Deep Convolutional Feature-Based Fluorescence-to-Color Image Registration

Fluorescence guided surgery (FGS) combines functional information (fluorescence imaging) and structural information (color imaging) to improve surgery performance. Fluorescence-to-color image registration plays fundamental role in FGS. In this paper, we used VGG16 to extract image features from color and fluorescence images, and feature descriptors were built with these features. Then, keypoint matching was conducted to build correspondence between color image and fluorescence image. Finally, fluorescence-to-color image registration was achieved based on the matched keypoint pairs. Experimental results show that our method outperforms conventional feature based image registration algorithms, like SIFT, BRISK, SUFT, ORB.

Recommended citation: X. Liu, T. Quang, W. Deng and Y. Liu, "Deep Convolutional Feature-Based Fluorescence-to-Color Image Registration," 2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Lausanne, Switzerland, 2021, pp. 1-6, doi: 10.1109/MeMeA52024.2021.9478607.
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