Breast cancer is a leading cause of death nowadays in women throughout the world. In developed countries, it is the most common type of cancer in women, and it is the second or third most common malignancy in developing countries. The cancer incidence is gradually increasing and remains a significant public health concern. The limitations of mammography as a screening and diagnostic modality, especially in young women with dense breasts, necessitated the development of novel and more effective strategies with high sensitivity and specificity. Thermal imaging (thermography) is a noninvasive imaging procedure used to record the thermal patterns using Infrared (IR) camera. The aim of this study is to evaluate the feasibility of using thermal imaging as a potential tool for detecting breast cancer. In this work, we have used 50 IR breast images (25 normal and 25 cancerous) collected from Singapore General Hospital, Singapore. Texture features were extracted from co-occurrence matrix and run length matrix. Subsequently, these features were fed to the Support Vector Machine (SVM) classifier for automatic classification of normal and malignant breast conditions. Our proposed system gave an accuracy of 88.10%, sensitivity and specificity of 85.71% and 90.48% respectively.

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BackgroundDiabetic retinopathy (DR) is a leading cause of vision loss in adults. Currently, the standard imaging technique to monitor and prognosticate DR and diabetic maculopathy is dye-based angiography. With the introduction of optical coherence tomography angiography (OCTA), it may serve as a potential rapid, non-invasive imaging modality as an adjunct.Main textRecent studies on the role of OCTA in DR include the use of vascular parameters e.g., vessel density, intercapillary spacing, vessel diameter index, length of vessels based on skeletonised OCTA, the total length of vessels, vascular architecture and area of the foveal avascular zone. These quantitative measures may be able to detect changes with the severity and progress of DR for clinical research. OCTA may also serve as a non-invasive imaging method to detect diabetic macula ischemia, which may help predict visual prognosis. However, there are many limitations of OCTA in DR, such as difficulty in segmentation between superficial and deep capillary plexus; and its use in diabetic macula edema where the presence of cystic spaces may affect image results. Future applications of OCTA in the anterior segment include detection of anterior segment ischemia and iris neovascularisation associated with proliferative DR and risk of neovascular glaucoma.ConclusionOCTA may potentially serve as a useful non-invasive imaging tool in the diagnosis and monitoring of diabetic retinopathy and maculopathy in the future. Future studies may demonstrate how quantitative OCTA measures may have a role in detecting early retinal changes in patients with diabetes.

Contact lens-related complications are common, affecting around one third of wearers, although most are mild and easily managed. Contact lenses have well-defined anatomical and physiological effects on the ocular surface and can result in other consequences due to the presence of a biologically active material. A contact lens interacts with the tear film, ocular surface, skin, endogenous and environmental microorganisms, components of care solutions and other antigens which may result in disease specific to contact lens wear, such as metabolic or hypersensitivity disorders. Contact lens wear may also modify the epidemiology or pathophysiology of recognised conditions, such as papillary conjunctivitis or microbial keratitis. Wearers may also present with intercurrent disease, meaning concomitant or pre-existing conditions unrelated to contact lens wear, such as allergic eye disease or blepharitis, which may complicate the diagnosis and management of contact lens-related disease.Complications can be grouped into corneal infection (microbial keratitis), corneal inflammation (sterile keratitis), metabolic conditions (epithelial: microcysts, vacuoles, bullae, tight lens syndrome, epithelial oedema; stromal: superficial and deep neovascularisation, stromal oedema [striae/folds], endothelial: blebs, polymegethism/ pleomorphism), mechanical (corneal abrasion, corneal erosion, lens binding, warpage/refractive error changes; superior epithelial arcuate lesion, mucin balls, conjunctival epithelial flaps, ptosis, discomfort), toxic and allergic disorders (papillary conjunctivitis, solution-induced corneal staining, incomplete neutralisation of peroxide, Limbal Stem Cell Deficiency), tear resurfacing disorders/dry eye (contact lens-induced dry eye, Meibomian gland dysfunction, lid wiper epitheliopathy, lid parallel conjunctival folds, inferior closure stain, 3 and 9 o'clock stain, dellen, dimple veil) or contact lens discomfort. This report summarises the best available evidence for the classification, epidemiology, pathophysiology, management and prevention of contact lensrelated complications in addition to presenting strategies for optimising contact lens wear.

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Diabetes is a condition of increase in the blood sugar level higher than the normal range. Prolonged diabetes damages the small blood vessels in the retina resulting in diabetic retinopathy (DR). DR progresses with time without any noticeable symptoms until the damage has occurred. Hence, it is very beneficial to have the regular cost effective eye screening for the diabetes subjects. This paper documents a system that can be used for automatic mass screenings of diabetic retinopathy. Four classes are identified: normal retina, non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), and macular edema (ME). We used 238 retinal fundus images in our analysis. Five different texture features such as homogeneity, correlation, short run emphasis, long run emphasis, and run percentage were extracted from the digital fundus images. These features were fed into a support vector machine classifier (SVM) for automatic classification. SVM classifier of different kernel functions (linear, radial basis function, polynomial of order 1, 2, and 3) was studied. Receiver operation characteristics (ROC) curves were plotted to select the best classifier. Our proposed system is able to identify the unknown class with an accuracy of 85.2%, and sensitivity, specificity, and area under curve (AUC) of 98.9%, 89.5%, and 0.972 respectively using SVM classifier with polynomial kernel of order 3. We have also proposed a new integrated DR index (IDRI) using different features, which is able to identify the different classes with 100% accuracy.

The authors have measured the evaporation rate on a sequence of thermographic images. A region of interest was selected at first and the same region on all the images were warped into a standard form. Calculations were performed based on the thermal data in those regions to obtain the values of interest. The authors found that the tear evaporation rate for subjects of all age groups was 57.36 +/- 12.73 Wm(-2) and the corneal tear evaporation was higher in elder subjects. The corneal rate of evaporation fluctuated in a larger magnitude in subjects who blinked more than average.