Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual Dictionary Techniques

With the advances in the computer field, methods and techniques in automatic image processing and analysis provide the opportunity to detect automatically the change and degeneration in retinal images. Localization of the optic disc is extremely important for determining the hard exudate lesions or neovascularization, which is the later phase of diabetic retinopathy, in computer aided eye disease diagnosis systems. Whereas optic disc detection is fairly an easy process in normal retinal images, detecting this region in the retinal image which is diabetic retinopathy disease may be difficult. Sometimes information related to optic disc and hard exudate information may be the same in terms of machine learning. We presented a novel approach for efficient and accurate localization of optic disc in retinal images having noise and other lesions. This approach is comprised of five main steps which are image processing, keypoint extraction, texture analysis, visual dictionary, and classifier techniques. We tested our proposed technique on 3 public datasets and obtained quantitative results. Experimental results show that an average optic disc detection accuracy of 94.38%, 95.00%, and 90.00% is achieved, respectively, on the following public datasets: DIARETDB1, DRIVE, and ROC

Optical Graph Recognition

Graphs are an important model for the representation of structural information between objects. One identifies objects and nodes as well as a binary relation between objects and edges. Graphs have many uses, e. g., in social sciences, life sciences and engineering. There are two primary representations: abstract and visual. The abstract representation is well suited for processing graphs by computers and is given by an adjacency list, an adjacency matrix or any abstract data structure. A visual representation is used by human users who prefer a picture. Common terms are diagram, scheme, plan, or network. The objective of Graph Drawing is to transform a graph into a visual representation called the drawing of a graph. The goal is a “nice” drawing. In this thesis we introduce Optical Graph Recognition. Optical Graph Recognition (OGR) reverses Graph Drawing and transforms a digital image of a graph into an abstract representation. Our approach consists of four phases: Preprocessing where we determine which pixels of an image are part of the graph, Segmentation where we recognize the nodes, Topology Recognition where we detect the edges and Postprocessing where we enrich the recognized graph with additional information. We apply established digital image processing methods and make use of the special property that the image contains nodes that are connected by edges. We have focused on developing algorithms that need as little parameters as possible or to automatically calibrate the parameters. Most false recognition results are caused by crossing edges as this makes tracing the edges difficult and can lead to other recognition errors. We have evaluated hand-drawn and computer-drawn graphs. Our algorithms have a very high recognition rate for computer-drawn graphs, e. g., from a set of 100000 computer-drawn graphs over 90% were correctly recognized. Most false recognition results where observed for hand-drawn graphs as they can include drawing errors and inaccuracies. For universal usability we have implemented a prototype called OGRup for mobile devices like smartphones or tablet computers. With our software it is possible to directly take a picture of a graph via a built in camera, recognize the graph, and then use the result for further processing. Furthermore, in order to gain more insight into the way a person draws a graph by hand, we have conducted a field study