Siemens Corporation

Siemens Corporate Research, Inc.

Ke Huang

Hans Schüll

Michelle Xiao-Hong Yan

Xiao-Hong Michelle Yan

A local adaptive method is proposed for automatic detection of microaneurysms in a digital ocular fundus image. Multiple subregions of the image are automatically analyzed and adapted to local intensity variation and properties. A priori region and location information about structural features such as vessels, optic disk and hard exudates are incorporated to further improve the detection accuracy. The method effectively improves the specificity of microaneurysms detection, without sacrificing sensitivity. The method may be used in automatic level-one grading of diabetic retinopathy screening.

Method and system for local adaptive detection of microaneurysms in digital fundus images

A method for optic disk detection in retinal images, includes: extracting a vessel tree from a retinal image; locating an optic disk in the retinal image using the vessel tree; enhancing a contrast of the optic disk; removing vessels from the contrast enhanced optic disk; detecting a boundary of the optic disk with vessels removed by generating an edge map; projecting the edge map at a plurality of angles by using a radon transform; and estimating a radius and center of the optic disk using the projections.

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East Lansing, MI, United States of America

Ke Huang