident (CVA), also known as stroke, is an important health problem worldwide and it affects 16 million people worldwide every year. About 30% of those that have a stroke die and 40% remain with serious physical limitations. However, recovery in the damaged region is possible if treatment is performed immediately. In the case of a stroke, Computed Tomography (CT) is the most appropriate technique to confirm the occurrence and to investigate its extent and severity. Stroke is an emergency problem for which early identification and measures are difficult; however, computer-aided diagnoses (CAD) can play an important role in obtaining information imperceptible to the human eye. Thus, this work proposes a new method for extracting features based on radiological density patterns of the brain, called Analysis of Brain Tissue Density (ABTD). METHODS The proposed method is a specific approach applied to CT images to identify and classify the occurrence of stroke diseases. The evaluation of the results of the ABTD extractor proposed in this paper were compared with extractors already established in the literature, such as features from Gray-Level Co-Occurrence Matrix (GLCM), Local binary patterns (LBP), Central Moments (CM), Statistical Moments (SM), Hus Moment (HM) and Zernikes Moments (ZM). Using a database of 420 CT images of the skull, each extractor was applied with the classifiers such as MLP, SVM, kNN, OPF and Bayesian to classify if a CT image represented a healthy brain or one with an ischemic or hemorrhagic stroke. RESULTS ABTD had the shortest extraction time and the highest average accuracy (99.30%) when combined with OPF using the Euclidean distance. Also, the average accuracy values for all classifiers were higher than 95%. CONCLUSIONS The relevance of the results demonstrated that the ABTD method is a useful algorithm to extract features that can potentially be integrated with CAD systems to assist in stroke diagnosis.