RESUMEN
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
Asunto(s)
Humanos , Vasos Retinianos , Retinopatía Hipertensiva , Fondo de Ojo , Hipertensión , MicrocirculaciónRESUMEN
BACKGROUND: The effect on survival of delays in the consultation, diagnostic and treatment processes of lung cancer (LC) is still under debate. The objective of our study was to analyse these time delays and their possible effect on survival. METHODS: A retrospective study has been performed on all patients in our health area diagnosed with LC (confirmed by cytohistology) over 3 years. The delay in specialist consultation (time between start of symptoms and the first consultation with a specialist), hospital delay (time between first consultation and start of treatment) and overall delay (the sum of the previous two delays) were analysed. The influence of each of these delays was calculated using Cox regression, adjusted for other factors. RESULTS: A total of 415 patients were included. Of these, 92.5% were male and 75.4% were in stages III-B or IV. The overall delay gave a mean of 123.6 days, the delay in consulting a specialist 82.1 days and the delay in hospitalisation was 41.4 days. A greater overall delay or greater hospital delay was associated with longer survival. No relationship was observed between the specialist consultation delay and survival. CONCLUSIONS: Globally analysing all the cases and all the stages with LC, it is seen that longer delays are associated with longer survival. This probably reflects the fact that patients with more symptoms are treated more rapidly.