The cornea of the human eye is the main functional element for vision. The visual data information is transferred from all the layers of the cornea to the focal point of the retina. optical nerve acts as media between the cornea and brain. The image data sets are acquired from the confocal microscope, which improves the penetration depth in order to extract from all five layers. Two datasets are acquired from confocal endothelium image and pre-processed and analysed. The data set ‘1’consists of ‘2’ normal and ‘3’ abnormal images. And dataset ‘2’ consists of five abnormal images. The image is segmented for identifying the cell contours and boundary conditions of the cell. The contrast levels of the image are enhanced with histogram equalization. The high-frequency noise signal is curtailed with a gaussian filter. The image is further processed for identifying the dark objects using auto metric threshold method. The cell density, cell structure, Pleomorphism, Polymegathism, Elongation Factor (EF), Compactness Factor (CF), and Heywood Circularity Factor (HCF) has been meticulously estimated with Particle Filter (PF) approach for each endothelium cells. The estimated results are compared with the existing morphological operation 1(MO1), morphological operation 2 (MO2) and S-PSO (Snake Particle Swarm Optimization) Approaches. From the experiment, the proposed approach obtains satisfactory results in part of (average time inspection) is 18.49 m/s and Standard Deviation (SD) is 0.51 m/s per image is meticulously calculated. © School of Engineering, Taylor’s University