Deep Features Extraction for Endoscopic Image Matching

Houda Chaabouni-Chouayakh, Houda Chaabouni-Chouayakh, Manel Farhat, Manel Farhat, Achraf Ben-Hamadou, Achraf Ben-Hamadou

2022

Abstract

Image feature matching is a key step in creating endoscopic mosaics of the bladder inner walls, which help urologists in lesion detection and patient follow-up. Endoscopic images, on the other hand, are particularly difficult to match because they are weekly textured and have limited surface area per frame. Deep learning techniques have recently gained popularity in a variety of computer vision tasks. The ability of convolutional neural networks (CNNs) to learn rich and optimal features contributes to the success of these methods. In this paper, we present a novel deep learning based approach for endoscopic image matching. Instead of standard handcrafted image descriptors, we designed a CNN to extract feature vector from local interest points. We propose an efficient approach to train our CNN without manually annotated data. We proposed an adaptive triplet loss which has the advantage of improving the inter-class separability as well as the inter class compactness. The training dataset is automatically constructed, each sample is a triplet of patches: an anchor, one positive sample (a perspective transformation of the anchor) and one negative sample. The obtained experimental results show at the end of the training step a more discriminative space representation where the anchor becomes closer to the positive sample and farther from the negative one in the embedding space. Comparison with the well-known standard hand-crafted descriptor SIFT in terms of recall and precision showed the effectiveness of the proposed approach, reaching the top recall value for a precision value of 0.97.

Paper Citation

in Harvard Style

Chaabouni-Chouayakh H., Farhat M. and Ben-Hamadou A. (2022). Deep Features Extraction for Endoscopic Image Matching. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP; ISBN 978-989-758-555-5, SciTePress, pages 925-932. DOI: 10.5220/0010833700003124

in Bibtex Style

@conference{visapp22,
author={Houda Chaabouni-Chouayakh and Manel Farhat and Achraf Ben-Hamadou},
title={Deep Features Extraction for Endoscopic Image Matching},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP},
year={2022},
pages={925-932},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010833700003124},
isbn={978-989-758-555-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP
TI - Deep Features Extraction for Endoscopic Image Matching
SN - 978-989-758-555-5
AU - Chaabouni-Chouayakh H.
AU - Farhat M.
AU - Ben-Hamadou A.
PY - 2022
SP - 925
EP - 932
DO - 10.5220/0010833700003124
PB - SciTePress