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Automated diagnosis of celiac disease by video capsule endoscopy using DAISY Descriptors

  • Jahmunah Vicnesh
  • Joel Koh En Wei
  • Edward J. Ciaccio
  • Shu Lih Oh
  • Govind Bhagat
  • Suzanne K. Lewis
  • Peter H. Green
  • U. Rajendra AcharyaEmail author
Image & Signal Processing
  • 115 Downloads
Part of the following topical collections:
  1. Image & Signal Processing

Abstract

Celiac disease is a genetically determined disorder of the small intestine, occurring due to an immune response to ingested gluten-containing food. The resulting damage to the small intestinal mucosa hampers nutrient absorption, and is characterized by diarrhea, abdominal pain, and a variety of extra-intestinal manifestations. Invasive and costly methods such as endoscopic biopsy are currently used to diagnose celiac disease. Detection of the disease by histopathologic analysis of biopsies can be challenging due to suboptimal sampling. Video capsule images were obtained from celiac patients and controls for comparison and classification. This study exploits the use of DAISY descriptors to project two-dimensional images onto one-dimensional vectors. Shannon entropy is then used to extract features, after which a particle swarm optimization algorithm coupled with normalization is employed to select the 30 best features for classification. Statistical measures of this paradigm were tabulated. The accuracy, positive predictive value, sensitivity and specificity obtained in distinguishing celiac versus control video capsule images were 89.82%, 89.17%, 94.35% and 83.20% respectively, using the 10-fold cross-validation technique. When employing manual methods rather than the automated means described in this study, technical limitations and inconclusive results may hamper diagnosis. Our findings suggest that the computer-aided detection system presented herein can render diagnostic information, and thus may provide clinicians with an important tool to validate a diagnosis of celiac disease.

Keywords

Celiac disease Shannon entropy Normalization Particle swarm optimization Daisy descriptors, PillCam 

Notes

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Jahmunah Vicnesh
    • 1
  • Joel Koh En Wei
    • 1
  • Edward J. Ciaccio
    • 2
  • Shu Lih Oh
    • 1
  • Govind Bhagat
    • 2
    • 3
  • Suzanne K. Lewis
    • 2
  • Peter H. Green
    • 2
  • U. Rajendra Acharya
    • 1
    • 4
    • 5
    Email author
  1. 1.Department of Electronics and Computer Engineering, Ngee Ann PolytechnicSingaporeSingapore
  2. 2.Department of Medicine – Celiac Disease CenterColumbia UniversityNew YorkUSA
  3. 3.Department of Pathology and Cell BiologyColumbia UniversityNew YorkUSA
  4. 4.Department of Biomedical Engineering, School of Science and TechnologySingapore University of Social SciencesSingaporeSingapore
  5. 5.School of Medicine, Faculty of Health and Medical SciencesTaylor’s UniversitySubang JayaMalaysia

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