Design and Development of an Automatic Blood Detection System for Capsule Endoscopy Images

  • Pedro Pons
  • Reinier NoordaEmail author
  • Andrea Nevárez
  • Adrián Colomer
  • Vicente Pons Beltrán
  • Valery Naranjo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11872)


Wireless Capsule Endoscopy is a technique that allows for observation of the entire gastrointestinal tract in an easy and non-invasive way. However, its greatest limitation lies in the time required to analyze the large number of images generated in each examination for diagnosis, which is about 2 h. This causes not only a high cost, but also a high probability of a wrong diagnosis due to the physician’s fatigue, while the variable appearance of abnormalities requires continuous concentration. In this work, we designed and developed a system capable of automatically detecting blood based on classification of extracted regions, following two different classification approaches. The first method consisted in extraction of hand-crafted features that were used to train machine learning algorithms, specifically Support Vector Machines and Random Forest, to create models for classifying images as healthy tissue or blood. The second method consisted in applying deep learning techniques, concretely convolutional neural networks, capable of extracting the relevant features of the image by themselves. The best results (95.7% sensitivity and 92.3% specificity) were obtained for a Random Forest model trained with features extracted from the histograms of the three HSV color space channels. For both methods we extracted square patches of several sizes using a sliding window, while for the first approach we also implemented the waterpixels technique in order to improve the classification results.


Wireless capsule endoscopy Blood detection Machine learning Hand-crafted features Deep learning Convolutional neural networks 



This work was funded by the European Union’s H2020: MSCA: ITN program for the “Wireless In-body Environment Communication - WiBEC” project under the grant agreement no. 675353. Additionally, we gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan V GPU used for this research.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Pedro Pons
    • 1
  • Reinier Noorda
    • 2
    Email author
  • Andrea Nevárez
    • 3
  • Adrián Colomer
    • 1
  • Vicente Pons Beltrán
    • 3
  • Valery Naranjo
    • 1
  1. 1.Instituto de Investigación e Innovación en Bioingeniería (I3B)Universitat Politècnica de ValènciaValenciaSpain
  2. 2.iTEAM Research InstituteUniversitat Politècnica de ValènciaValenciaSpain
  3. 3.Unidad de Endoscopia Digestiva, Hospital Universitari i Politécnic La Fe, Digestive Endoscopy Research Group, IIS La FEValenciaSpain

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