Boklye Kim1,
Jennifer L. Boes1,
Kirk A. Frey 2,
Charles R. Meyer1
1Department of Radiology, University of Michigan Medical Center
2Department of Internal Medicine, Division of Nuclear Medicine,
University of Michigan Medical Center
Abstract
An automated multimodal warping based on mutual information metric (MI) as a mapping cost function is demonstrated. Mutual information (I) is calculated from two dimensional (2D) gray scale histogram of an image pair and MI (=-I) provides a matching cost function which can be effective in registration of two or three dimensional data sets independent of modality. Most histological image data, though information rich and high resolution, present non-linear deformations due to the specimen sectioning and need reconstitution into deformation corrected volumes prior to geometric mapping to an anatomical volume for spatial analyses. The section alignment via automatic 2D registrations employing MI as a global cost function and thin-plate-spline (TPS) warping is applied to deoxy-D-[14C]glucose autoradiographic image slices of a rat brain with video reference images of the uncut block face to reconstitute a cerebral glucose metabolic volume data. Unlike the traditional feature based TPS warping algorithms, initial control points are defined independently from feature landmarks. Registration quality using automated multimodal image warping is validated by comparing MIs of the image pair registered by automated affine registration and manual warping method. The MI proves to be a robust objective matching cost function effective for automatic multimodality warping for 2D data sets and can be readily applied to volume registrations.
Keywords:
warping, unwarping, registration, fusion,
mutual information, MI,
automatic, automated,
rat brain,
autoradiograph
Kim, B., J.L. Boes, K.A. Frey and C.R. Meyer: Mutual information for automated unwarping of rat brain autoradiographs. NeuroImage 5(1):31-40, 1997
This work was supported in part by DHHS PHS grant NIH 1R01 CA59412-01.