TY - JOUR
T1 - TCIA
T2 - A Transformer-CNN Model with Illumination Adaptation for Enhancing Cell Image Saliency and Contrast
AU - Yang, Jietao
AU - Huang, Guoheng
AU - Luo, Yanzhang
AU - Zhang, Xiaofeng
AU - Yuan, Xiaochen
AU - Chen, Xuhang
AU - Pun, Chi Man
AU - Cai, Mu Yan
N1 - Publisher Copyright:
© 1963-2012 IEEE.
PY - 2025
Y1 - 2025
N2 - Inconsistent illumination across imaging instruments poses significant challenges for accurate cell detection and analysis. Conventional methods (e.g. histogram equalization and basic filtering) struggle to adapt to complex lighting conditions, resulting in limited image enhancement and inconsistent performance. To address these issues, we propose the Transformer-CNN Illumination Adaptation (TCIA) model, which improves cell image saliency and contrast. By extracting Illumination Invariant Features (IIF) using a locally sensitive histogram as prior knowledge, our model effectively adapts to varying illumination conditions. The TCIA framework employs Hybrid Convolution Blocks (HCB) to extract and preserve essential features from image pairs, followed by a two-branch decomposition-fusion network that separates features into low-frequency and high-frequency components. The Lite-Transformer (LT) captures global context for low-frequency features, while the Circular Difference Invertible (CDI) module focuses on fine-grained textures and edges. These features are then fused and reconstructed to produce high-contrast, salient images. Extensive experiments on three datasets (MoNuSeg, MoNuSAC, and our contributed MTGC) demonstrate that TCIA outperforms existing methods in image fusion and cell detection, achieving an average improvement in detection accuracy 2%. This work provides a robust and innovative solution for enhanced cell imaging, contributing to more precise diagnostics and analysis. The source code will be available at https://github.com/Mrzhans/TCIA.
AB - Inconsistent illumination across imaging instruments poses significant challenges for accurate cell detection and analysis. Conventional methods (e.g. histogram equalization and basic filtering) struggle to adapt to complex lighting conditions, resulting in limited image enhancement and inconsistent performance. To address these issues, we propose the Transformer-CNN Illumination Adaptation (TCIA) model, which improves cell image saliency and contrast. By extracting Illumination Invariant Features (IIF) using a locally sensitive histogram as prior knowledge, our model effectively adapts to varying illumination conditions. The TCIA framework employs Hybrid Convolution Blocks (HCB) to extract and preserve essential features from image pairs, followed by a two-branch decomposition-fusion network that separates features into low-frequency and high-frequency components. The Lite-Transformer (LT) captures global context for low-frequency features, while the Circular Difference Invertible (CDI) module focuses on fine-grained textures and edges. These features are then fused and reconstructed to produce high-contrast, salient images. Extensive experiments on three datasets (MoNuSeg, MoNuSAC, and our contributed MTGC) demonstrate that TCIA outperforms existing methods in image fusion and cell detection, achieving an average improvement in detection accuracy 2%. This work provides a robust and innovative solution for enhanced cell imaging, contributing to more precise diagnostics and analysis. The source code will be available at https://github.com/Mrzhans/TCIA.
KW - cell detection
KW - Cell image enhancement
KW - illumination-adaptive
KW - transformer-cnn
UR - http://www.scopus.com/inward/record.url?scp=85214798835&partnerID=8YFLogxK
U2 - 10.1109/TIM.2025.3527542
DO - 10.1109/TIM.2025.3527542
M3 - Article
AN - SCOPUS:85214798835
SN - 0018-9456
JO - IEEE Transactions on Instrumentation and Measurement
JF - IEEE Transactions on Instrumentation and Measurement
ER -