Assessment of artery dilation by using image registration based on spatial features

The use of affine image registration based on normalized mutual information (NMI) has recently been proposed by Frangi et al. as an automatic method for assessing brachial artery flow mediated dilation (FMD) for the characterization of endothelial function. Even though this method solves many problems of previous approaches, there are still some situations that can lead to misregistration between frames, such as the presence of adjacent vessels due to probe movement, muscle fibres or poor image quality. Despite its widespread use as a registration metric and its promising results, MI is not the panacea and can occasionally fail. Previous work has attempted to include spatial information into the image similarity metric. Among these methods the direct estimation of α-MI through Minimum Euclidean Graphs allows to include spatial information and it seems suitable to tackle the registration problem in vascular images, where well oriented structures corresponding to vessel walls and muscle fibres are present. The purpose of this work is twofold. Firstly, we aim to evaluate the effect of including spatial information in the performance of the method suggested by Frangi et al. by using α-MI of spatial features as similarity metric. Secondly, the application of image registration to long image sequences in which both rigid motion and deformation are present will be used as a benchmark to prove the value of α-MI as a similarity metric, and will also allow us to make a comparative study with respect to NMI.

[1]  W. Eric L. Grimson,et al.  Multi-modal Volume Registration Using Joint Intensity Distributions , 1998, MICCAI.

[2]  Huzefa Neemuchwala,et al.  Image registration using alpha-entropy measures and entropic graphs , 2002 .

[3]  Carlo Tomasi,et al.  Image Similarity Using Mutual Information of Regions , 2004, ECCV.

[4]  Alfred O. Hero,et al.  Feature coincidence trees for registration of ultrasound breast images , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[5]  Daniel Rueckert,et al.  Non-rigid registration using higher-order mutual information , 2000, Medical Imaging.

[6]  A. Hero,et al.  Entropic Graphs for Registration , 2018, Multi-Sensor Image Fusion and Its Applications.

[7]  Alfred O. Hero,et al.  Applications of entropic spanning graphs , 2002, IEEE Signal Process. Mag..

[8]  Alejandro F. Frangi,et al.  A registration-based approach to quantify flow-mediated dilation (FMD) of the brachial artery in ultrasound image sequences , 2003, IEEE Transactions on Medical Imaging.

[9]  Colin Studholme,et al.  An overlap invariant entropy measure of 3D medical image alignment , 1999, Pattern Recognit..

[10]  Alejandro F Frangi,et al.  Combined statistical analysis of vasodilation and flow curves in brachial ultrasonography: technique and its connection to cardiovascular risk factors , 2005, SPIE Medical Imaging.

[11]  Max A. Viergever,et al.  Image registration by maximization of combined mutual information and gradient information , 2000, IEEE Transactions on Medical Imaging.

[12]  Guy Marchal,et al.  Automated multi-moda lity image registration based on information theory , 1995 .

[13]  Paul A. Viola,et al.  Alignment by Maximization of Mutual Information , 1997, International Journal of Computer Vision.

[14]  Alfred O. Hero,et al.  Asymptotic theory of greedy approximations to minimal k-point random graphs , 1999, IEEE Trans. Inf. Theory.

[15]  Anand Rangarajan,et al.  A unified feature-based registration method for multimodality images , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[16]  Jean-Philippe Thiran,et al.  Affine Registration with Feature Space Mutual Information , 2001, MICCAI.