Kecheng Liu

A review on MR vascular image processing: skeleton versus nonskeleton approaches: part II

Vascular segmentation has recently been given much attention. This review paper has two parts. Part I of this review focused on the physics of magnetic resonance angiography (MRA) and prefiltering techniques applied to MRA. Part II of this review presents the state-of-the-art overview, status, and new achievements in vessel segmentation algorithms from MRA. The first part of this review paper is focused on the nonskeleton or direct-based techniques. Here, we present eight different techniques along with their mathematical foundations, algorithms and their pros and cons. We will also focus on the skeleton or indirect-based techniques. We will discuss three different techniques along with their mathematical foundations, algorithms and their pros and cons. This paper also includes a clinical discussion on skeleton versus nonskeleton-based segmentation techniques. Finally, we shall conclude this paper with the possible challenges, the future, and a brief summary on vascular segmentation techniques.

A review on MR vascular image processing algorithms: acquisition and prefiltering: part I

Vascular segmentation has recently been given much attention. This review paper has two parts. Part I focuses on the physics of magnetic resonance angiography (MRA) generation and prefiltering techniques applied to MRA data sets. Part II of the review focuses on the vessel segmentation algorithms. The first section of this paper introduces the five different sets of receive coils used with the MRI system for magnetic resonance angiography data acquisition. This section then presents the five different types of the most popular data acquisition techniques: time-of-flight (TOF), phase-contrast, contrast-enhanced, black-blood, T2-weighted, and T2*-weighted, along with their pros and cons. Section II of this paper focuses on prefiltering algorithms for MRA data sets. This is necessary for removing the background nonvascular structures in the MRA data sets. Finally, the paper concludes with a clinical discussion on the challenges and the future of the data acquisition and the automated filtering algorithms.

White and black blood volumetric angiographic filtering: ellipsoidal scale-space approach

Prefiltering is a critical step in three-dimensional (3D) segmentation of a blood vessel and its display. This paper presents a scale-space approach for filtering white blood and black blood angiographic volumes and its implementation issues. The raw MR angiographic volume is first converted to isotropic volume followed by 3D higher order separable Gaussian derivative convolution with known scales to generate edge volume. The edge volume is then run by the directional processor at each voxel where the eigenvalues of the 3D ellipsoid are computed. The vessel score per voxel is then estimated based on these three eigenvalues which suppress the nonvasculature and background structures yielding the filtered volume. The filtered volume is ray-cast to generate the maximum intensity projection images for display. The performance of the system is evaluated by computing the mean, variance, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) images. The system is run over 20 patient studies from different areas of the body such as the brain, abdomen, kidney, knee, and ankle. The computer program takes around 150 s of processing time per study for a data size of 512 /spl times/ 512 /spl times/ 194, which includes the complete performance evaluation. We also compare our strategy with the recently published MR filtering algorithms by Alexander et al. (2000) and Sun et al. (1999).

Level set regularizers for shape recovery in medical images

Due to the recent growth of level sets and partial differential equation (PDE) based approaches, the importance of designing regularizers has risen steadily. This paper presents a classification tree for regularizers and the design of regularization forces for the robust segmentation of static and motion imagery.

Automatic local effect of window/level on 3D scale-space ellipsoidal filtering on run-off-arteries from white blood magnetic resonance angiography

Pre-filtering is a critical step in 3D segmentation of a blood vessel and its display. This paper presents the local effect of window/level over the 3D scale-space approach for filtering the white blood angiographic volumes and its implementation issues. The raw MR angiographic volume is first converted to an isotropic volume, then the window/level is automatically adjusted slice by slice and a composite volume is generated. 3D edges are then generated using separable Gaussian derivative convolution with known scales. The edge volume is then run by the directional processor at each voxel where the eigenvalues of the 3D ellipsoid are computed. The vessel score per voxel is then estimated based on these three eigenvalues which suppress the non-vasculature and background structures, yielding the filtered volume. The filtered volume is ray-cast to generate the maximum intensity projection images for display. The performance of the system is evaluated by computing the mean, variance, SNR and CNR images. We compare the filtering results with and without the usage of the local effect of window/level over 3D scale-space ellipsoidal filtering. We show that the automatic window/level is effective in detecting small vessels which are otherwise difficult to extrapolate. The system was run over 20 patient studies from different parts of the body such as brain, abdomen, kidney, knee, and ankle. The computer program takes around 150 seconds of processing time per study for a study with a data size of 512 /spl times/ 512 /spl times/ 194, which includes complete performance evaluation.