Characterisation of human non-proliferative diabetic retinopathy using the fractal analysis

· AIM: To investigate and quantify changes in the branching patterns of the retina vascular network in diabetes using the fractal analysis method.·METHODS: This was a clinic-based prospective study of 172 participants managed at the Ophthalmological Clinic of Cluj-Napoca, Romania, between January 2012 and December 2013. A set of 172 segmented and skeletonized human retinal images, corresponding to both normal(24 images) and pathological(148 images)states of the retina were examined. An automatic unsupervised method for retinal vessel segmentation was applied before fractal analysis. The fractal analyses of the retinal digital images were performed using the fractal analysis software Image J. Statistical analyses were performed for these groups using Microsoft Office Excel2003 and Graph Pad In Stat software.·RESULTS: It was found that subtle changes in the vascular network geometry of the human retina are influenced by diabetic retinopathy(DR) and can be estimated using the fractal geometry. The average of fractal dimensions D for the normal images(segmented and skeletonized versions) is slightly lower than the corresponding values of mild non-proliferative DR(NPDR) images(segmented and skeletonized versions).The average of fractal dimensions D for the normal images(segmented and skeletonized versions) is higher than the corresponding values of moderate NPDR images(segmented and skeletonized versions). The lowestvalues were found for the corresponding values of severe NPDR images(segmented and skeletonized versions).· CONCLUSION: The fractal analysis of fundus photographs may be used for a more complete understanding of the early and basic pathophysiological mechanisms of diabetes. The architecture of the retinal microvasculature in diabetes can be quantitative quantified by means of the fractal dimension.Microvascular abnormalities on retinal imaging may elucidate early mechanistic pathways for microvascular complications and distinguish patients with DR from healthy individuals.

Characterization of human retinal vessel arborisation in normal and amblyopic eyes using multifractal analysis

AIMTo characterize the human retinal vessel arborisation in normal and amblyopic eyes using multifractal geometry and lacunarity parameters.METHODSMultifractal analysis using a box counting algorithm was carried out for a set of 12 segmented and skeletonized human retinal images, corresponding to both normal (6 images) and amblyopia states of the retina (6 images).RESULTSIt was found that the microvascular geometry of the human retina network represents geometrical multifractals, characterized through subsets of regions having different scaling properties that are not evident in the fractal analysis. Multifractal analysis of the amblyopia images (segmented and skeletonized versions) show a higher average of the generalized dimensions (Dq) for q=0, 1, 2 indicating a higher degree of the tree-dimensional complexity associated with the human retinal microvasculature network whereas images of healthy subjects show a lower value of generalized dimensions indicating normal complexity of biostructure. On the other hand, the lacunarity analysis of the amblyopia images (segmented and skeletonized versions) show a lower average of the lacunarity parameter Λ than the corresponding values for normal images (segmented and skeletonized versions).CONCLUSIONThe multifractal and lacunarity analysis may be used as a non-invasive predictive complementary tool to distinguish amblyopic subjects from healthy subjects and hence this technique could be used for an early diagnosis of patients with amblyopia.