Abstract
Many music works produced in the past are currently available only as original manuscripts or as photocopies. Preserving them entails their digitalization and consequent accessibility in a machine-readable format, which encourages browsing, retrieval, search and analysis while providing a generalized access to the digital material. Carrying this task manually is very time consuming and error prone. While optical music recognition (OMR) systems usually perform well on printed scores, the processing of handwritten music by computers remains below the expectations. One of the fundamental stages to carry out this task is the detection and subsequent removal of staff lines. In this paper we integrate a general-purpose, knowledge-free method for the automatic detection of staff lines based on stable paths, into a recently developed staff line removal toolkit. Lines affected by curvature, discontinuities, and inclination are robustly detected. We have also developed a staff removal algorithm adapting an existing line removal approach to use the stable path algorithm at the detection stage, Experimental results show that the proposed technique outperforms well-established algorithms. The developed algorithm will now be integrated in a web based system providing seamless access to browsing, retrieval, search and analysis of submitted scores.

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.

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Abstract
The breast cancer conservative treatment. cosmetic results (BCCT. core) is a new software tool created for the automatic and objective evaluation of the aesthetic result of BCCT. It makes use of a face-only photographic view of each patient and might thus have been considered insufficient for an accurate evaluation, as others have used multiple views of each patient. The purpose of this work is to compare the performance of the BCCT. core (using face-only views) with a subjective expert analysis using both the face-only and four-view assessment. Photographs in four-views of 150 patients, were evaluated by a panel of experts and a consensus classification was obtained. The agreement between the consensus and the BCCT. core (face-only view) was calculated using the kappa (k) and weighted kappa (wk) statistics. Face-only views, of the same 150 patients, were subsequently sorted out in a different order and sent for individual evaluation by three specialists from the previous panel of experts. The individual agreement between the face-only view and the four-view evaluation by each of the three experts and the consensus was calculated using the same methods. Obtained results were compared to the BCCT. core performance. The software obtained a moderate agreement with the consensus (k = 0.57; wk = 0.68). The highest value of agreement, from the three experts, between the four-view evaluation and the consensus was identical to the software agreement (k = 0.55; wk = 0.67). In the face-only view experiment, the highest value of agreement between the experts and the consensus was only fair (k = 0.37; wk = 0.54). Performance of the software was thus considered equal to that obtained by experts using a four-view evaluation.

Abstract
Breast cancer conservative treatment (BCCT), due to its proven oncological safety, is considered, when feasible, the gold standard of breast cancer treatment. However, aesthetic results are heterogeneous and difficult to evaluate in a standardized way, due to the lack of reproducibility of the subjective methods usually applied. The objective assessment methods, considered in the past as being less capable of evaluating all aspects of BCCT, are nowadays being preferred to overcome the drawbacks of the subjective evaluation. A computer-aided medical system was recently developed to objectively and automatically evaluate the aesthetic result of BCCT. In this system, the detection of the breast contour on the patient's digital photograph is a necessary step to extract the features subsequently used in the evaluation process. In this paper an algorithm based on the shortest path oil a graph is proposed to detect automatically the breast contour. The proposed method extends an existing semi-automatic algorithm for the same purpose. A comprehensive comparison with manually-drawn contours reveals the strength of the proposed method.