STructured Analysis of the Retina
What is STARE?
The STARE (STructured Analysis of the Retina)
Project was conceived and initiated in 1975 by
Michael Goldbaum, M.D.,
at the University of California, San Diego.
It was funded by the U.S.
National Institutes of Health .
During its history, over thirty people contributed to the project,
with backgrounds ranging from medicine to science to engineering.
Images and clinical data were provided by the
Shiley Eye Center at the
University of California, San Diego, and by the
Veterans Administration Medical Center in San Diego.
I had the pleasure of working on the project from 1996-2004.
The contents of this web page reflect my contributions.
Please contact me if you have any questions or requests
concerning our data or code.
Please contact Dr. Goldbaum if you have any
requests concerning the current state of the project.
A brief overview of the project
An ophthalmologist is a medical doctor that specializes in the
structure, function, and diseases of the human eye.
During a clinical examination, an opthalmologist notes findings that
are visible in the eyes of the subject. The ophthalmologist then
uses these findings to reason about the health of the subject.
For instance, a patient may exhibit discoloration of the optic nerve,
or a narrowing of the blood vessels in the retina. An opthalmologist
uses this information to diagnose the patient, as having for instance
Coats' disease or a central retinal artery occlusion.
A common procedure during an examination is retinal imaging.
An optical camera is used to see through the pupil of the eye to
the rear inner surface of the eyeball. A picture is taken showing
the optic nerve, fovea, surrounding vessels, and the retinal layer.
The opthalmologist can then reference this image while considering
any observed findings.
This research concerns a system to automatically diagnose diseases
of the human eye. The system takes as input information observable
in a retinal image. This information is formulated to mimic the findings
that an ophthalmologist would note during a clinical examination.
The main output of the system is a diagnosis formulated to mimic the
conclusion that an ophthalmologist would reach about the health of
Our approach breaks the problem into two components. The first
component concerns automatically processing a retinal image to denote
the important findings.
The second component concerns automatically reasoning about the
findings to determine a diagnosis.
Additional outputs include detailed measurements
of the anatomical structures and lesions visible in the retinal image.
These measurements are useful for tracking disease severity and the
evaluation of treatment progress over time. By collecting a
database of measurements for a large number of people, the STARE
project could support clinical population studies and intern training.
The full set of ~400 raw images in the STARE database can be obtained
Small versions of all the images can be viewed 100 at a time:
diagnosis codes and
diagnoses for each image.
Expert annotations of the manifestations
(features) visible in each image, tabulated
here in text files.
A total of 44 possible manifestations were queried to the experts during
data collection and then reduced to 39 values during encoding.
This mapping provides details.
Blood vessel segmentation work
including 40 hand labeled images, our results, and a demo.
Artery/vein labelings of 10 images done by
expert 1 and
Optic nerve detection work
including 80 images with ground truth, and our results.
A lot has been published on this project by many people; these are
my two most relevant papers:
A. Hoover, V. Kouznetsova and M. Goldbaum,
"Locating Blood Vessels in Retinal Images by Piece-wise Threhsold
Probing of a Matched Filter Response",
IEEE Transactions on Medical Imaging , vol. 19 no. 3,
pp. 203-210, March 2000.
A. Hoover and M. Goldbaum,
"Locating the optic nerve in a retinal image using the fuzzy
convergence of the blood vessels",
IEEE Transactions on Medical Imaging , vol. 22 no. 8,
pp. 951-958, August 2003.
Source code for Microsoft Visual C.
The image processing portions are separated from the GUI and
are coded in plain C.
Last revised January 2013