Optical Coherence Tomography (OCT) is a noninvasive imaging modality that has significantly contributed to the quantitative assessment of ocular diseases. Another tool available to ophthalmic clinicians is in-vivo confocal microscopy, which allows anatomical structures to be observed live at the cellular level. Incorporating both of these modalities for imaging the cornea allows us to take structural measurements to characterize disease-related changes in corneal anatomy. Notable diseases that directly impact or correlate with corneal structures include glaucoma and diabetic neuropathy. Given glaucoma's impact as the second leading cause of blindness in the world, great efforts have been made in researching and understanding the disease. Correlations have been found between the central corneal thickness (CCT) and the risk of developing visual field loss in patients diagnosed with glaucoma. It should come as no surprise that measuring CCT among glaucoma suspects has also now become a clinical standard of practice. Diabetes is a group of metabolic diseases where the body experiences high blood sugar levels over prolonged periods of time. It is a prominent disease that affects millions of Americans each day. While not necessarily an ocular disease in its own right, it has been shown that diabetes can still affect the corneal structures. Diabetics have decreased corneal sensitivity and a significant link has been established between neuropathic severity in diabetic patients and corneal nerve fiber density. Given the availability of these imaging tools and the significant impact these prominent diseases have on society a growing focus has developed on relating corneal structure measurements and ophthalmic diseases. However, manually acquiring structural measures of the cornea can be a labor intensive and daunting task. Hence, experts have sought to develop automatic alternatives. The goals of our work includes the ability to automatically segment the corneal structures from anterior segment-optical coherence tomography (AS-OCT) and in-vivo confocal microscopy (IVCM) to provide useful structural information from the cornea. The major contributions of this work include 1) utilizing the information of AS-OCT imagery to segment the cornea layers simultaneously in 3D, 2) increasing the region-of-interest of IVCM imagery using a feature-based registration approach to develop a panorama from the images, 3) incorporating machine-learning techniques to segment the corneal nerves in the IVCM imagery, and 4) extracting structural measurements from the segmentation results to determine correlations between the structural measurements known to differ from the corneal structures in various subject groups.