| Residential College | false |
| Status | 即將出版Forthcoming |
| TCIA: A Transformer-CNN Model with Illumination Adaptation for Enhancing Cell Image Saliency and Contrast | |
Yang, Jietao1; Huang, Guoheng1; Luo, Yanzhang2; Zhang, Xiaofeng3; Yuan, Xiaochen4; Chen, Xuhang5; Pun, Chi Man6 ; Cai, Mu Yan7
| |
| 2025 | |
| Source Publication | IEEE Transactions on Instrumentation and Measurement
 |
| ISSN | 0018-9456 |
| Abstract | 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. |
| Keyword | cell detection Cell image enhancement illumination-adaptive transformer-cnn |
| DOI | 10.1109/TIM.2025.3527542 |
| URL | View the original |
| Language | 英語English |
| Scopus ID | 2-s2.0-85214798835 |
| Fulltext Access | |
| Citation statistics | |
| Document Type | Journal article |
| Collection | DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE |
| Affiliation | 1.Guangdong University of Technology, School of Computer Science and Technology, Guangzhou, 510006, China 2.Second Affiliated Hospital of SouthChina University of Technology, Guangzhou, 510180, China 3.Shanghai Jiao Tong University, School of Electronic Information and Electrical Engineering, Shanghai, 201100, China 4.Macao Polytechnic University, Faculty of Applied Sciences, Macao, 999078, Macao 5.Huizhou University, School of Computer Science and Engineering, Huizhou, 516001, China 6.University of Macau, Department of Computer and Information Science, Macao, 999078, Macao 7.Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, 510060, China |
| Recommended Citation GB/T 7714 | Yang, Jietao,Huang, Guoheng,Luo, Yanzhang,et al. TCIA: A Transformer-CNN Model with Illumination Adaptation for Enhancing Cell Image Saliency and Contrast[J]. IEEE Transactions on Instrumentation and Measurement, 2025. |
| APA | Yang, Jietao., Huang, Guoheng., Luo, Yanzhang., Zhang, Xiaofeng., Yuan, Xiaochen., Chen, Xuhang., Pun, Chi Man., & Cai, Mu Yan (2025). TCIA: A Transformer-CNN Model with Illumination Adaptation for Enhancing Cell Image Saliency and Contrast. IEEE Transactions on Instrumentation and Measurement. |
| MLA | Yang, Jietao,et al."TCIA: A Transformer-CNN Model with Illumination Adaptation for Enhancing Cell Image Saliency and Contrast".IEEE Transactions on Instrumentation and Measurement (2025). |
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