Local characterization of neovascularization and identification of proliferative diabetic retinopathy in retinal fundus images.

Neovascularization (NV) is a characteristic of the onset of sight-threatening stage of DR, called proliferative DR (PDR). Identification of PDR requires modeling of these unregulated ill-formed vessels, and other associated signs of PDR. We present an approach that models the micro-pattern of local variations (using texture based analysis) and quantifies structural changes in vessel patterns in localized patches, to arrive at a score of neovascularity. The distribution of patch-level confidence scores is collated into an image-level decision of presence or absence of PDR. Evaluated on a dataset of 779 images combining public data and clinical data from local hospitals, the patch-level neovascularity prediction has a sensitivity of 92.4% at 92.6% specificity. For image-level PDR identification our method is shown to achieve sensitivity of 83.3% at a high specificity operating point of 96.1% specificity, and specificity of 83% at high sensitivity operating point of 92.2% sensitivity. Our approach could have potential application in DR grading where it can localize NVE regions and identify PDR images for immediate intervention.

Computer-assisted identification of proliferative diabetic retinopathy in color retinal images.

Advanced (proliferative) stage of diabetic retinopathy (DR) is indicated by the growth of thin, fragile and highly unregulated vessels, neovascularization (NV). In order to identify proliferative diabetic retinopathy (PDR), our approach models the micro-pattern of local variations using texture based analysis and quantifies the structural changes in vessel patterns in localized patches, to map them to the confidence score of being neovascular using supervised learning framework. Rule-based criteria on patch-level neovascularity scores in an image is used for the decision of absence or presence of PDR. Evaluated using 3 datasets, our method achieves 96% sensitivity and 92.6% specificity for localizing NV. Image-level identification of PDR achieves high sensitivity of 96.72% at 79.6% specificity and high specificity of 96.50% at 73.22% sensitivity. Our approach could have potential application in DR grading where it can localize NVE regions and identify PDR images for immediate intervention.

Celosia argentea Linn. leaf extract improves wound healing in a rat burn wound model.

Celosia argentea (CA) is used in traditional medicine for sores, ulcers, and skin eruptions. The present study was aimed at investigating the healing efficacy of CA extract in an ointment formulated (10 % w/w) as an alcohol extract of CA using a rat burn wound model. Wound closure occurred earlier in the treated rats (15 days vs. 30 in the untreated group; p < 0.05). Granulation tissue collected on every fifth day of healing showed an increase in collagen and hexosamine content at a faster rate in the treated wounds. This correlated with the accelerated wound closure observed in the treated groups. To probe the cellular basis of this effect, we investigated the effect of this extract on two major cellular responses; cell proliferation and cell motility, in two key cell lineages, fibroblasts and keratinocytes. CA was not toxic at concentrations of < 3 microg/ml in fibroblasts and < 30 microg/ml in keratinocytes. The alcohol extract promoted cell motility and proliferation of primary dermal fibroblasts at 0.1-1.0 microg/ml but did not alter these responses in primary keratinocytes. In an initial examination of molecular mechanisms, we found that the CA extract did not alter fibroblast and keratinocyte responses to the wound repair-associated epidermal growth factor receptor ligands. In short, we demonstrate a salutary action of the CA extract on wound healing, and suggest that this may be due to mitogenic and motogenic promotion of dermal fibroblasts.