Content Based Image Retrieval Using Depth Maps for Colonoscopy Images

Md Rahman, JungHwan Oh, Wallapak Tavanapong, Piet C. de Groen

2023

Abstract

Content Based Image Retrieval (CBIR) finds similar images given a query image. Effective CBIR has been actively studied over several decades. For measuring image similarity, low-level visual features (i.e., color, shape, texture, and spatial layout), combination of low-level features, or Convolutional Neural Network (CNN) are typically used. However, a similarity measure based on these features is not effective for some type of images, for example, colonoscopy images captured from colonoscopy procedures. This is because the low-level visual features of these images are mostly very similar. We propose a new method to compare these images and find their similarity in terms of their surface topology. First, we generate a grey-scale depth map image for each image, then extract four straight lines from it. Each point in the four lines has a grey-scale value (depth) in its depth map. The similarity of the two images is measured by comparing the depth values on the four corresponding lines from the two images. We propose a function to compute a difference (distance) between two sets of four lines using Mean Absolute Error. The experiments based on synthetic and real colonoscopy images show that the proposed method is promising.

Paper Citation

in Harvard Style

Rahman M., Oh J., Tavanapong W. and C. de Groen P. (2023). Content Based Image Retrieval Using Depth Maps for Colonoscopy Images. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS; ISBN 978-989-758-631-6, SciTePress, pages 301-308. DOI: 10.5220/0011749100003414

in Bibtex Style

@conference{biosignals23,
author={Md Rahman and JungHwan Oh and Wallapak Tavanapong and Piet C. de Groen},
title={Content Based Image Retrieval Using Depth Maps for Colonoscopy Images},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS},
year={2023},
pages={301-308},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011749100003414},
isbn={978-989-758-631-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS
TI - Content Based Image Retrieval Using Depth Maps for Colonoscopy Images
SN - 978-989-758-631-6
AU - Rahman M.
AU - Oh J.
AU - Tavanapong W.
AU - C. de Groen P.
PY - 2023
SP - 301
EP - 308
DO - 10.5220/0011749100003414
PB - SciTePress