Marek Kocinski

3D image texture analysis of simulated and real-world vascular trees

A method is proposed for quantitative description of blood-vessel trees, which can be used for tree classification and/or physical parameters indirect monitoring. The method is based on texture analysis of 3D images of the trees. Several types of trees were defined, with distinct tree parameters (number of terminal branches, blood viscosity, input and output flow). A number of trees were computer-simulated for each type. 3D image was computed for each tree and its texture features were calculated. Best discriminating features were found and applied to 1-NN nearest neighbor classifier. It was demonstrated that (i) tree images can be correctly classified for realistic signal-to-noise ratio, (ii) some texture features are monotonously related to tree parameters, (iii) 2D texture analysis is not sufficient to represent the trees in the discussed sense. Moreover, applicability of texture model to quantitative description of vascularity images was also supported by unsupervised exploratory analysis. Eventually, the experimental confirmation was done, with the use of confocal microscopy images of rat brain vasculature. Several classes of brain tissue were clearly distinguished based on 3D texture numerical parameters, including control and different kinds of tumours - treated with NG2 proteoglycan to promote angiogenesis-dependent growth of the abnormal tissue. The method, applied to magnetic resonance imaging e.g. real neovasculature or retinal images can be used to support noninvasive medical diagnosis of vascular system diseases.

Automated modeling of tubular blood vessels in 3D MR angiography images

An algorithm is developed for automated modeling of tubular blood vessel segments, based on their noisy 3D raster image. The approach is based on continuous-function approximation of binary skeleton lines extracted from thresholded multiscale vesselness images. The continuous centerline functions allow robust computation of tangent vectors, to define normal planes and 3D image cross-sections on those planes. A vessel intensity profile model is next least-squares fitted to the image cross-section along straight lines segments - anchored at centerline and extended toward vessel walls, at a number of directions covering the full angle. Vessel parameters, such as local radius for circular vessels, distances between the centerline and edges for non-circular shapes or intensity profile corresponding to blood velocity distribution, are estimated through the model fitting. Subvoxel accuracy vessel representation, robustness to noise and image inhomogeneity are of primary concern. The algorithm is applied to 3D synthetic and real-life magnetic resonance images. It is demonstrated that the proposed method facilitates automated extraction of geometric vessel-tree models from images and outperforms the well-known Hessian vector approach in terms of accurate estimation of the centerline local direction in noisy images.

Level-set segmentation of noisy 3D images of numerically simulated blood vessels and vascular trees

The objective of this paper is to evaluate performance of the level set approach applied to segmentation and tracking of noisy 3D images of computer-simulated blood-vessel phantoms and artificial vascular trees. Of particular interest was the segmentation of thin vessels, with diameter smaller that voxel size. Flood fill technique was also explored, for comparison. Quantitative measures of segmentation accuracy were used for the methods evaluation. It was demonstrated, that the level set method provides good segmentation results even for noisy images. This promising result encourages its future application for vascularity modeling based on MR images.

Arteries tracking in simultaneous TOF-SWI MR images: image characteristics and preliminary results

The issue of vessel tracking in a novel, simultaneous ToF-SWI (time-of flight, susceptibility weighted imaging) 3D images is considered. Properties of the ToF-SWI images are discussed briefly. Results of arterial tree tracking by means of multiscale filtering, flod-fill and level set methods are presented and compared. Human brain and phantom MR images are used in the study. Topics of future research aimed at modeling and quantitative analysis of the vasculature, on the basis of segmentation of ToF-SWI, are addressed.

Assessment of Renal Function from 3D Dynamic Contrast Enhanced MR Images Using Independent Component Analysis

In this paper we present an automated, unsupervised, data-driven approach to assess renal function from 3D DCE-MR images. Applying independent component analysis to four different data sets acquired at different field strengths and with different measurement techniques, we show that functional regions in the human kidney can be recovered by a subset of independent components. Time intensity curves, reflecting perfusion in the kidney can be extracted from the processed data. The procedure may allow non-invasive, local assessment of renal function (e.g. glomerular filtration rate, GFR) from the image time series in future.

Centerline-based surface modeling of blood-vessel trees in cerebral 3D MRA

A technique is proposed for modeling the surface of normal cerebral vasculature based on three-dimensional magnetic resonance images. The Frangi multiscale image filtering is the starting point, followed by thresholding and skeletonization. The skeleton of tubular branches is approximated by a smooth function in 3D, allowing accurate estimation of tangent vector to the vessel centerline and planes normal to it. Vessel radius is then computed by least-squares fitting of the image intensity model to vessel cross-sections by normal planes. In effect, each tubular branch of the vessel tree is represented by centerline-radius description. The usage of Frangi filtering results in tubular branch discontinuities in places where the vessels do not feature the assumed elongated shape, e.g. at bifurcations and intensity artefacts. This paper proposes algorithms for modeling the vessel tree surface discontinuities. The resulting integrated surface of the macroscale (of diameter comparable or larger than the voxel side) vessels model is waterproof. This is important for future usage of the model for blood flow simulation. A network of mesoscale vessels (of diameter smaller than the voxel side) is synthesized at the branch terminations of the macroscale surface model, using constrained numerical optimization. This is a step toward modeling the whole brain vasculature.

Quantitative description of 3D vascularity images: texture-based approach and its verification through cluster analysis

This paper undertakes the problem of quantitative inspection of 3D vascular tree images. Through the use of cluster analysis, it confirms the correspondence between texture descriptors and various vessel system parameters, such as blood viscosity and the number of tree branches. Moreover, it is shown that unsupervised selection of significant texture parameters, especially in the synthetic data sets corresponding to noisy images, becomes feasible if the search for relevant attributes is guided by the clustering stability-based optimization criterion.

Cluster Analysis in Application to Quantitative Inspection of 3D Vascular Tree Images

This paper provides — through the use of cluster analysis — objective confirmation of the relevance of texture description applied to vascular tree images. Moreover, it is shown that unsupervised selection of significant texture parameters in the datasets corresponding to noisy images becomes feasible if the search for relevant attributes is guided by the clustering stability–based optimization criterion.

Texture analysis in application to quantitative study of 3D vascular tree images

It has been demonstrated recently by the authors that texture analysis of 3D images of vascular trees can be used to describe the trees quantitatively (see References section). Computer-simulated trees and their raster images were used in that study. This paper presents experimental confirmation of the findings using supervised and unsupervised classification methods.

Towards multi-scale personalized modeling of brain vasculature based on magnetic resonance image processing

A technique is proposed for personalized modeling of cerebral brain vasculature based on three-dimensional magnetic resonance images. High resolution ToF, QSM MR images were used to build 3D geometric models of arteries and veins. To make a next step towards modeling of the whole vascular system, a surface of gray matter was extracted from T1 weighted image. Then, within selected part of the cortex, a computer-synthesized blood vessels originating from nearby artery were built as mesoscopic part of the cerebral blood system. Limitations of the ToF and QSM-based approach to development of such a comprehensive model are pointed out and discussed.