MÉTODO COMPUTACIONAL PARA MEDIÇÃO AUTOMÁTICA DO DIÂMETRO LIMBAR

The measurement of the limbus diameter in millimeters isuseful for ophthalmologists in various tests, such as thosethat enable the detection of congenital glaucoma. Some examsrequire the patient to interact with the doctor, providinginformation during the exam. Patients who cannotcollaborate, such as children aged 0-3, need to be sedatedto allow the specialist to check the diameter of the limbus.This measurement is not always accurate, because in medicalpractice it is common to use a ruler close to the eye to gaugethe diameter of the limbus. In this context, it is appropriateto develop a computational solution that avoids the useof invasive techniques in patients, also avoiding the need tosedate them for such examinations, as well as improving theaccuracy of the measurement. In this work, a computationalmethod is proposed for the automatic detection of limbus inpatient images and for the calculation of its diameter in millimeters.The results obtained by the developed method arecompatible with the values obtained by the manual measurementmethod. The performance obtained by the developedtechnique indicates that the proposed methodology has potentialfor application in ophthalmic officés.A medida em milı́metros do diâmetro do limbo é útil para os médicos oftalmologistas em diversos exames, como os que possibilitam a detecção do glaucoma congênito. Alguns exames precisam que o paciente interaja com o médico, fornecendo informações durante a realização do exame. Os pacientes que não podem colaborar, como crianças de 0 a 3 anos, precisam ser sedados para permitir que o especialista verifique o diâmetro do limbo. Esta medida nem sempre é precisa, pois na prática médica, é comum a utilização de uma régua próxima ao olho para aferir o diâmetro do limbo. Neste contexto, faz-se oportuno o desenvolvimento de uma solução computacional que evite a utilização de técnicas invasivas nos pacientes, evitando também a necessidade de sedá-los para a realização de tais exames, assim como melhorando a precisão da medida. Neste trabalho, é proposto um método computacional para a detecção automática do limbo em imagens de pacientes e para o cálculo do seu diâmetro em milı́metros. Os resultados obtidos através do método desenvolvido são compatı́veis com os valores obtidos pelo método manual de medição. O desempenho obtido pela técnica desenvolvida indica que a metodologia proposta tem potencial de aplicação em consultórios oftalmológicos

A Method for Detecting Pathologies in Concrete Structures Using Deep Neural Networks

Pathologies in concrete structures, such as cracks, splintering, efflorescence, corrosion spots, and exposed steel bars, can be visually evidenced on the concrete surface. This paper proposes a method for automatically detecting these pathologies from images of the concrete structure. The proposed method uses deep neural networks to detect pathologies in these images. This method results in time savings and error reduction. The paper presents results in detecting the pathologies from wide-angle images containing the overall structure and also for the specific pathology identification task for cropped images of the region of the pathology. Identifying pathologies in cropped images, the classification task could be performed with 99.4% accuracy using cross-validation and classifying cracks. Wide images containing no, one, or several pathologies in the same image, the case of pathology detection, could be analyzed with the YOLO network to identify five pathology classes. The results for detection with YOLO were measured with mAP, mean Average Precision, for five classes of concrete pathology, reaching 11.80% for fissure, 19.22% for fragmentation, 5.62% for efflorescence, 27.24% for exposed bar, and 24.44% for corrosion. Pathology identification in concrete photos can be optimized using deep learning