Abstract
The optical recognition of handwritten musical scores by computers remains far from ideal. Most OMR algorithms rely on an estimation of the staffline thickness and the vertical line distance within the same staff. Subsequent operation can use these values as references, dismissing the need for some predetermined threshold values. In this work we improve on previous conventional estimates for these two reference lengths. We start by proposing a new method for binarized music scores and then extend the approach for gray-level music scores. An experimental study with 50 images is used to assess the interest of the novel method. © 2010 IEEE.

Abstract
The restoration and recovery of a defective skull can be performed through operative techniques to implant a customized prosthesis. Recently, image processing and surface reconstruction methods have been used for digital prosthesis design. In this paper we present a framework for prosthesis modeling. Firstly, we take the computed tomography (CT) of the skull and perform bone segmentation by thresholding. The obtained binary volume is processed by morphological operators, frame-by-frame, to get the inner and outer boundaries of the bone. These curves are used to initialize a 2D deformable model that generates the prosthesis boundary in each CT frame. In this way, we can fill the prosthesis volume which is the input for a marching cubes technique that computes the digital model of the target geometry. In the experimental results we demonstrate the potential of our technique and compare it with a related one.

Abstract
The cosmetic result is an important endpoint for breast cancer conservative treatment (BCCT), but the verification of this outcome remains without a standard. Objective assessment methods are preferred to overcome the drawbacks of subjective evaluation. In this paper a novel algorithm is proposed, based on support vector machines, for the classification of ordinal categorical data. This classifier is then applied as a new methodology for the objective assessment of the aesthetic result of BCCT. Based on the new classifier, a semi-objective score for quantification of the aesthetic results of BCCT was developed, allowing the discrimination of patients into four classes.

Abstract
Classification of ordinal data is one of the most important tasks of relation learning. This paper introduces a new machine learning paradigm specifically intended for classification problems where the classes have a natural order. The technique reduces the problem of classifying ordered classes to the standard two-class problem. The introduced method is then mapped into support vector machines and neural networks. Generalization bounds of the proposed ordinal classifier are also provided. An experimental study with artificial and real data sets, including an application to gene expression analysis, verifies the usefulness of the proposed approach.

Abstract

Abstract
The automatic detection and segmentation of the pectoral muscle in the medio-lateral oblique view of mammograms is essential for further analysis of breast anormalies. However, it is still a very difficult task since the sizes, shapes and intensity contrasts of pectoral muscles change greatly from image to image. In this paper, an algorithm based on the shortest path on a graph is proposed to automatically detect the pectoral muscle contour. To overcome the difficulties of searching for the path between a lateral and the top margins of the image, this is first transformed, using polar coordinates. In the transformed image, the muscle boundary in amongst the shortest paths between the top and the bottom rows. A comprehensive comparison with manually-drawn contours reveals the strength of the proposed method.