Jesús Angulo

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MOTIVATION DNA microarrays are an experimental technology which consists in arrays of thousands of discrete DNA sequences that are printed on glass microscope slides. Image analysis is an important aspect of microarray experiments. The aim of this step is to reduce an image of spots into a table with a measure of the intensity for each spot. Efficient,(More)
A novel approach for vector ordering is introduced in this paper. The generic framework is based on a supervised learning formulation which leads to reduced orderings. A training set for the background and another training set for the foreground are needed as well as a supervised method to construct the ordering mapping. Two particular cases of learning(More)
The algorithm proposed in this paper allows to automatically segment the optic disc from a fundus image. The goal is to facilitate the early detection of certain pathologies and to fully automate the process so as to avoid specialist intervention. The method proposed for the extraction of the optic disc contour is mainly based on mathematical morphology(More)
The paper presents a technique to automatically detect the working area of peripheral blood smears stained with May-Grünwuald Giemsa. The optimal area is defined as the well spread part of the smear. This zone starts when the erythrocytes stop overlapping (on the body film side) and finishes when the erythrocytes start losing their clear central zone (on(More)
This paper introduces a watershed-based stochastic segmentation methodology. The approach is based on using M realizations of N random markers to build a probability density function (pdf) of contours which is then segmented by volumic watershed for de ning the R most signi cant regions. It over-performs the standard watershed algorithms when the aim is to(More)
The paper presents a new segmentation and classi cation scheme to analyze hyperspectral (HS) data. The Robust Color Morphological Gradient of the HS image is computed, and the watershed transformation is applied to the obtained gradient. After the pixel-wise Support Vector Machines classi cation, the majority voting within the watershed regions is(More)