A Supervised Learning Based Approach to Detect Crohn’s Disease in Abdominal MR Volumes View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2012

AUTHORS

Dwarikanath Mahapatra , Peter Schueffler , Jeroen A. W. Tielbeek , Joachim M. Buhmann , Franciscus M. Vos

ABSTRACT

Accurate diagnosis of Crohn’s disease (CD) has emerged as an important medical challenge. Because current Magnetic resonance imaging (MRI) analysis approaches rely on extensive manual segmentation for an accurate analysis, we propose a method for the automatic identification and localization of regions in abdominal MR volumes that have been affected by CD. Our proposed approach will serve to augment results from colonoscopy, the current reference standard for CD diagnosis. Intensity statistics, texture anisotropy and shape asymmetry of the 3D regions are used as features to distinguish between diseased and normal regions. Particular emphasis is laid on a novel entropy based asymmetry calculation method. Experiments on real patient data show that our features achieve a high level of accuracy and perform better than two competing methods. More... »

PAGES

97-106

References to SciGraph publications

  • 2011. Fast Multiple Organ Detection and Localization in Whole-Body MR Dixon Sequences in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2011
  • 2001-10. Random Forests in MACHINE LEARNING
  • 2011. Detection, Grading and Classification of Coronary Stenoses in Computed Tomography Angiography in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2011
  • 2011. Motion Correction and Parameter Estimation in dceMRI Sequences: Application to Colorectal Cancer in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2011
  • 2011. X-ray Categorization and Spatial Localization of Chest Pathologies in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2011
  • 2011. Focal Biologically Inspired Feature for Glaucoma Type Classification in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2011
  • 2011. Detecting and Classifying Linear Structures in Mammograms Using Random Forests in INFORMATION PROCESSING IN MEDICAL IMAGING
  • 2011. Biological Indexes Based Reflectional Asymmetry for Classifying Cutaneous Lesions in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2011
  • 2011. Entangled Decision Forests and Their Application for Semantic Segmentation of CT Images in INFORMATION PROCESSING IN MEDICAL IMAGING
  • 2011. Fully Automatic Segmentation of Brain Tumor Images Using Support Vector Machine Classification in Combination with Hierarchical Conditional Random Field Regularization in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2011
  • Book

    TITLE

    Abdominal Imaging. Computational and Clinical Applications

    ISBN

    978-3-642-33611-9
    978-3-642-33612-6

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-33612-6_11

    DOI

    http://dx.doi.org/10.1007/978-3-642-33612-6_11

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1047131421


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