TITLE:
Segmentation and texture analysis with multimodel inference for the automatic detection of exudates in early diabetic retinopathy
AUTHORS:
Jack Lee, Benny Zee, Qing Li
KEYWORDS:
Texture Analysis; Multimodel Inference; Morphological Technique; Exudates; Diabetic Retinopathy
JOURNAL NAME:
Journal of Biomedical Science and Engineering,
Vol.6 No.3,
March
28,
2013
ABSTRACT: Diabetic retinopathy (DR) is an eye
disease caused by the increase of insulin in blood and may cause blindness if
not treated at an early stage. Exudates are the primary sign of DR. Currently
there is no fully automated method to detect exudates in the literature and
it would be useful in large scale screening if fully automatic method is
available. In this paper we developed a novel method to detect exudates that
based on interactions between texture analysis and segmentation with
mathematical morphological technique by using multimodel inference. The texture
analysis involves three components: they are statistical texture analysis,
high order spectra analysis, and fractal analysis. The performance of the
proposed method is assessed by the sensitivity, specificity and accuracy using
the public data DIARETDB1. Our results show that the sensitivity, specificity
and accuracy are 95.7%, 97.6% and 98.7% (SE = 0.01), respectively. It is shown
that the proposed method can be run automatically and also improve the
accuracy of exudates detection significantly over most of the previous methods.