Blood vessels usually have poor local contrast, and the application of existing edge detection algorithms yield results which are not satisfactory. An operator for feature extraction based on the optical and spatial properties of objects to be recognized is introduced. The gray-level profile of the cross section of a blood vessel is approximated by a Gaussian-shaped curve. The concept of matched filter detection of signals is used to detect piecewise linear segments of blood vessels in these images. Twelve different templates that are used to search for vessel segments along all possible directions are constructed. Various issues related to the implementation of these matched filters are discussed. The results are compared to those obtained with other methods.

Systemic hypertension is a common condition associated with significant morbidity and mortality. Hypertension confers cardiovascular risk by causing target-organ damage that includes retinopathy in addition to heart disease, stroke, renal insufficiency and peripheral vascular disease. The recognition of hypertensive retinopathy is important in cardiovascular risk stratification of hypertensive individuals. This review reevaluates the changing perspectives in the pathophysiology, classification and prognostic significance of fundal lesions in hypertensives.

Medical imaging is shifting from film to electronic images. The STAR.E (structured analysis of the retina) system is a sophisticated image management system that will automatically diagnose images, compare images, measure key features in images, annotate image contents, and search for images similar in content. We concentrate on automated diagnosis. The images are annotated by segmentation of objects of interest, classification of the extracted objects, and reasoning about the image contents. The inferemcing is accomplished with Bayesian networks that learn from image examples of each disease. This effort at image understanding in fundus images anticipates the future use of medical images. As these capabilities mature, we expect that ophthalmologists and physicians in other fields that rely in images will use a system like STARE to reduce repetitive work, to provide assistance to physicians in difficult diagnoses or with unfamiliar diseases, and to manage images in large image databases.

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IntroductIon 1 rama Mohana r. turaga and Kalyan Bhaskar E lectronic waste (e-waste), that is, waste arising from end-of-life electronic products such as computers and mobile phones, is one of the fastest growing waste streams in the world today. Annual global production of e-waste is estimated to surpass 50 million tons in 2020. 2 India is among the top five e-waste producing countries in the world with estimated annual production of 2 million tons. Like some of the other developing countries, e-waste management in India is dominated by the informal sector with estimates of more than 90 per cent of the waste being processed in this sector. E-waste contains several precious metals, rare earth metals, ferrous and non-ferrous metals, plastic, wood and glass. Unscientific practices in the processing of e-waste are associated with several environmental and health externalities. 3 In response to these concerns, many developed and devel-1 The earlier versions of the articles in this colloquium (except for the article by Hitesh Sharma) were written for the report 'E-waste Roadmap 2023 for India', an initiative under the India E-waste Program of the International Finance Corporation (IFC). The programme was supported by the Government of Japan and the Korea Green Growth Trust Fund of the World Bank. We gratefully acknowledge the contribution of Neeta Misra, Sarina Bolla and Kalyan Bhaskar, the editors of the 'E-waste Roadmap 2023 for India' report, who worked with a team of experts on e-waste in India and were responsible for the editing of the initial versions of many of the articles in the colloquium.