A Variational Approach to Exploit Prior Information in Object-Background Segregation: Application to Retinal Images

One of the main challenges in image segmentation is to adapt prior knowledge about the objects/regions that are likely to be present in an image, in order to obtain more precise detection and recognition. Typical applications of such knowledge-based segmentation include partitioning satellite images and microscopy images, where the context is generally well defined. In particular, we present an approach that exploits the knowledge about foreground and background information given in a reference image, in segmenting images containing similar objects or regions. This problem is presented within a variational framework, where cost functions based on pair-wise pixel similarities are minimized. This is perhaps one of the first attempts in using non-shape based prior information within a segmentation framework. We validate the proposed method to segment the outer nuclear layer (ONL) in retinal images. This approach successfully segments the ONL within an image and enables further quantitative analysis.