Robust blind color deconvolution and blood detection on histological images using Bayesian K-SVD
Year: 2024
Type: article
Abstract: Hematoxylin and Eosin (H&E) color variation among histological images from different laboratories can significantly degrade the performance of Computer-Aided Diagnosis systems. The staining procedure is the primary factor responsible for color variation, and consequently, the methods designed to reduce such variations are designed in concordance with this procedure. In particular, Blind Color Deconvolution (BCD) methods aim to identify the true underlying colors in the image and to separate the tissue structure from the color information. Unfortunately, BCD methods often assume that images are stained solely with pure staining colors (e.g., blue and pink for H&E). This assumption does not hold true when common artifacts such as blood are present, requiring an additional color component to represent them. This is a challenge for color standardization algorithms, which are unable to correctly identify the stains in the image, leading to unexpected results. In this work, we propose a Blood-Robust Bayesian K-Singular Value Decomposition model designed to simultaneously detect blood and extract color from histological images while preserving structural details. We evaluate our method using both synthetic and real images, which contain varying amounts of blood pixels. • Blind Color Deconvolution separates color from structure in histological images. • Few BCD methods consider the effects of noise and artifacts. • We propose a blood robust approach that combines blood detection with BCD. • The proposed approach significantly improves the quality of stain separation in the presence of blood.
Institutions: Universidad de Granada, University of Stavanger
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,230
OpenAlex ID: W4401829880