Sensitivity Detection of Retinal Nerve Fiber Layer in Glaucoma Based on High Level Semantic Image Fusion Algorithm

Abstract

Glaucoma is currently recognized as a multifactorial, persistent and degenerative retinal disease. It mainly causes the loss of function and death of retinal ganglion cells in the optic nerve head area, and eventually leads to visual loss and blindness. Aiming at the segmentation and\n sensitivity detection of retinal nerve fiber layer (RNFL) in glaucoma, this paper mainly studies it based on high-level semantic image fusion algorithm. Firstly, the feature extracted by high-level semantic image fusion technology is used to train random forest classifier to segment retina\n to get rough position of the boundary of nerve fiber layer, then the first step of rough boundary is refined by boundary tracking algorithm to get the final result of retinal layer segmentation. In this algorithm, random forest classifier is used to find the boundary of single pixel width\n between layers of retina, and 12 features are used to train random forest classifier. Among them, relative gray feature and neighborhood feature can solve the problem of large segmentation error of uneven gray. By using high-level semantic image technology, the mean value and gradient features\n are extracted under multi-scale, and the relative gray difference features and neighborhood features are introduced, then the features are trained by random forest classifier. The trained classifier gives different labels to the unclassified features, and finally successfully segments the\n lower boundary of the retinal nerve fiber layer, which solves the sensitive segmentation and detection problem of the retinal nerve fiber layer with uneven pixel gray.