Optic disc detection based on fully convolutional neural network and structured matrix decomposition

Optic disc (OD) region provides a wealth of information for fundus images analysis. The accurate detection of OD contour is very important for the diagnosis and treatment of eye diseases. Considering that the OD region is generally a saliency area that distinguishes with the background in the fundus image, in this paper, we propose a unified OD detection approach by using the fully convolution neural network (FCNN) combined with the structured matrix decomposition (SMD) model, which can detect the OD region from original image directly excluding the localization step. First, the original fundus image is clustered into super pixels by Simple Linear Iterative Clustering (SLIC) algorithm, and its color, texture, and edge features are extracted to construct a feature matrix. Then, a hierarchical segmentation tree is established based on the spatial connectivity and feature similarity between super pixel patches. Finally, the SMD model and the high-level semantic prior knowledge provided by FCNN are used together to decompose the feature matrix into a sparse matrix representing the OD region and a low-rank matrix indicating the background. In this way, the OD region is derived from the sparse matrix obtained by decomposition. Our proposed method is evaluated on DRISHTI-GS dataset and IDRiD dataset and shows superior performance compared with the state-of-the-art methods.