Jaime S. Cardoso

Toward a generic evaluation of image segmentation

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.

Optical music recognition: state-of-the-art and open issues

For centuries, music has been shared and remembered by two traditions: aural transmission and in the form of written documents normally called musical scores. Many of these scores exist in the form of unpublished manuscripts and hence they are in danger of being lost through the normal ravages of time. To preserve the music some form of typesetting or, ideally, a computer system that can automatically decode the symbolic images and create new scores is required. Programs analogous to optical character recognition systems called optical music recognition (OMR) systems have been under intensive development for many years. However, the results to date are far from ideal. Each of the proposed methods emphasizes different properties and therefore makes it difficult to effectively evaluate its competitive advantages. This article provides an overview of the literature concerning the automatic analysis of images of printed and handwritten musical scores. For self-containment and for the benefit of the reader, an introduction to OMR processing systems precedes the literature overview. The following study presents a reference scheme for any researcher wanting to compare new OMR algorithms against well-known ones.

INbreast: toward a full-field digital mammographic database.

RATIONALE AND OBJECTIVES Computer-aided detection and diagnosis (CAD) systems have been developed in the past two decades to assist radiologists in the detection and diagnosis of lesions seen on breast imaging exams, thus providing a second opinion. Mammographic databases play an important role in the development of algorithms aiming at the detection and diagnosis of mammary lesions. However, available databases often do not take into consideration all the requirements needed for research and study purposes. This article aims to present and detail a new mammographic database. MATERIALS AND METHODS Images were acquired at a breast center located in a university hospital (Centro Hospitalar de S. João [CHSJ], Breast Centre, Porto) with the permission of the Portuguese National Committee of Data Protection and Hospitals Ethics Committee. MammoNovation Siemens full-field digital mammography, with a solid-state detector of amorphous selenium was used. RESULTS The new database-INbreast-has a total of 115 cases (410 images) from which 90 cases are from women with both breasts affected (four images per case) and 25 cases are from mastectomy patients (two images per case). Several types of lesions (masses, calcifications, asymmetries, and distortions) were included. Accurate contours made by specialists are also provided in XML format. CONCLUSION The strengths of the actually presented database-INbreast-relies on the fact that it was built with full-field digital mammograms (in opposition to digitized mammograms), it presents a wide variability of cases, and is made publicly available together with precise annotations. We believe that this database can be a reference for future works centered or related to breast cancer imaging.

Towards an intelligent medical system for the aesthetic evaluation of breast cancer conservative treatment

OBJECTIVE This work presents a novel approach for the automated prediction of the aesthetic result of breast cancer conservative treatment (BCCT). Cosmetic assessment plays a major role in the study of BCCT. Objective assessment methods are being preferred to overcome the drawbacks of subjective evaluation. METHODOLOGY The problem is addressed as a pattern recognition task. A dataset of images of patients was classified in four classes (excellent, good, fair, poor) by a panel of international experts, providing a gold standard classification. As possible types of objective features we considered those already identified by domain experts as relevant to the aesthetic evaluation of the surgical procedure, namely those assessing breast asymmetry, skin colour difference and scar visibility. A classifier based on support vector machines was developed from objective features extracted from the reference dataset. RESULTS A correct classification rate of about 70% was obtained when categorizing a set of unseen images into the aforementioned four classes. This accuracy is comparable with the result of the best evaluator from the panel of experts. CONCLUSION The results obtained are rather encouraging and the developed tool could be very helpful in assuring objective assessment of the aesthetic outcome of BCCT.

Optical recognition of music symbols: A comparative study

Many musical works produced in the past are still currently available only as original manuscripts or as photocopies. The preservation of these works requires their digitalization and transformation into a machine-readable format. However, and despite the many research activities on optical music recognition (OMR), the results for handwritten musical scores are far from ideal. Each of the proposed methods lays the emphasis on different properties and therefore makes it difficult to evaluate the efficiency of a proposed method. We present in this article a comparative study of several recognition algorithms of music symbols. After a review of the most common procedures used in this context, their respective performances are compared using both real and synthetic scores. The database of scores was augmented with replicas of the existing patterns, transformed according to an elastic deformation technique. Such transformations aim to introduce invariances in the prediction with respect to the known variability in the symbols, particularly relevant on handwritten works. The following study and the adopted databases can constitute a reference scheme for any researcher who wants to confront a new OMR algorithm face to well-known ones.

Factors Determining Esthetic Outcome after Breast Cancer Conservative Treatment

Abstract:  The aim of this study was to evaluate the factors that determine esthetic outcome after breast cancer conservative treatment, based on a consensual classification obtained with an international consensus panel. Photographs were taken from 120 women submitted to conservative unilateral breast cancer surgery (with or without axillary surgery) and radiotherapy. The images were sent to a panel of observers from 13 different countries and consensus on the classification of esthetic result (recorded as excellent, good, fair or poor) was obtained in 113 cases by means of a Delphi method. For each patient, data were collected retrospectively regarding patient characteristics, tumor, and treatment factors. Univariate and multivariate analysis were used to evaluate the correlation between these factors and overall cosmetic results. On univariate analysis, younger and thinner patients as well as patients with lower body mass index (BMI) and premenopausal status obtained better cosmetic results. In the group of tumor‐ and treatment‐related factors, larger removed specimens, clearly visible scars, the use of chemotherapy and longer follow‐up period were associated with less satisfactory results. On multivariate analysis, only BMI and scar visibility maintained a significant association with cosmesis. BMI and scar visibility are the only factors significantly associated with cosmetic results of breast cancer conservative treatment, as evaluated by an international consensus panel.

Recommendations for the aesthetic evaluation of breast cancer conservative treatment

During the Turning Subjective Into Objective seminar held in Lisbon in May 2011, experts in the topic gathered to discuss the unsolved problems of aesthetic evaluation of breast-conserving treatment (BCT). The purpose of this study is to review the main methodological issues related to the aesthetic evaluation of BCT, to discuss currently used methods of evaluation and the lack of a gold standard, and to write a set of recommendations that can be used as guidance for the aesthetic evaluation of BCT.

Diagnostic of pathology on the vertebral column with embedded reject option

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.

2005 Special issue: Modelling ordinal relations with SVMs: An application to objective aesthetic evaluation of breast cancer conservative treatment

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.

The unimodal model for the classification of ordinal data

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.