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.

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This paper presents a generalized framework for segmenting closed-contour anatomical and pathological features using graph theory and dynamic programming (GTDP). More specifically, the GTDP method previously developed for quantifying retinal and corneal layer thicknesses is extended to segment objects such as cells and cysts. The presented technique relies on a transform that maps closed-contour features in the Cartesian domain into lines in the quasi-polar domain. The features of interest are then segmented as layers via GTDP. Application of this method to segment closed-contour features in several ophthalmic image types is shown. Quantitative validation experiments for retinal pigmented epithelium cell segmentation in confocal fluorescence microscopy images attests to the accuracy of the presented technique.

Many of the images encountered during scholarly studies in the fields of art and archaeology have deteriorated through the effects of time. The Ice -Age rock art of the now -closed caves near Lascaux are prime examples of this fate. However, faint and subtle details of these can be exceedingly important as some theories suggest that the designs are computers or calendars pertaining to astronomical cycles as well as seasons for hunting, gathering, and planting. Consequently, we have applied a range of standard image processing algorithms (viz., edge detection, spatial filtering, spectral differencing, and contrast enhancement) as well as specialized techniques (e.g., matched filters) to the clarification of these drawings. Also, we report the results of computer enhancement studies pertaining to authenticity, faint details, sitter identity, and age of portraits by da Vinci, Rembrandt, Rotari, and Titian.

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