Abstract
Many real life problems require the classification of items in naturally ordered classes. These problems are traditionally handled by conventional methods for nominal classes, ignoring the order. This paper introduces a new training model for feedforward neural networks, for multiclass classification problems, where the classes are ordered. The proposed model has just one output unit which takes values in the interval [0,1]; this interval is then subdivided into K subintervals (one for each class), according to a specific probabilistic model. A comparison is made with conventional approaches, as well as with other architectures specific for ordinal data proposed in the literature. The new model compares favourably with the other methods under study, in the synthetic dataset used for evaluation.

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
Cosmetic assessment or conservative breast cancer treatment plays a major role in the study of breast cancer techniques. Objective assessment methods are being preferred to overcome the drawbacks of subjective evaluation. In this paper a methodology for the objective assessment of conservative breast cancer treatment is proposed. The quantitative measures used in this research provide an objective way to calculate the overall cosmetic result. We report experiments using support vector machines to derive an optimal assessment rule. The results seem to indicate that it is possible to construct an algorithm for a complete objective classification of the aesthetic result of breast conservative treatment.

Abstract
Purpose: The subjective evaluation of aesthetic results in conservative breast cancer treatment has largely been used without questioning the observer's skills. The aim of this study was to evaluate interobserver agreement of the aesthetic results of breast cancer conservative treatment in three groups of observers with different levels of experience. Methods and Materials: Photographs were taken of 55 women who had undergone conservative unilateral breast cancer treatment and 5 control women with no breast disease. The images were then distributed to 13 observers who were divided into three groups according to their experience in breast cancer treatment: experienced, medium experienced, and inexperienced. They were first asked to distinguish the patients from the controls and for the patients to identify the operated side. Subsequently, they were asked to classify the aesthetic result as excellent, good, fair, or poor. The accuracy in identifying controls, patients, and side of treatment was calculated individually for all observers. The interobserver agreement for the aesthetic result was calculated using observed agreement and multiple K statistic (K) in each of the three groups. Results: Inexperienced observers performed significantly worse than experienced observers in identifying controls, patients, and the side of treatment. Agreement of the aesthetic result was significantly greater in the group of experienced observers (kappa = 0.59) than in the medium experienced (kappa = 0.35) and inexperienced (kappa= 0.33) observers. Conclusion: Previous experience in breast cancer conservative treatment should be considered a prerequisite for the evaluation of the aesthetic results. (C) 2005 Elsevier Inc.

Abstract
The implementation of fast dedicated processor for block matching motion estimation based on cumulants matching criteria implies the optimization of all of its components. Special care should be spent with the multiply-accumulate unit that is the core of many digital signal processing systems. Therefore, its optimization may be of outmost importance, specially if a significative number of such units are present in the platform. In this paper, the minimization of the size of one such unit is provided for a specific application, although the results have relevance in other scenarios.

Abstract
Image segmentation plays a major role in a broad range of applications. Evaluating the adequacy of a segmentation algorithm for a given application is a requisite both to allow the appropriate selection of segmentation algorithms as well as to tune their parameters for optimal performance. However, objective segmentation quality evaluation is far from being a solved problem. In this paper, a generic framework for segmentation evaluation is introduced after a brief review of previous work. A metric based on the distance between segmentation partitions is proposed to overcome some of the limitations of existing approaches. Symmetric and asymmetric distance metric alternatives are presented to meet the specificities of a wide class of applications. Experimental results confirm the potential of the proposed measures.