Detection of Primary Glaucoma in Humans Using Simple Linear Iterative Clustering (SLIC) Algorithm

It is a well-known fact in the world that the glaucoma is the second largest disease which is affecting the human beings in the world. Proper care has to be taken to avoid this at an early stage as this would result in the loss of vision in the humans. This occurs due to the increase in the pressure in the eyes, where it bursts the nerve fibres leading to the vision loss. If the patient goes to the doctor, it is an expensive treatment. Hence, we are devising a low cost module method of detecting the primary glaucoma in the humans using their fundus images. The images of the patients will be taken by the fundus camera, analyzed & a info is given to the patient that he/she is affected with the disease. Once the person comes to know that they are affected, then proper diagnosis can be done by consultations from the hospital experts. The method of detecting the primary glaucoma is being presented in this section using a revised simple linear iterative clustering (SLIC) algorithm clubbed with edge detection using canny edge operators. SLIC concepts are being used for the segmentation process of the cup and the disc & finally the region of interest, i.e., the cup and the disc areas are found out from which the ratio is computed, from where the disease can be detection seeing the ratio. The simulation results shown the effectivity of the method proposed by us in this research work.