Tissues Classification of the Cardiovascular System Using Texture Descriptors View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017

AUTHORS

Claudia Mazo , Enrique Alegre , Maria Trujillo , Víctor González-Castro

ABSTRACT

In this paper, we present an approach to automatically classify tissues of the cardiovascular system using texture information. Additionally, this process makes possible to identify some cardiovascular organs, since some tissues belong to muscles associated to those, i.e. identifying the tissue makes possible to identify the organ. We have assessed rotation invariant Local Binary Patterns (LBPri) and Haralick features to describe the content of histological images. We also assessed Random Forest (RF) and Linear Discriminant Analysis (LDA) for the classification of these descriptors. The tissues were classified into four classes: (i) cardiac muscle of the heart, (ii) smooth muscle of the elastic artery, (iii) loose connective tissue, and (iv) smooth muscle of the large vein and the elastic artery. The experimental validation is conducted with a set of 2400 blocks of \(100\times 100\) pixels each. The classifier was assessed using a 10-fold cross-validation. The best AUCs (0.9875, 0.9994 and 0.9711 for the cardiac muscle of the heart, the smooth muscle of muscular artery, the smooth muscle of the large vein and the elastic artery classes, respectively) are achieved by LBPri and RF. More... »

PAGES

123-132

References to SciGraph publications

  • 2012. An Automatic Segmentation Approach of Epithelial Cells Nuclei in PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS
  • 2013. Identifying Loose Connective and Muscle Tissues on Histology Images in PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS
  • 2013-12. Evaluation of noise robustness for local binary pattern descriptors in texture classification in EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING
  • 2014. Automatic Classification of Coating Epithelial Tissue in ADVANCED INFORMATION SYSTEMS ENGINEERING
  • 2013-09. Automated classification of breast cancer morphology in histopathological images in DIAGNOSTIC PATHOLOGY
  • Book

    TITLE

    Medical Image Understanding and Analysis

    ISBN

    978-3-319-60963-8
    978-3-319-60964-5

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-60964-5_11

    DOI

    http://dx.doi.org/10.1007/978-3-319-60964-5_11

    DIMENSIONS

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