Description: A novel method is developed for non-rigid registration of transrectal ultrasound and magnetic resonance prostate images based on a non-linear regularized framework of point correspondences obtained from a statistical measure of shape-contexts. The segmented prostate shapes are represented by shape-contexts and the Bhattacharyya distance between the shape contexts is used to find th...

Multimodal registration is a challenging problem due the high variability of tissue appearance under different imaging modalities. The crucial component here is the choice of the right similarity measure. We make a step towards a general learning-based solution that can be adapted to specific situations and present a metric based on a convolutional neural network. Our network can be trained fro...

Description: A novel method is developed for non-rigid registration of transrectal ultrasound and magnetic resonance prostate images based on a non-linear regularized framework of point correspondences obtained from a statistical measure of shape-contexts. The segmented prostate shapes are represented by shape-contexts and the Bhattacharyya distance between the shape contexts is used to find th...

Recent studies indicate that maximizing the mutual information of the joint histogram of two images is an accurate and robust way to rigidly register two monoor multimodal images. Using mutual information for registration directly in a local manner is often not admissible owing to the weakened statistical power of the local histogram compared to a global one. We propose to use a global joint hi...

This article deals with registration and fusion of multimodal opththalmologic images obtained by means of a laser scanning device (Heidelberg retina angiograph). The registration framework has been designed and tested for combination of autofluorescent and infrared images. This process is a necessary step for consecutive pixel level fusion and analysis utilizing information from both modalities...

Current methods of multimodal image registration usually seek to maximize the similarity measure of mutual information (MI) between two images over their region of overlap. In applications such as planned radiation therapy, a diagnostician is more concerned with registration over specific regions of interest (ROI) than registration of the global image space. Registration of the ROI can be unrel...

In this paper, different automatic registration schemes based on different optimization techniques in conjunction with different similarity measures are compared in terms of accuracy and efficiency. Results from every optimization procedure are quantitatively evaluated with respect to the manual registration, which is the standard registration method used in clinical practice. The comparison ha...

Rigid registration of bone structures in stacks of CT images can be improved by using a binary segmentation mask for the registration compared to using the segmented grey values directly. Three criteria are applied to the different reference and template datasets in order to quantify registration results. For all three criteria a statistically significant superiority of the technique using the ...

11 Existing algorithms based on scale invariant feature transform (SIFT) and Harris corners such as edge-driven 12 dual-bootstrap iterative closest point and Harris-partial intensity invariant feature descriptor (PIIFD) respectivley have 13 been shown to be robust in registering multimodal retinal images. However, they fail to register color retinal 14 images with other modalities in the presen...