Main Article Content
Here we address the detection of Hemorrhages and microaneurysms in color fundus images. In pre-Processing we separate red, green, blue color channel from the retinal images. The green channel will pass to the further process. The green color plane was used in the analysis since it shows the best contrast between the vessels and the background retina. Then we extract the GLCM(Gray Level Co-Occurance Matrix) feature. In the GLCMs, several statistics information are derived using the different formulas. These statistics provide information about the texture of an image. Such as Energy, Entropy, Dissimilarity, Contrast, Inverse difference , correlation Homogeneity, Auto correlation, Cluster Shade Cluster Prominence, Maximum probability, Sum of Squares will be calculated for texture image. After feature Extraction, we provide this feature to classifier. Finally it will predict about the retinal whether it is hemorrhages or microaneurysms . After predicting the about the retinal image we will localize the affected place. For segmenting the localized place we will use adaptive thresholding segmentation. .
How to Cite
, S. R. P. S. M. B. (2018). Detection of Diabetic Retinopathy Diseases for Color Fundus Images. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 4(5), 200–203. Retrieved from https://ijfrcsce.org/index.php/ijfrcsce/article/view/1673