20-04-2012, 01:08 PM
Detection of Diabetic Symptoms in Retina Images Using Analog Algorithms
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INTRODUCTION
DIABETIC neuropathy is one of the serious complications
caused by diabetes, causing pathological changes in the
retina, which is the most important tissue of the eye, and
therefore affects vision [1], [2]. There may exist different
kinds of abnormal lesions caused by diabetic retinopathy, the
most frequent being microaneurysm, hard exudate, soft
exudate, hemorrhage, and neovascularization. All these
pathologies have specific characteristics and are important in
the clinical assessment of this disorder. Microaneurysms are
the earliest clinically detectable lesions. Neovascularization is
the most serious abnormality type in diabetic retinopathy and
consists in the formation of new blood vessels that are weak
and can therefore easily break, causing hemorrhages which
appear as dark irregular spots on the retina. Hard exudates are
lipid formations leaked from weakened vessels and usually
appear in clusters. Soft exudates (micro-infarctions) appear
due to obstruction of retinal arterioles. Both hard and soft
exudates lesions appear brighter than the neighborhood.
CONCLUSION
Some analog algorithms were discussed, which may find
applications in biomedical image processing, specifically in
retinal images which may present pathological aspects caused
by diabetes, generally referred to as retinopathy. These tasks
are easily implemented in cellular neural network systems
using minimum size templates, mainly linear. Due to the
parallel processing performed by the array structure, the
algorithms are fast and can be grouped in analog subroutines;
the template parameters are adjusted digitally. The advantage
of these systems is that no analog-digital image conversions
are necessary, the processing is entirely done in the analog
domain, but under digital control, which ensures precision and
robustness. Several analog processing tasks were presented,
including morphological operations, linear filtering,
thresholding etc., some proposed by the authors. These can be
used as a pre-processing step in a more elaborate assisted
diagnosis system, which may assess the presence of specific
retinal lesions of diabetic origin. Here only a few such tasks
were discussed, but the applicability of analog processing
tasks is much larger.