Blood Vessel Analysis

Introduction

A three-dimensional (3D) representation of vasculature in the brain is extremely important in image-guided neurosurgery, pre-operation planning and clinical analysis. Typically, the data is available in a set of two-dimensional slices covering the brain, where a lot of information is present but noisy and hidden in between slices. Experts have to select and color the desired regions of slices before a program connects the colored components. This is tedious, not precise and prone to human error. There is no reason why one could not obtain improved results using intelligent shape models and automatic reconstruction algorithms. In the course of my master's research, I have mastered the theory of such model-based techniques using invariant geometric flows for blood vessel segmentation. These types of evolution equations are flexible and adaptable to new constraints and external forces. The essential idea is to evolve a surface in 3D so that it clings to the features of interest in the image.

Problem

We are given a magnetic ressonance sequence of images (MRI) like the proton density shown below. Note that blood vessels appear dark on bright structures in PD images

Currently, most blood vessel segmentation approaches in the literature work on specific MRA (Magnetic Resonnance Angiogram) examples but are not applicable on common MRI sequences like PD, T1 ro T2. The challenge is to detect and segment the 3D vasculature tree present in that kind of standard MR image commonly used in all hospitals.

Approach

We use the Hessian matrix to locally describe the structure of the image. Frangi et al. [2] defined a multiscale vesselness measure based on eigen values of the Hessian matrix. The operator is designed to be maximum at the vessel's centerline. A voxel belonging to a voxel region will ideally have its smallest eigen value very close to zero and the other two of large magnitude and almost equal. Finally, the eigen vector corresponding to the zero eigen value gives the vessel's orientation. Examples are shown below. Here we visualize a levelset of the maximum vesselness measure with overlayed eigen vector.

Figure 1. Synthetic branching and helix example

Figure 2. Cropped region of a MRA

We see that the eigen vector agrees nicely with the vessel orientation. Hence, at every voxel, we have a vesselness measure. Moreover, for strong vesselness quantities we also have the vessel's orientation and the vessel's radius (scale at which the maximum vesselness was detected).

Now, we have enough information to drive a flow that will cling to blood vessels. We are working on an augmentation of the flux maximizing geometric flow created by Vasilevskiy and Siddiqi in [2]. Their flow evolves a surface in a 3D vector field so as to increase the inward flux through its boundary as fast as possible. The gradient equation is

The evolution is simple and has an elegant interpretation. However, it does not include any geometrical properties of the vessels to be segmented.

We are investigating the augmentation of this method by incorporating information about the geometry of tube-like structures. As seen before, the multiscale eigen value and eigen vector analysis of the Hessian matrix provides information on the location, direction and size of vessels. We are able to locally fit and align ellipsoids (such as those seen in Figure 3) close to the true vessel boundary, which allows the computation of a much better flux map and thus, the segmentation process is cleaner and more accurate and "intelligent".

Figure 3. Ellipsoid of a given orientation and a scale to fit the true vessel boundaries.

Examples

Here is the result of the flux maximizing flow segmentation on a MRA image presented in [1]. A flow version of the augmentation with geometrical consideration of tube-like structures should be ready soon.

Key References

1: A. Vasilevskiy and K. Siddiqi
Flux maximizing geometric flows.
IEEE Transactions on Pattern Analysis and Machine Intelligence, 24:12, pp. 1-14, 2002.
2: A. Frangi, W. Niessen, K.L. Vincken, and M.A. Viergever
Multiscale vessel enhancement filtering.
Proc. MICCAI'98, pp.130-137, 1998.
3: K. Krissian, G. Malandain, N. Ayache
Model-based detection of tubular structures in 3D images.
Computer Vision and Image Understanding, 80:2, pp. 130-171, Nov. 2000.