Polyp fingerprint: automatic recognition of colorectal polyps’ unique features

Ana García-Rodríguez, Jorge Bernal, F. Javier Sánchez, Henry Córdova, Rodrigo Garcés Durán, Cristina Rodríguez de Miguel, Gloria Fernández-Esparrach*

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)


Background: Content-based image retrieval (CBIR) is an application of machine learning used to retrieve images by similarity on the basis of features. Our objective was to develop a CBIR system that could identify images containing the same polyp (‘polyp fingerprint’). Methods: A machine learning technique called Bag of Words was used to describe each endoscopic image containing a polyp in a unique way. The system was tested with 243 white light images belonging to 99 different polyps (for each polyp there were at least two images representing it in two different temporal moments). Images were acquired in routine colonoscopies at Hospital Clínic using high-definition Olympus endoscopes. The method provided for each image the closest match within the dataset. Results: The system matched another image of the same polyp in 221/243 cases (91%). No differences were observed in the number of correct matches according to Paris classification (protruded: 90.7% vs. non-protruded: 91.3%) and size (< 10 mm: 91.6% vs. > 10 mm: 90%). Conclusions: A CBIR system can match accurately two images containing the same polyp, which could be a helpful aid for polyp image recognition.

Original languageEnglish
Pages (from-to)1887-1889
Number of pages3
JournalSurgical Endoscopy
Issue number4
Publication statusPublished - 1 Apr 2020


  • Artificial intelligence
  • Colorectal polyps
  • Content-based image retrieval


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