@misc{10481/92252,
year = {2024},
month = {3},
url = {https://hdl.handle.net/10481/92252},
abstract = {Content-Based Histopathological Image Retrieval (CBHIR) is a search technique based on
the visual content and histopathological features of whole-slide images (WSIs). CBHIR tools assist
pathologists to obtain a faster and more accurate cancer diagnosis. Stain variation between hospitals
hampers the performance of CBHIR tools. This paper explores the effects of color normalization
(CN) in a recently proposed CBHIR approach to tackle this issue. In this paper, three different CN
techniques were used on the CAMELYON17 (CAM17) data set, which is a breast cancer data set.
CAM17 consists of images taken using different staining protocols and scanners in five hospitals. Our
experiments reveal that a proper CN technique, which can transfer the color version into the most
similar median values, has a positive impact on the retrieval performance of the proposed CBHIR
framework. According to the obtained results, using CN as a pre-processing step can improve the
accuracy of the proposed CBHIR framework to 97% (a 14% increase), compared to working with the
original images.},
organization = {European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 860627 (CLARIFY Project). CLoud ARtificial Intelligence For pathologY. DOI: 10.3030/860627},
publisher = {MDPI},
keywords = {Color},
keywords = {Histopathological images},
keywords = {Content Based Image Retrieval},
keywords = {Whole slide imaging},
title = {Advancing Content-Based Histopathological Image Retrieval Pre-Processing: A Comparative Analysis of the Effects of Color Normalization Techniques},
doi = {10.3390/app14052063},
author = {Tabatabaei, Zahra and Pérez Bueno, Fernando and Colomer, Adrián and Oliver Moll, Javier and Molina Soriano, Rafael and Naranjo, Valery},
}