Automatic registration and fast volume reconstruction from serial histology sections

The scope of this research is to propose a novel method for automatic registration and fast volume reconstruction of serial tissue sections based on common-configured computer, on the premise that the algorithm can achieve accurate result with fast speed and little interaction as much as possible. The whole flowchart comprises the four main parts, of which image registration and image reconstruction are of great significance, materialized the following innovative ideas. Firstly, mutual information measure is combined with morphological gradient information to contain fewer erroneous maxima and lead to the global maximum when registration. Secondly, the hybrid optimizer combined Particle Swarm Optimization (PSO) with Powell algorithm is proposed to restrain local maxima of mutual information function and to improve the registration accuracy. Thirdly, the multiresolution data structure based on Mallat decomposition not only improves the behavior of registration function, but also accelerates the algorithm speed. Finally, an improved Shear–Warp algorithm is proposed based on the sorted volumetric dataset for registered image stack, which can skip all of the transparent voxels and achieve faster speed. Experimental results demonstrate that the novel registration and reconstruction algorithms are effective and efficient to achieve the virtual three-dimensional gekko’s cervical spinal cord successfully and the reconstruction result can be rotated, scaled, incised and measured arbitrarily, which is valuable to be applied to the other kinds of serial histology sections.

► The method is given to registration and volume reconstruction of tissue sections.
► The ultimate goal is to achieve results with fast speed and little interaction.
► The innovative aspects are improved MI, hybrid optimizer and improved S-W algorithm.
► The reconstructed 3D gekko’s cervical spinal cord can be operated arbitrarily.