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Diabetic macular edema (DME) characterized by discrete white{yellow lipid deposits due to
vascular leakage is one of the most severe complication seen in diabetic patients that cause
vision loss in affected areas. Such vascular leakage can be treated by laser surgery. A regular
follow{up and laser photocoagulation can reduce the risk of blindness by 90%. In an automated
retina screening system, it is thus very crucial to make the segmentation of such hard exudates
accurate and register these images taken over time to a reference co-ordinate system to make the
necessary follow-ups more precise. We introduce a novel method of ethnicity based statistical
atlas for exudates segmentation and follow-up. Ethnic background plays a significant role in
retinal pigment epithelium, visibility of the choroidal vasculature and overall retinal luminance in
patients and retinal images. Such statistical atlas can thus help to provide a solution, simplify the
image processing steps and increase the detection rate. In this paper, bright lesion segmentation
is investigated and experimentally verified for the gold standard built from African American
fundus images.
40 automatically generated landmark points on the major vessel arches with macula and
optic centers are used to warp the retinal images. PCA is used to obtain a mean shape of the
retinal major arches (both lower and upper). The mean of the co-ordinates of the macula and
optic disk center are obtained resulting 42 landmark points and together they provide a reference
co-ordinate frame ( or the atlas co-ordinate frame) for the images. The retinal funds images of an
ethnic group without any artifact or lesion are warped to this reference co-ordinate frame from
which we obtain a mean image representing the statistical measure of the chromatic distribution
of the pigments in the eye of that particular ethnic group.
400 images of African American eye has been used to build such a gold standard for this ethnic
group. Any test image of the patient of that ethnic group is first warped to the reference frame
and then a distance map is obtained with this mean image. Finally, the post-processing schemes
are applied on the distance map image to enhance the edges of the exudates. A multi-scale and
multi-directional steerable filters along with the Kirsch edge detector was found to be promising.
Experiments with the publicly available HEI-MED dataset showed the good performance of the
proposed method. We achieved the lesion localization fraction (LLF) of 82.5% at 35% of
non{lesion localization fraction (NLF) on the FROC curve.
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