Image segmentation is the process of dividing an electronic digital image into numerous sub-images. Its objective is to categorize image into various regions in such a way that every potential object in image gets individual sector. Instinctive recognition of diabetic retinopathy wounds, like exudates can provide opportunity to identify certain diseases. Lack of accuracy in these techniques can lead to fatal results because of incorrect treatment. So, there is a great need for automation techniques with high accuracy for retinal disease identification. Several automation techniques have been proposed for retinal image analysis which can detect the exudates in fundus images in more promising manner. The related work has found that the issue of noise in fundus images is ignored in the majority of existing literature. Although Gabor filter bank has shown significant results over available techniques, but it is poor in its speed. Also it is not very efficient for multiple kinds of noises at a same time. Therefore to improve the accuracy of exudate extraction further a Hybrid Gabor filter bank with trilateral based filtering technique is proposed. This filtering will use improved trilateral filtering which enables us to detect exudates even in highly corrupted noisy images.