Non-rigid registration of serial section images by blending transforms for 3D reconstruction

Abstract In this research, we propose a novel registration method for three-dimensional (3D) reconstruction from serial section images. 3D reconstructed data from serial section images provides structural information with high resolution. However, there are three problems in 3D reconstruction: non-rigid deformation, tissue discontinuity, and accumulation of scale change. To solve the non-rigid deformation, we propose a novel non-rigid registration method using blending rigid transforms. To avoid the tissue discontinuity, we propose a target image selection method using the criterion based on the blending of transforms. To solve the scale change of tissue, we propose a scale adjustment method using the tissue area before and after registration. The experimental results demonstrate that our method can represent non-rigid deformation with a small number of control points, and is robust to a variation in staining. The results also demonstrate that our target selection method avoids tissue discontinuity and our scale adjustment reduces scale change.

[1]  Johan Debayle,et al.  Rigid image registration by General Adaptive Neighborhood matching , 2016, Pattern Recognit..

[2]  Nikos Paragios,et al.  Deformable Medical Image Registration: A Survey , 2013, IEEE Transactions on Medical Imaging.

[3]  Chigako Uwabe,et al.  Phenotypic variability in human embryonic holoprosencephaly in the Kyoto Collection. , 2004, Birth defects research. Part A, Clinical and molecular teratology.

[4]  Adrien Bartoli,et al.  Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces , 2013, BMVC.

[5]  Paul L. Rosin,et al.  Feature Neighbourhood Mutual Information for multi-modal image registration: An application to eye fundus imaging , 2015, Pattern Recognit..

[6]  Ching-Wei Wang,et al.  Robust image registration of biological microscopic images , 2014, Scientific Reports.

[7]  Nicholas Ayache,et al.  A Fast and Log-Euclidean Polyaffine Framework for Locally Linear Registration , 2009, Journal of Mathematical Imaging and Vision.

[8]  Brian B. Avants,et al.  3D Mouse Brain Reconstruction from Histology Using a Coarse-to-Fine Approach , 2006, WBIR.

[9]  Brian B. Avants,et al.  Histology-derived volumetric annotation of the human hippocampal subfields in postmortem MRI , 2014, NeuroImage.

[10]  D. Shepard.  A two-dimensional interpolation function for irregularly-spaced data , 1968, ACM National Conference.

[11]  R. Woods,et al.  Mapping Histology to Metabolism: Coregistration of Stained Whole-Brain Sections to Premortem PET in Alzheimer's Disease , 1997, NeuroImage.

[12]  Its'hak Dinstein,et al.  New maximum likelihood motion estimation schemes for noisy ultrasound images , 2002, Pattern Recognit..

[13]  Hans Martin Kjer,et al.  Free-form image registration of human cochlear μCT data using skeleton similarity as anatomical prior , 2016, Pattern Recognit. Lett..

[14]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[15]  Célia A. Zorzo Barcelos,et al.  A variational approach to non-rigid image registration with Bregman divergences and multiple features , 2018, Pattern Recognit..

[16]  Sébastien Ourselin,et al.  Co-registration of Histological, Optical and MR Data of the Human Brain , 2002, MICCAI.

[17]  Carlos Ortiz-de-Solorzano,et al.  Consistent and Elastic Registration of Histological Sections Using Vector-Spline Regularization , 2006, CVAMIA.

[18]  Gary R. Bradski,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.

[19]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[20]  M H Deverell,et al.  Three-dimensional reconstruction: methods of improving image registration and interpretation. , 1993, Analytical cellular pathology : the journal of the European Society for Analytical Cellular Pathology.

[21]  Junzhou Huang,et al.  Simultaneous image transformation and sparse representation recovery , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Ching-Wei Wang,et al.  Fully automatic and robust 3D registration of serial-section microscopic images , 2015, Scientific Reports.

[23]  H. Yokota,et al.  Prediction of open urinary tract in laparoscopic partial nephrectomy by virtual resection plane visualization , 2014, BMC Urology.

[24]  Stefanos Zafeiriou,et al.  Robust FFT-Based Scale-Invariant Image Registration with Image Gradients , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Stefan Klein,et al.  Randomly Perturbed B-Splines for Nonrigid Image Registration , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Yasuhiro Mukaigawa,et al.  Feature-Based Non-rigid Registration of Serial Section Images by Blending Rigid Transformations , 2017, 2017 4th IAPR Asian Conference on Pattern Recognition (ACPR).

[27]  Jan Flusser,et al.  Image registration methods: a survey , 2003, Image Vis. Comput..

[28]  Hiroyuki Ochiai,et al.  Anti-commutative Dual Complex Numbers and 2D Rigid Transformation , 2016, ArXiv.

[29]  Li Bai,et al.  Smoothness-guided 3-D reconstruction of 2-D histological images , 2011, NeuroImage.

[30]  Sebastian Zambanini,et al.  Feature-based groupwise registration of historical aerial images to present-day ortho-photo maps , 2018, Pattern Recognit..

[31]  T Gustavsson,et al.  Computer‐assisted realignment of light micrograph images from consecutive section series of cat cerebral cortex , 1992, Journal of microscopy.

[32]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[33]  Daniel Rueckert,et al.  Diffeomorphic Registration Using B-Splines , 2006, MICCAI.

[34]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[35]  Aaron D. Ward,et al.  A Method for 3D Histopathology Reconstruction Supporting Mouse Microvasculature Analysis , 2015, PloS one.

[36]  Vincent Lepetit,et al.  BRIEF: Computing a Local Binary Descriptor Very Fast , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[37]  Jirí Zára,et al.  Geometric skinning with approximate dual quaternion blending , 2008, TOGS.

[38]  Charles R. Meyer,et al.  Mutual Information for Automated Unwarping of Rat Brain Autoradiographs , 1997, NeuroImage.

[39]  Torsten Rohlfing,et al.  Image Similarity and Tissue Overlaps as Surrogates for Image Registration Accuracy: Widely Used but Unreliable , 2012, IEEE Transactions on Medical Imaging.

[40]  Chia-Ling Tsai,et al.  Registration of Challenging Image Pairs: Initialization, Estimation, and Decision , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[41]  Nicholas Ayache,et al.  Polyrigid and polyaffine transformations: A novel geometrical tool to deal with non-rigid deformations - Application to the registration of histological slices , 2005, Medical Image Anal..

[42]  Tianzi Jiang,et al.  Nonrigid registration of brain MRI using NURBS , 2007, Pattern Recognit. Lett..

[43]  Albert C. S. Chung,et al.  A novel learning-based dissimilarity metric for rigid and non-rigid medical image registration by using Bhattacharyya Distances , 2017, Pattern Recognit..

[44]  Guojun Lu,et al.  Enhancing SIFT-based image registration performance by building and selecting highly discriminating descriptors , 2016, Pattern Recognit. Lett..