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Objective To assess the repeatability of blood flow velocity index (BFVi) metrics obtained with a recently FDA-cleared laser speckle contrast imaging device, the XyCAM RI, and characterize differences in these metrics between control, glaucoma suspect, and glaucoma subjects. Design Prospective observational study Participants 46 subjects (20 control, 16 glaucoma suspect, and 10 glaucoma; one eye per subject) Methods Key dynamic BFVi metrics–mean, peak, dip, volumetric rise index (VRI), volumetric fall index (VFI), time to rise (TtR), time to fall (TtF), blow out time (BOT), skew, acceleration time index (ATI)–were measured in the optic disc, optic disc vessels, optic disc perfusion region, and macula in four imaging sessions on the same day. Intrasession and intersession variability were calculated using the coefficient of variation (CV) for each metric in each region of interest (ROI). Values for each dynamic BFVi variable were compared between glaucoma subjects, glaucoma suspects, and controls using bivariate and multivariable analysis. Pearson correlation coefficients were used to correlate each variable in each ROI with age, intraocular pressure, cup to disc ratio, mean deviation, pattern standard deviation, retinal nerve fiber layer thickness, and minimum rim width. Main outcome measures CV for the intrasession and intersession variability for each dynamic BFVi metric in each ROI, and differences in each metric in each ROI between each diagnostic group. Results The intersession CV for mean, peak, dip, as well as VRI, VFI, TtR, and TtF ranged from 3.2 ± 2.5% to 11.0 ± 3.8%. Age, cup-to-disc ratio, optical coherence tomography metrics, and visual field metrics showed significant correlations with dynamic BFVi variables. Peak, mean, dip, VRI, and VFI, were significantly lower in glaucoma subjects than in control subjects in all ROI except the fovea. These metrics were also significantly lower in glaucoma subjects than suspects in the disc vessels. Conclusions Dynamic blood flow metrics measured with the XyCAM RI are reliable, associated with structural and functional glaucoma metrics, and significantly different between glaucoma subjects, glaucoma suspects, and controls. The XyCAM RI may serve as an important tool in glaucoma management in the future.

Iris melanocytic tumours, are the most dangerous tumours in the eye , commonly known as eye tumours. This includes freckle, nevus, melanocytoma, Lisch nodule, and melanoma. The detection of eye tumour is very difficult in early stages. Many research works are being carried out to detect eye diseases. But few research works in the eye tumour were published. Most of the system needs specific data acquisition devices to capture the region. This is very expensive. To diagnose eye melanoma, doctors recommend PET - CT, eye ultrasound, angiogram, optical coherence tomography, etc. Here, a new approach is presented to detect the eye tumour from eye images using deep learning technique. The deep network model created with modified LeNet architecture. The model created with the segmented eyeball images. Hough circle transformation could predict the eyeball and iris regions. As the deep learning technique needs more data for training, the number of image data has been increased with image augmentation method. Successful testing of this method with an accuracy of 95% shows that this method can be implemented in real time applications.

The motivation behind this study is to detect brain tumour and provide better treatment for the sufferings. The abnormal growths of cells in the brain are called tumours and cancer is a term used to represent malignant tumours. Usually CT or MRI scans are used for the detection of cancer regions in the brain. Positron Emission Tomography, Cerebral Arteriogram, Lumbar Puncture, Molecular testing are also used for brain tumour detection. In this study, MRI scan images are taken to analyse the disease condition. Objective this research works are i) identify the abnormal image ii) segment tumour region. Density of the tumour can be estimated from the segmented mask and it will help in therapy. Deep learning technique is employed to detect abnormality from MRI images. Multi level thresholding is applied to segment the tumour region. Number of malignant pixels gives the density of the affected region.