Performance Comparison of Multi-Modal Fusion Techniques in Tissue Perfusion Analysis Using Homography Calibration
SENSORS, cilt.26, sa.14, ss.1-13, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 26 Sayı: 14
- Basım Tarihi: 2026
- Doi Numarası: 10.3390/s26144585
- Dergi Adı: SENSORS
- Derginin Tarandığı İndeksler: Academic Search Ultimate (EBSCO), Biomedical Reference Collection: Corporate Edition (EBSCO), Health Research Premium Collection (ProQuest), Scopus, Science Citation Index Expanded (SCI-EXPANDED), Compendex, EMBASE, INSPEC, MEDLINE, Directory of Open Access Journals
- Sayfa Sayıları: ss.1-13
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Akdeniz Üniversitesi Adresli: Evet
Özet
This study explores the impact of fusion techniques and camera calibration on tissue perfusion analysis using multi-modal imaging. Building on the existing TTPD dataset collected with a prototype hardware platform, we focus on optimizing data alignment and fusion strategies to improve classification accuracy. The imaging system integrates infrared (IR), thermal, and RGB cameras, capturing complementary information about tissue perfusion. To improve modality alignment, we apply Homography-based calibration, reducing spatial discrepancies between different imaging sources. Furthermore, we evaluated early and late fusion approaches using deep learning models (ResNet50 and ResNet101) to determine the most effective integration strategy. Experimental results demonstrate that late fusion, particularly the combination of thermal and RGB modalities, achieves the highest classification performance and that Homography-based alignment improves results. These findings highlight the importance of precise calibration and modality selection in the development of robust and non-invasive tissue perfusion monitoring systems.