Application Of Markov Random Field In Retinal Vascular Segmentation

Eyes are the windows of the soul. Only retinal vessels can be observed in all deep micro vascular network directly with non-invasively, and the retinal vascular network of the human body is unchanged under normal circumstances. However, the cerebrovascular diseases such as glaucoma, diabetes and so on can contribute to the change of structure and shape of retinal vascular network. Therefore, the clinical diagnosis and treatment of glaucoma, diabetes, hypertension, atherosclerosis and other diseases, is on the basis of the changes of retinal vascular network detection. But owing to the retinal vascular network distribution is very complex and small, the manual analysis of vessel is very complicated and the number of experienced clinicians is limited, it is very significant to study the diagnosis of glaucoma by machine which is directed against the method of the time-consuming manual segmentation.Markov random field (MRF) image segmentation is a kind of image segmentation method built on statistical theory, which can combine space relationship of image pixels, reflect the underlying structure and the random of the image. Besides understanding the mathematical concept from physical model, it can also fit the grayscale or characteristics of the image directly. Markov random field model has been widely studied and applied in the field of image segmentation on account of the advantages such as making full use of prior knowledge, few parameters and easy to combine with other methods(massively parallel algorithm). It is worth mentioning that this paper achieved certain results by using markov random field theory to divide fundus retinal blood vessels and applying to the diagnosis of glaucoma. The main achievements of the article was divided into the following two pieces:(1) the maximum a posteriori probability model (MAP-MRF) embedded in the iterated conditional mode (ICM) in the segmentation of retinal blood vessels and it has compared to the traditional segmentation algorithms (clustering) of medical image of retinal blood vessels segmentation, MRF segmentation method in vessel segmentation performs a better filtering. (2) After retinal blood vessels segmentation based on MRF are obtained, this paper puts forward to changing detection method of the retinal nerve fiber layer (RNFL) based on Gauss Markov random field model. This method first uses Gauss Markov random field (GMRF) on retinal texture modeling, and the GMRF model parameters through using the method of least squares (LSE) estimation. The parameters of the model as a feature vector of texture for retinal nerve fiber layer to classify the fundus health and glaucoma, then identify glaucoma.