Segmentation of the Optic Disc, Macula and Vascular Arch in Fundus Photographs

• Published in: IEEE transactions on medical imaging Vol. 26; no. 1; pp. 116 - 127
• Main Authors: Niemeijer, Meindert, Abramoff, Michael D., van Ginneken, Bram
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2007; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Anatomical structure; Artificial Intelligence; Blindness; Cities and towns; Cost function; Diabetes; Diabetic retinopathy; Diabetic Retinopathy - diagnosis; Fluorescein Angiography - methods; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Information Storage and Retrieval - methods; Macula Lutea - pathology; Optic Disk - pathology; Pathology; Pattern Recognition, Automated - methods; point distribution model; Reproducibility of Results; Retina; Retina - pathology; Retinal Vessels - pathology; Retinopathy; screening; Sensitivity and Specificity; Structural discs; Studies; System testing; vascular atlas
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2006.885336

An automatic system is presented to find the location of the major anatomical structures in color fundus photographs; the optic disc, the macula, and the vascular arch. These structures are found by fitting a single point-distribution-model to the image, that contains points on each structure. The method can handle optic disc and macula centered images of both the left and the right eye. The system uses a cost function, which is based on a combination of both global and local cues, to find the correct position of the model points. The global terms in the cost function are based on the orientation and width of the vascular pattern in the image. The local term is derived from the image structure around the points of the model. To optimize the fit of the point-distribution-model to an image, a sophisticated combination of optimization processes is proposed which combines optimization in the parameter space of the model and in the image space, where points are moved directly. Experimental results are presented demonstrating that our specific choices for the cost function components and optimization scheme are needed to obtain good results. The system was developed and trained on a set of 500 screening images, and tested on a completely independent set of 500 screening images. In addition to this the system was also tested on a separate set of 100 pathological images. In the screening set it was able to find the vascular arch in 93.2%, the macula in 94.4%, the optic disc location in 98.4% and whether it is dealing with a left or right eye in 100% of all tested cases. For the pathological images test set, this was 77.0%, 92.0%, 94.0%, and 100% respectively.