Constrained distance transforms for spatial atlas registration.

Bill Hill,Richard A Baldock

doi:10.1186/s12859-015-0504-5

Abstract

BackgroundSpatial frameworks are used to capture organ or whole organism image data in biomedical research. The registration of large biomedical volumetric images is a complex and challenging task, but one that is required for spatially mapped biomedical atlas systems. In most biomedical applications the transforms required are non-rigid and may involve significant deformation relating to variation in pose, natural variation and mutation. Here we develop a new technique to establish such transformations for mapping data that cannot be achieved by existing approaches and that can be used interactively for expert editorial review.ResultsThis paper presents the Constrained Distance Transform (CDT), a novel method for interactive image registration. The CDT uses radial basis function transforms with distances constrained to geodesics within the domains of the objects being registered. A geodesic distance algorithm is discussed and evaluated. Examples of registration using the CDT are presented.ConclusionThe CDT method is shown to be capable of simultaneous registration and foreground segmentation even when very large deformations are required.

Highlights

Spatial frameworks are used to capture organ or whole organism image data in biomedical research
Many of these databases, such as e-Mouse Atlas Gene Expression database (EMAGE) [2] and the Allen Brain Atlas [3], are based on volumetric atlases or reference models with assay data mapped onto the atlas models through non–linear spatial transformations or warps
In this paper we describe a mesh based image registration method, suited to interactive image registration and which is suitable for 2 and 3-dimensional images in which large deformations are required

Summary

Introduction

Spatial frameworks are used to capture organ or whole organism image data in biomedical research. The use of spatially mapped databases has become widespread within the biomedical research community [1] Many of these databases, such as EMAGE [2] and the Allen Brain Atlas [3], are based on volumetric atlases or reference models with assay data mapped onto the atlas models through non–linear spatial transformations or warps. With the significant challenges including variations in pose, mutant phenotypes, inter–species registration and the frequently non-corresponding image values due to gene expression or other spatial signals. It is often in these most challenging of cases that the biological interest is greatest. In such cases the time spent by an expert may be significantly less than that spent correcting correspondences found automatically by an algorithm

Methods

Results

Conclusion