Glaucoma progression detection using variational expectation maximization algorithm

Glaucoma, the second leading cause of blindness worldwide, is an optic neuropthy characterized by distinctive changes in the optic nerve head (ONH) and visual field. In this context, the Heidelberg Retina Tomograph (HRT), a confocal scanning laser technology, has been commonly used to detect glaucoma and monitor its progression. In this paper, we present a new framework for detection of glaucomatour progression using the HRT images. In contrast to previous works that do not integrate a priori knowledge available on the images and particularly the spatial pixel dependency in the change detection map, we propose the use of the Markov Random Field to handle a such dependency. To our knowledge, the task of inferring the glaucomatous changes with a Variational Expectation Maximization VEM algorithm will be used for the first time in the glaucoma diagnosis framework. We then compared the diagnostic performance of the proposed framework to existing methods of progression detection.