Fast and Robust Symmetric Image Registration Based on Distances Combining Intensity and Spatial Information

Abstract

Intensity-based image registration approaches rely on similarity measures to guide the search for geometric correspondences with high affinity between images. The properties of the used measure are vital for the robustness and accuracy of the registration. In this study a symmetric, intensity interpolationfree, affine registration framework based on a combination of intensity and spatial information is proposed. The excellent performance of the framework is demonstrated on a combination of synthetic tests, recovering known transformations in the presence of noise, and real applications in biomedical and medical image registration, for both 2D and 3D images. The method exhibits greater robustness and higher accuracy than similarity measures in common use, when inserted into a standard gradientbased registration framework available as part of the open source Insight Segmentation and Registration Toolkit (ITK). The method is also empirically shown to have a low computational cost, making it practical for real applications. Source code is available.

Towards Automatic Protein Co-Expression Quantification in Immunohistochemical TMA Slides

“…2) Image registration: The H images from the cores carry information about the common structure of the core; a common scaffold, and are therefore used for image registration (while the DAB images may differ as they represent different proteins that may have different spatial distribution within the tissue). Using the recent registration framework proposed by [10], the main transformation between cores and its parameters is obtained by using H 1 and H 2 in equation 1:…”

“…and spatial information in the same regularization term [10], typically exhibiting a larger region of convergence, increasing the chance of finding the correct transformation parameters under larger rotations, translations and scaling.…”

“…This motivates the alignment of the cores. To do so, we used a recent registration framework [10] that takes advantage of the spatial information carried by H and also the intensity information of the unmixed stains.…”

Algorithm Selection in Multimodal Medical Image Registration

“…Furthermore, the registration process is a necessary step before information obtained from different patterns can be fused or integrated. Although the early usage of medical image registration primarily occurred in the analysis of brain imaging, it has now become a core analysis tool in several other medical imaging applications, including cardiology, neurology, oncology, dentistry, and orthopedics [5]. Two categories of registration are used: visual registration and registration that utilizes a computer system.…”

Partial pose estimation of 3D rigid object system using outer box method

“…Previous study presented various techniques on CAD image database development [5] using .stl data, image data acquisition for surface inspection [6], pre-processing techniques on image registration [7][8][9][10][11][12][13][14][15][16][17][18], features extraction such as Vote-based 3D shape recognition and registration [19], edges extraction [20], reflection symmetric [21], DoG-based detector presented by deriving scale-invariant mesh features for image registration [22], Local Procustes Regression (LPR) [23], Estimation-by-Completion (EbC) [24] and Customized three-dimensional template matching [25][26][27] In this paper, the study focus on object description, scaling and image registration method. The contribution of this paper two fold.…”