RESUMO
Background and objective: With the development of image processing techniques, it has become possible to measure the changes in retinal vessels of hypertensive patients by means of eye fundus photographs. Patients and method: In this paper we aim to classify retinal vessels automatically into arterioles and venules. In order to do so, we have compared three different strategies based on the colour of the pixels in images through an analysis of 78 hypertensive patients' eye fundus images. The first strategy classifies all the vessels by applying a clustering algorithm. The second divides the retinal image into four quadrants and classifies the vessels that belong to the same quadrant independently from the rest of the vessels. The third strategy classifies the vessels by dividing the retinal image into four quadrants that are rotated inside the mentioned image. Results: The third strategy was the one that obtained the best results, since it minimizes the number of unclassified vessels. In the initially analysed set of 20 images, we correctly classified 86.53% of the vessels, and this percentage remains similar in a set of 58 images examined by three medical experts. This confirms the validity of the method that automatically calculates the arteriovenous ratio (AVR).Conclusion: Our results are an improvement on those previously described in the bibliography, reducing the number of non-classified vessels. Furthermore, the method entails low computational costs.
Fundamento y objetivo: El desarrollo de técnicas de procesado de imágenes ha devuelto interés para poder medir de una forma objetiva los cambios en la estructura microvascular del hipertenso a través de las fotografías digitales del fondo de ojo. Pacientes y método: Para clasificar de forma automática los vasos de la retina en arteriolas y vénulas, con una elevada precisión, hemos comparado tres estrategias diferentes basadas en la información del color de los pixeles de la imagen del fondo de ojo, analizando 78 imágenes de fondo de ojo de hipertensos. La primera estrategia clasificaría todos los vasos aplicando un algoritmo de agrupamiento. La segunda divide la retina en cuatro cuadrantes y clasifica los vasos que pertenecen al mismo cuadrante independientemente del resto de los vasos. La tercera estrategia clasifica los vasos dividiendo la retina en cuadrantes que son rotados. Resultados: La mejor estrategia resultó la tercera porque minimiza el error y el número de vasos no clasificados. La característica vectorial más determinante está basada en la media o la mediana del componente gris del espacio de color RGB. Para las 20 imágenes inicialmente analizadas hemos clasificado correctamente el 86.53% de los vasos, y este porcentaje permanece similar en el grupo de 58 imágenes examinadas por tres expertos, lo que confirma la validez del método, para el cálculo del índice arteriovenoso de forma automática. Conclusión: Nuestros resultados son superiores a los descritos previamente, reduciendo además el número de vasos no clasificados. Por otro lado, el costo computacional del método es bajo
Assuntos
Humanos , Vasos Retinianos , Retinopatia Hipertensiva , Fundo de Olho , Hipertensão , MicrocirculaçãoRESUMO
BACKGROUND: No studies have identified which patients with upper-extremity deep vein thrombosis (DVT) are at low risk for adverse events within the first week of therapy. METHODS: We used data from Registro Informatizado de la Enfermedad TromboEmbólica to explore in patients with upper-extremity DVT a prognostic score that correctly identified patients with lower limb DVT at low risk for pulmonary embolism, major bleeding, or death within the first week. RESULTS: As of December 2014, 1135 outpatients with upper-extremity DVT were recruited. Of these, 515 (45%) were treated at home. During the first week, three patients (0.26%) experienced pulmonary embolism, two (0.18%) had major bleeding, and four (0.35%) died. We assigned 1 point to patients with chronic heart failure, creatinine clearance levels 30-60 mL min(-1) , recent bleeding, abnormal platelet count, recent immobility, or cancer without metastases; 2 points to those with metastatic cancer; and 3 points to those with creatinine clearance levels < 30 mL min(-1) . Overall, 759 (67%) patients scored ≤ 1 point and were considered to be at low risk. The rate of the composite outcome within the first week was 0.26% (95% confidence interval [CI] 0.004-0.87) in patients at low risk and 1.86% (95% CI 0.81-3.68) in the remaining patients. C-statistics was 0.73 (95% CI 0.57-0.88). Net reclassification improvement was 22%, and integrated discrimination improvement was 0.0055. CONCLUSIONS: Using six easily available variables, we identified outpatients with upper-extremity DVT at low risk for adverse events within the first week. These data may help to safely treat more patients at home.