Mosaicing of Dynamic Mesentery Video with Gradient Blending

In the aspect of medical imaging, it is crucial to obtain certain structures or even cellular features in microscopic resolution. Unfortunately, the field of view is inherently limited by the capability of capturing instruments. Thus, mosaicing of such microstructure is of utmost importance in order to restore original visual information for establishing broad structure morphology. Large panoramic images with microscopic resolution can be rewarding and practical for conducting comprehensive observation and exploration of the object of interest and surrounding biological structure. But mosaicing can be challenging if there are deformable, motion-blurred, textureless, feature-poor frames. Feature-based methods perform poorly in such cases for the lack of distinctive keypoints. Standard single block correlation matching strategies might not provide robust registration due to deformable content. In addition, the panorama suffers if there is motion blur present in a sequence. To handle these challenges, we propose a novel algorithm, Deformable Normalized Cross Correlation (DNCC) image matching with RANSAC to establish robust registration. Besides, to produce seamless panorama from motion-blurred frames we present gradient blending method based on image edge information. The DNCC algorithm is applied on Frog Mesentery sequences. Our result is compared with PSS/AutoStitch [1, 2] to establish the efficiency and robustness of the proposed DNCC method.

1 Introduction

Due to wide applications of biomedical imagery mosaicing, lots of research has been done on this field. Correlation based image registration and mosaicing has been explored for stitching of high-resolution microendoscope (HRME) [3], multispectral and hyperspectral data [4], chicken breast [5], etc. Some applications include Lukas Canady Tracker for feature selection for mosaicing of colon [6], bone, blood and lung [7]. In addition, structure propagation for mouse brain [8], phase correlation for breast tissue [9], SFT for confocal microscopy in the oral cavity [10], etc. have also shown promising results. Lots of research has been reported on retinal images with different features such as covariance [11], Y-feature: where 3 vessels converge [12], mSFT [13], URSFT [14], etc.