Methods for analysis of corneal deformation images from the Corvis tonometer can be different. This monograph shows only one of the possible approaches to image analysis and one of possible GUI solutions. The presented solution works well in medical practice providing a number of additional parameters impossible to obtain in the Corvis tonometer software. Their interpretation and finding the link with the type of the patient’s disease constitute the next step of analysis. Surely it should be the analysis made using one of classification possibilities (e.g. available in Matlab): decision trees, neural networks, naive Bayesian classifier or support vector machine (SVM). For this purpose there are profiled functions/toolboxes: classregtree, Neural Network Toolbox, predict, svmtrain and svmclassify respectively. They should be used to verify if classification is correct for the data coming from different medical institutions. The number of features used for classification should also be limited to the most significant ones and at least three times smaller than the number of analysed cases. Classification results, at the stage of training the classifier, should be verified by an ophthalmologist (expert) or compared with other measurement results obtained using more accurate methods. This will prevent excessive overfitting to the data, which is also the consequence of excessive algorithmic profiling [1]. The classification of patients may therefore relate to various diseases and issues: for example, the impact of keratoconus, IOP, Age-related Macular Degeneration (AMD) or diabetes to changes in features w. Thus the features w, whose measurement method is presented in this monograph, can be used in many different ways. In extreme cases, the proposed algorithm can be also profiled for use in other fields of ophthalmology and medicine. The GUI can be modified in any way by choosing other parameters associated with the location of buttons and images. The given source code also enables to compile and use modules of the algorithm in C language. It is therefore possible to combine fragments of the presented algorithm with the existing Corvis tonometer software. Finally, the presented algorithms and this monograph, due to the full availability of source codes without any limitation, can be used for teaching purposes, as one example of using Matlab in teaching. In conclusion, I would like to encourage readers to make their own modifications and improvements in the presented application. Perhaps this approach will increase the popularity and versatility of measurements and diagnostics using the Corvis tonometer.