Noelia Vállez

Publications (8) View all

• Article: A parallel solution for high resolution histological image analysis.

  G Bueno, R González, O Déniz, M García-Rojo, J González-García, M M Fernández-Carrobles, N Vállez, J Salido
  [show abstract] [hide abstract]
  ABSTRACT: This paper describes a general methodology for developing parallel image processing algorithms based on message passing for high resolution images (on the order of several Gigabytes). These algorithms have been applied to histological images and must be executed on massively parallel processing architectures. Advances in new technologies for complete slide digitalization in pathology have been combined with developments in biomedical informatics. However, the efficient use of these digital slide systems is still a challenge. The image processing that these slides are subject to is still limited both in terms of data processed and processing methods. The work presented here focuses on the need to design and develop parallel image processing tools capable of obtaining and analyzing the entire gamut of information included in digital slides. Tools have been developed to assist pathologists in image analysis and diagnosis, and they cover low and high-level image processing methods applied to histological images. Code portability, reusability and scalability have been tested by using the following parallel computing architectures: distributed memory with massive parallel processors and two networks, INFINIBAND and Myrinet, composed of 17 and 1024 nodes respectively. The parallel framework proposed is flexible, high performance solution and it shows that the efficient processing of digital microscopic images is possible and may offer important benefits to pathology laboratories.
  Computer methods and programs in biomedicine 04/2012; 108(1):388-401. · 1.14 Impact Factor
• Article: Emerging trends: grid technology in pathology.

  Gloria Bueno, Marcial García-Rojo, Oscar Déniz, María Del Milagro Fernández-Carrobles, Noelia Vállez, Jesús Salido, Jesús García-González
  [show abstract] [hide abstract]
  ABSTRACT: Grid technology has enabled clustering and access to, and interaction among, a wide variety of geographically distributed resources such as supercomputers, storage systems, data sources, instruments as well as special devices and services, realizing network-centric operations. Their main applications include large scale computational and data intensive problems in science and engineering. Grids are likely to have a deep impact on health related applications. Moreover, they seem to be suitable for tissue-based diagnosis. They offer a powerful tool to deal with current challenges in many biomedical domains involving complex anatomical and physiological modeling of structures from images or large image databases assembling and analysis. This chapter analyzes the general structures and functions of a Grid environment implemented for tissue-based diagnosis on digital images. Moreover, it presents a Grid middleware implemented by the authors for diagnostic pathology applications. The chapter is a review of the work done as part of the European COST project EUROTELEPATH.
  Studies in health technology and informatics 01/2012; 179:218-29.
• Chapter: A Tree Classifier for Automatic Breast Tissue Classification Based on BIRADS Categories

  Noelia Vállez, Gloria Bueno, Oscar Déniz-Suárez, José A. Seone, Julián Dorado, Alejandro Pazos
  [show abstract] [hide abstract]
  ABSTRACT: Breast tissue density is an important risk factor in the detection of breast cancer. It is also known that interpretation of mammogram lesions is more difficult in dense tissues. Therefore, getting a preliminary tissue classification may aid in the subsequent process of breast lesion detection and analysis. This article reviews several classification techniques for two datasets, both digitized screen-film (SFM) and full-field digital (FFDM) mammography, classified according to BIRADS categories. It concludes with a tree classification procedure based on the combination of two classifiers on texture features. Statistical analysis to test the normality and homoscedasticity of the features was carried. Thus, just features that are significant influenced by the tissue type were considered. The results obtained on 322 mammograms of the SFM dataset and on 1137 mammograms of the FFDM dataset demonstrate that up to 80% of samples were correctly classified using using 10-fold cross-validation to train and test the classifiers.
  06/2011: pages 580-587;
• Source

  Available from: Noelia Vállez

  Conference Proceeding: A Tree Classifier for Automatic Breast Tissue Classification Based on BIRADS Categories

  Noelia Vállez, Gloria Bueno, Oscar Déniz-Suárez, José A. Seoane, Julian Dorado, Alejandro Pazos
  [show abstract] [hide abstract]
  ABSTRACT: Breast tissue density is an important risk factor in the detection of breast cancer. It is also known that interpretation of mammogram lesions is more difficult in dense tissues. Therefore, getting a preliminary tissue classification may aid in the subsequent process of breast lesion detection and analysis. This article reviews several classification techniques for two datasets, both digitized screen-film (SFM) and full-field digital (FFDM) mammography, classified according to BIRADS categories. It concludes with a tree classification procedure based on the combination of two classifiers on texture features. Statistical analysis to test the normality and homoscedasticity of the features was carried. Thus, just features that are significant influenced by the tissue type were considered. The results obtained on 322 mammograms of the SFM dataset and on 1137 mammograms of the FFDM dataset demonstrate that up to 80% of samples were correctly classified using using 10-fold cross-validation to train and test the classifiers.
  IbPRIA; 01/2011
• Conference Proceeding: Automated System for Microscopic Image Acquisition and Analysis

  J. Vidal, O. Déniz, J. Salido, N. Vállez, Milagros M. Fernández, G. Bueno
  Advances in Biomedical Informatics: COMBIOMED; 01/2011