Alberto Rey

University of A Coruña, La Corogne, Galicia, Spain

Are you Alberto Rey?

Claim your profile

Publications (5)0 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: The use of medical imaging for the diagnosis and, to a lesser extent, the prognosis and treatment of disease, is a common practice in modern medicine. Consequently, the need has arisen to develop applications that combine the ability to visualize digital medical images with the features required by clinical personnel in order to manage them. A number of medical image viewers are currently available, but nearly all of them are oriented towards visualizing and managing a single study of a patient, which limits the analysis of the expert. This paper introduces a novel application that contains the basic functionality required for common medical image analysis and which may be extended by a plug-in system with new features that could be demanded in the future. The application also makes it possible to visualize and analyze several studies at the same time, completely independently, increasing the accuracy of the analysis and facilitating the work of experts.
    Proceedings of the 4th international conference on Ambient Assisted Living and Home Care; 12/2012
  • A. Rey · B. Arcay · A. Castro ·
    [Show abstract] [Hide abstract]
    ABSTRACT: The detection of pulmonary nodules is one of the most studied areas and challenging task in the field of medical image analysis, due the current relevance of the lung carcinoma. The difficulty and complexity of this task has led to the development of CAD systems for the automated detection of lung nodules in CT scans, which provides valuable assistance for radiologists and could improve the detection rate. A common phase of these systems is the detection of regions of interest (ROIs) that could be marked as nodules, in order to reduce the searching space problem. In this paper, we evaluate and compare the combination of various approaches of supervised vector machines (SVMs) with different kinds of fuzzy clustering algorithms, so as to improve the detection and segmentation of ROIs that could represent lung nodules in high resolution CT scans. These images are provided by the LIDC database (Lung Internet Database Consortium).
    Fuzzy Systems (FUZZ), 2011 IEEE International Conference on; 07/2011
  • [Show abstract] [Hide abstract]
    ABSTRACT: The detection of pulmonary nodules in CT images has been extensively researched because it is a highly complicated and socially interesting matter. The classical approach consists in the development of a computer-aided diagnosis (CAD) system that indicates, in phases, the presence or absence of nodules. A common phase of these systems is the detection of regions of interest (ROIs), that may correspond to nodules, in order to reduce the searching space. This paper evaluates the use of various neural networks for the defuzzification of the output of fuzzy clustering algorithms, in order to improve the detection of true positives and the reduction of false positives. Also, they are compared to the results from a support vector machine (SVM).
    Advances in Computational Intelligence - 11th International Work-Conference on Artificial Neural Networks, IWANN 2011, Torremolinos-Málaga, Spain, June 8-10, 2011, Proceedings, Part I; 01/2011
  • A. Rey · A. Castro · B. Arcay ·
    [Show abstract] [Hide abstract]
    ABSTRACT: The complexity of detecting pulmonary nodules has led to the development of Computer Aided Systems (CAD) that automate and reduce the cost of this task. The first phase of such systems usually consists in preprocessing the Computer Tomography (CT) scans, with the aim of segmenting the lungs and eliminating the elements that might interfere with the process. This paper presents an automatic method for the segmentation of lungs into three-dimensional pulmonary high resolution CT images. The proposed method has three main steps, that combine both 3D and 2D techniques. Firstly the trachea and the main airways are removed from the volume; then the lung region is segmented by grey-level thresholding, separating the right and left lungs if a junction is visible in the image, and the lung contour is smoothed; finally, a ”region growing” is applied using two seeds from each identified lung, avoiding as such the incorporation of other elements that do not belong to the lungs.
    Biomedical Engineering, 2011 10th International Workshop on; 01/2011
  • [Show abstract] [Hide abstract]
    ABSTRACT: The detection of pulmonary nodules in radiological or CT images has been widely investigated in the field of medical image analysis due to the high degree of difficulty it presents. The traditional approach is to develop a multistage CAD system that will reveal the presence or absence of nodules to the radiologist. One of the stages within this system is the detection of ROIs (regions of interest) that may possibly be nodules, in order to reduce the scope of the problem. In this article we evaluate clustering algorithms that use different classification strategies for this purpose. In order to evaluate these algorithms we used high resolution CT images from the LIDC (Lung Internet Database Consortium) database.
    Computer Vision and Graphics - International Conference, ICCVG 2010, Warsaw, Poland, September 20-22, 2010, Proceedings, Part I; 01/2010