The estimation of edge features, such as sub-pixel position, orientation, curvature and change in intensity at both sides of the edge, from the computation of the gradient vector in each pixel is usually inexact, even in ideal images. In this paper, we present a new edge detector based on an edge and acquisition model derived from the partial area effect, which does not assume continuity in the image values. The main goal of this method consists in achieving a highly accurate extraction of the position, orientation, curvature and contrast of the edges, even in difficult conditions, such as noisy images, blurred edges, low contrast areas or very close contours. For this purpose, we first analyze the influence of perfectly straight or circular edges in the surrounding region, in such a way that, when these conditions are fulfilled, the features can exactly be determined. Afterward, we extend it to more realistic situations considering how adverse conditions can be tackled and presenting an iterative scheme for improving the results. We have tested this method in real as well as in sets of synthetic images with extremely difficult edges, and in both cases a highly accurate characterization has been achieved.

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Abstract. This paper presents a new approach in the application of computer vision techniques to the diagnosis of solid breast tumors on ultrasound images. Most works related to medical image analysis for breast cancer detection refer to mammography. However, radiologists have proved the significance of some aspects observed on ultrasound images, among which are spiculation, calcifications, ellipsoid shape, dimensions, echogenicity, capsule, angular margins, lobulations, shadowing and ramifications. We have developed a common framework for the analysis of these criteria, so that a series of parameters are available for the physicians to decide whether the biopsy is necessary or not. We present a set of mathematical methods to extract objective evidence of the presence or absence of the diagnostic criteria. This system is able to extract the relevant features for solid breast nodules with high accuracy and represents a very valuable help in the assessment of radiologists.

Abstract.The aortic dissection is a disease that can cause a deadly situation, even with a correct treatment. It consists in a rupture of a layer of the aortic artery wall, causing a blood flow inside this rupture, called dissection. The aim of this paper is to contribute to its diagnosis, detecting the dissection edges inside the aorta. A subpixel accuracy edge detector based on the hypothesis of partial volume effect is used, where the intensity of an edge pixel is the sum of the contribution of each color weighted by its relative area inside the pixel. The method uses a floating window centred on the edge pixel and computes the edge features. The accuracy of our method is evaluated on synthetic images of different thickness and noise levels, obtaining an edge detection with a maximal mean error lower than 16 percent of a pixel.

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