Automated feature-based alignment for 3D volume reconstruction of CLSM imagery

We address the problem of automated image alignment for 3D volume reconstruction from stacks of fluorescent confocal laser scanning microscope (CLSM) imagery acquired at multiple confocal depths, from a sequence of consecutive slides. We focus on automated image alignment based on centroid and area shape features by solving feature correspondence problem, also known as Procrustes problem, in highly deformable and ill-conditioned feature space. In result, we compare image alignment accuracy of a fully automated method with registration accuracy achieved by human subjects using a manual alignment method. Our work demonstrates significant benefits of automation for 3D volume reconstruction in terms of accuracy, consistency, and performance time. We also outline the limitations of fully automated and manual 3D volume reconstruction system