Development of retinal blood vessel segmentation methodology using wavelet transforms for assessment of diabetic retinopathy

Automated image processing has the potential to assist in the early detection of diabetes, by detecting changes in blood vessel diameter and patterns in the retina. This paper describes the development of segmentation methodology in the processing of retinal blood vessel images obtained using non-mydriatic colour photography. The methods used include wavelet analysis, supervised classifier probabilities and adaptive threshold procedures, as well as morphology-based techniques. We show highly accurate identification of blood vessels for the purpose of studying changes in the vessel network that can be utilized for detecting blood vessel diameter changes associated with the pathophysiology of diabetes. In conjunction with suitable feature extraction and automated classification methods, our segmentation method could form the basis of a quick and accurate test for diabetic retinopathy, which would have huge benefits in terms of improved access to screening people for risk or presence of diabetes

Using basic image features for texture classification

Representing texture images statistically as histograms over a discrete vocabulary of local features has proven widely effective for texture classification tasks. Images are described locally by vectors of, for example, responses to some filter bank; and a visual vocabulary is defined as a partition of this descriptor-response space, typically based on clustering. In this paper, we investigate the performance of an approach which represents textures as histograms over a visual vocabulary which is defined geometrically, based on the Basic Image Features of Griffin and Lillholm (Proc. SPIE 6492(09):1-11, 2007), rather than by clustering. BIFs provide a natural mathematical quantisation of a filter-response space into qualitatively distinct types of local image structure. We also extend our approach to deal with intra-class variations in scale. Our algorithm is simple: there is no need for a pre-training step to learn a visual dictionary, as in methods based on clustering, and no tuning of parameters is required to deal with different datasets. We have tested our implementation on three popular and challenging texture datasets and find that it produces consistently good classification results on each, including what we believe to be the best reported for the KTH-TIPS and equal best reported for the UIUCTex databases

Making a national atlas of population by computer

This paper describes the conceptual and practical problems encountered and solved in producing a multi-colour atlas of population characteristics in Great Britain. The atlas itself is in A4 format; it consists of some thirty-four maps of Great Britain in four colours and the same number of regional maps, together with descriptive text. All maps were plotted on a laser plotter with a resolution of 127 microns. The paper describes how mapping of ratios, such as percentages, was found to be highly misleading and describes the novel probability mapping solution adopted, based on the signed chi-square statistic. In addition, the rationale for selecting the class intervals and for selecting colour schemes is described

Atomic-scale modeling of the deformation of nanocrystalline metals

Nanocrystalline metals, i.e. metals with grain sizes from 5 to 50 nm, display technologically interesting properties, such as dramatically increased hardness, increasing with decreasing grain size. Due to the small grain size, direct atomic-scale simulations of plastic deformation of these materials are possible, as such a polycrystalline system can be modeled with the computational resources available today. We present molecular dynamics simulations of nanocrystalline copper with grain sizes up to 13 nm. Two different deformation mechanisms are active, one is deformation through the motion of dislocations, the other is sliding in the grain boundaries. At the grain sizes studied here the latter dominates, leading to a softening as the grain size is reduced. This implies that there is an ``optimal'' grain size, where the hardness is maximal. Since the grain boundaries participate actively in the deformation, it is interesting to study the effects of introducing impurity atoms in the grain boundaries. We study how silver atoms in the grain boundaries influence the mechanical properties of nanocrystalline copper.Comment: 10 pages, LaTeX2e, PS figures and sty files included. To appear in Mater. Res. Soc. Symp. Proc. vol 538 (invited paper). For related papers, see http://www.fysik.dtu.dk/~schiotz/publist.htm

Scale Space Smoothing, Image Feature Extraction and Bessel Filters

The Green function of Mumford-Shah functional in the absence of discontinuities is known to be a modified Bessel function of the second kind and zero degree. Such a Bessel function is regularized here and used as a filter for feature extraction. It is demonstrated in this paper that a Bessel filter does not follow the scale space smoothing property of bounded linear filters such as Gaussian filters. The features extracted by the Bessel filter are therefore scale invariant. Edges, blobs, and junctions are features considered here to show that the extracted features remain unchanged by varying the scale of a Bessel filter. The scale invariance property of Bessel filters for edges is analytically proved here. We conjecture that Bessel filters also enjoy this scale invariance property for other kinds of features. The experimental results presente