Abstract
Automatic pectoral muscle removal on mediolateral oblique view of mammogram is an essential step for many mammographic processing algorithms. However, the wide variability in the position of the muscle contour, together with the similarity between in muscle and breast tissues makes the detection a difficult task. In this paper, we propose a two step procedure to detect the muscle contour. In a first step, the endpoints of the contour are predicted with a pair of support vector regression models; one model is trained to predict the intersection point of the contour with the top row while the other is designed for the prediction of the endpoint of the contour on the left column. Next, the muscle contour is computed as the shortest path between the two endpoints. A comprehensive comparison with manually-drawn contours reveals the strength of the proposed method.

Abstract
Many real life problems require the classification of items into naturally ordered classes. These problems are traditionally handled by conventional methods intended for the classification of nominal classes where the order relation is ignored. This paper introduces a new machine learning paradigm intended for multi-class classification problems where the classes are ordered. The theoretical development of this paradigm is carried out under the key idea that the random variable class associated with a given query should follow a unimodal distribution. In this context, two approaches are considered: a parametric, where the random variable class is assumed to follow a specific discrete distribution; a nonparametric, where the random variable class is assumed to be distribution-free. In either case, the unimodal model can be implemented in practice by means of feedforward neural networks and support vector machines, for instance. Nevertheless, our main focus is on feedforward neural networks. We also introduce a new coefficient, r(int), to measure the performance of ordinal data classifiers. An experimental study with artificial and real datasets is presented in order to illustrate the performances of both parametric and nonparametric approaches and compare them with the performances of other methods. The superiority of the parametric approach is suggested, namely when flexible discrete distributions, a new concept introduced here, are considered.

Abstract
Computer aided diagnosis systems with the capability of automatically decide if a patient has or not a pathology and to hold the decision on the dificult cases, are becoming more frequent. The latter are afterwards reviewed by an expert reducing therefore time consuption on behalf of the expert. The number of cases to review depends on the cost of erring the diagnosis. In this work we analyse the incorporation of the option to hold a decision on the diagnostic of pathologies on the vertebral column. A comparison with several state of the art techniques is performed. We conclude by showing that the use of the reject option techniques is an asset in line with the current view of the research community.

Abstract
Image binarization is a common operation in the preprocessing stage in most Optical Music Recognition (OMR) systems. The choice of an appropriate binarization method for handwritten music scores is a difficult problem. Several works have already evaluated the performance of existing binarization processes in diverse applications. However, no goal-directed studies for music sheets documents were carried out. This paper presents a novel binarization method based in the content knowledge of the image. The method only needs the estimation of the staffline thickness and the vertical distance between two stafflines. This information is extracted directly from the gray level music score. The proposed binarization procedure is experimentally compared with several state of the art methods.

Abstract
Ordinal data classification (ODC) has a wide range of applications in areas where human evaluation plays an important role, ranging from psychology and medicine to information retrieval. In ODC the output variable has a natural order, however, there is not a precise notion of the distance between classes. The recently proposed method for ordinal data, Kernel Discriminant Learning Ordinal Regression (KDLOR), is based on Linear Discriminant Analysis (LDA), a simple tool for classification. KDLOR brings LDA to the forefront in the ODC field, motivating further research. This paper compares three LDA based algorithms for ODC. The first method uses the generic framework of Frank and Hall for ODC instantiated with a kernel version of LDA. Similarly, the second method is based on the also generic Data Replication framework for ODC instantiated with the same kernel version of LDA. Both the Frank and Hall and Data Replication methods address the ODC problem by the use of a base binary classifier. Finally, the third method under comparison is KDLOR. The experiments are carried out on synthetic and real datasets. A comparison between the performances of the three systems is made based on t-statistics. The performance and running time complexity of the methods do not support any advantage of KDLOR over the other two methods. © 2012 IEEE.

Abstract
While ordinal classification problems are common in many situations, induction of ordinal decision trees has not evolved significantly. Conventional trees for regression settings or nominal classification are commonly induced for ordinal classification problems. On the other hand a decision tree consistent with the ordinal setting is often desirable to aid decision making in such situations as credit rating. In this work we extend a recently proposed strategy based on constraints defined globally over the feature space. We propose a bootstrap technique to improve the accuracy of the baseline solution. Experiments in synthetic and real data show the benefits of our proposal. © 2011 IEEE.