Age Related Macular Degeneration (ARMD) is a frequent disease appearing mainly in elderly people, commonly diagnosed by ophthalmologists during patient examination. ARMD is characterized by the accumulation of extra cellular materials under retina seen in retina images as yellowish spots and called Drusen spots. Until now, ophthalmologists analysis has done manually, based only on qualitative aspects. However this process is highly dependent on the ophthalmologist and is very difficult to reproduce.In this paper we propose a methodology for an automatic Drusen analysis, which models each Drusen spot calculating important information like Drusen area, volume and location. The proposed methodology is a reproducible process independent of the clinician allowing the comparison of, images taken in different instants to evaluate treatment effectiveness and disease evolution.To achieve our objectives four different algorithms were used: the first divides the original image in smaller images grouping Drusen neighbours, the second algorithm is based in a Gaussian Blur filter and corrects the non-uniform illumination. The third algorithm based on labelling gradient paths and is responsible for giving Drusen spots location and intensity. The last algorithm is based in Levenberg-Marquardt methodology and is responsible for this modelling of each Drusen spot.

A Degeneracao da Macula Relacionada com a Idade (DMRI) e uma doenca dos olhos que e caracterizada pelo aparecimento de manchas de tom amarelado na retina. Estas manchas sao vulgarmente chamadas de Drusas. Estas manchas sao normalmente objecto de analise dos oftalmologistas de forma a verificar a eficiencia dos tratamentos efectuados. Neste trabalho e proposta uma metodologia para ajudar a comunidade de oftalmologistas a analisar as manchas de Drusas. O objectivo desta metodologia e encontrar um modelo matematico que caracterize a imagem a analisar. A partir desta tecnica e possivel eliminar o ruido e a analise da imagem pode ser reproduzida de forma independente do medico, permitindo ainda obter medicoes mais precisas do que as efectuadas manualmente. No processo de modelacao sao usados diversos algoritmos tais como: algoritmo de correccao da iluminacao nao uniforme baseado num filtro gaussiano, algoritmo de localizacao das Drusas baseado em etiquetagem do caminho do maior gradiente, algoritmo de agrupamento das Drusas em imagens individuais baseado em componentes ligadas e algoritmo de optimizacao baseado no algoritmo de Levenberg-Marquardt.

Drusens are indicators of macular degeneration, a disease characterized by accumulations of extra cellular materials under retina. The automatic study of the quantitative evolution of Drusen spots throughout a medical treatment constitutes a useful tool for ophthalmologists. Until now, drusen evaluation was done manually, based only on qualitative aspects. Also the analyses depended on the ophthalmologist and were not ease to reproduce.Another important issue is that retina is not a plane surface and therefore light doesn't have a uniform distribution, producing images with non-uniform illumination and consequently with different contrast areas.Computer aided tools that can help doctors in repetitive analysis are becoming more reliable and faster, contributing for its increasing acceptance. In this paper it is presented an algorithm based on smoothing splines for non-uniform illumination correction, and a software application based on Levenberg-Marquardt optimization algorithm for automatic modelling of drusen deposits in retina images. For improving the modelling process a customized algorithm for detecting image intensity maximums based on labelling gradient paths is presented. The results of applying this methodology to retina images containing medium sized Drusen will also be presented.

Age Related Macula Degeneration (ARMD) is an eye disease characterized by the appearing of yellowish spots in the retina. These spots are usually called Drusen spots. Drusen spots are object of analysis by the ophthalmologists for treatment effectiveness evaluation. In this paper we proposed a methodology to help ophthalmologist community in Drusen spots analysis. The objective of our methodology is to achieve a mathematical model of the original image. With this technique noise is removed, analysis can be reproduced independently of the clinician and Drusen spots measures can be done accurately. In the modelling process are used several algorithms: non-uniform illumination correction, based on gaussian blur filter; Drusen location algorithm based on labelling of the maximum gradient path; Levenberg- Marquardt optimization algorithm for image modelling. It is also used an algorithm to divide each original image in smaller images containing Drusen islands.