Laura Reden

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.

Computer Vision and Pattern Recognition Techniques for 2-D and 3-D MR Cerebral Cortical Segmentation (Part I): A State-of-the-Art Review

Abstract: Extensive growth in functional brain imaging, perfusion-weighted imaging, diffusion-weighted imaging, brain mapping and brain scanning techniques has led tremendously to the importance of the cerebral cortical segmentation, both in 2-D and 3-D, from volumetric brain magnetic resonance imaging data sets. Besides that, recent growth in deformable brain segmentation techniques in 2-D and 3-D has brought the engineering community, such as the areas of computer vision, image processing, pattern recognition and graphics, closer to the medical community, such as to neuro-surgeons, psychiatrists, oncologists, neuro-radiologists and internists. This paper is an attempt to review the state-of-the-art 2-D and 3-D cerebral cortical segmentation techniques from brain magnetic resonance imaging based on three main classes: region-based, boundary/surface-based and fusion of boundary/surface-based with region-based techniques. In the first class, region-based techniques, we demonstrated more than 18 different techniques for segmenting the cerebral cortex from brain slices acquired in orthogonal directions. In the second class, boundary/surface-based, we showed more than ten different techniques to segment the cerebral cortex from magnetic resonance brain volumes. Particular emphasis will be placed by presenting four state-of-the-art systems in the third class, based on the fusion of boundary/surface-based with region-based techniques outlined in Part II of the paper, also called regional-geometric deformation models, which take the paradigm of partial differential equations in the level set framework. We also discuss the pros and cons of various techniques, besides giving the mathematical foundations for each sub-class in the cortical taxonomy.

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.

Fusion of Region and Boundary/Surface-Based Computer Vision and Pattern Recognition Techniques for 2-D and 3-D MR Cerebral Cortical Segmentation (Part-II): A State-of-the-Art Review

Abstract: Extensive growth in functional brain imaging, perfusion-weighted imaging, diffusion-weighted imaging, brain mapping and brain scanning techniques has led tremendously to the importance of cerebral cortical segmentation both in 2-D and 3-D from volumetric brain magnetic resonance imaging data sets. Besides that, recent growth in deformable brain segmentation techniques in 2-D and 3-D has brought the engineering community, such as the areas of computer vision, image processing, pattern recognition and graphics, closer to the medical community, such as to neuro-surgeons, psychiatrists, oncologists, neuro-radiologists and internists. In Part I of this research (see Suri et al [1]), an attempt was made to review the state-of-the-art in 2-D and 3-D cerebral cortical segmentation techniques from brain magnetic resonance imaging based on two main classes: region- and boundary/surface-based. More than 18 different techniques for segmenting the cerebral cortex from brain slices acquired in orthogonal directions were shown using region-based techniques. We also showed more than ten different techniques to segment the cerebral cortex from magnetic resonance brain volumes using boundary/surface-based techniques. This paper (Part II) focuses on presenting state-of-the-art systems based on the fusion of boundary/surface-based with region-based techniques, also called regional-geometric deformation models, which takes the paradigm of partial differential equations in the level set framework. We also discuss the pros and cons of these various techniques, besides giving the mathematical foundations for each sub-class in the cortical taxonomy. Special emphasis is placed on discussing the advantages, validation, challenges and neuro-science/clinical applications of cortical segmentation.

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).

MODELING SEGMENTATION VIA GEOMETRIC DEFORMABLE REGULARIZERS, PDE AND LEVEL SETS IN STILL AND MOTION IMAGERY: A REVISIT

Partial Differential Equations (PDEs) have dominated image processing research recently. The three main reasons for their success are: first, their ability to transform a segmentation modeling problem into a partial differential equation framework and their ability to embed and integrate different regularizers into these models; second, their ability to solve PDEs in the level set framework using finite difference methods; and third, their easy extension to a higher dimensional space. This paper is an attempt to survey and understand the power of PDEs to incorporate into geometric deformable models for segmentation of objects in 2D and 3D in still and motion imagery. The paper first presents PDEs and their solutions applied to image diffusion. The main concentration of this paper is to demonstrate the usage of regularizers in PDEs and level set framework to achieve the image segmentation in still and motion imagery. Lastly, we cover miscellaneous applications such as: mathematical morphology, computation of missing boundaries for shape recovery and low pass filtering, all under the PDE framework. The paper concludes with the merits and the demerits of PDEs and level set-based framework for segmentation modeling. The paper presents a variety of examples covering both synthetic and real world images.

A note on future research in segmentation techniques applied to neurology, cardiology, mammography and pathology

In previous chapters, we saw the application of segmentation in different areas of the body, such as the brain, heart, breast and cells. We covered many different kinds of models of CVGIP1 and PR2, but with the pace at which research in segmentation is progressing, this book would be incomplete if it did not also envision the future of segmentation techniques for the above mentioned areas. Therefore, we present in this chapter the future aspects of the segmentation techniques covered in this book.

Advances in computer vision, graphics, image processing and pattern recognition techniques for MR brain cortical segmentation and reconstruction: a review toward functional MRI (fMRI)

The importance of 2-D and 3-D brain segmentation has increased tremendously due to the recent growth in functional MRI (fMRI), perfusion-weighted imaging, diffusion-weighted imaging, volume graphics, 3-D segmentation, neurosurgical planning, navigation and MR brain scanning techniques. Besides that, recent growth in supervised and non-supervised brain segmentation techniques in 2-D (see Suri [322], Zavaljevski et al. [323], Barra et al. [324]) and 3-D (see Salle et al. [325], Kiebel et al. [326], Zeng et al. [327], Xu et al. [606], Fischl et al. [328], Linden et al. [329], Stokking [330], Smith [331], Hurdal [332] and ter Haar et al. [333]) have brought the engineering community, in areas such as computer vision, graphics, image processing (CVGIP) and pattern recognition, closer to the medical community, such as neuro-surgeons, psychiatrists, psychologists, physiologists, oncologists, radiologists and internists. This chapter is an attempt to review state-of-the-art cortical segmentation techniques in 2-D and 3-D using magnetic resonance imaging (MRI), and their applications. New challenges in this area are also discussed.

MR Breast Perfusion Analysis System (BPAS)

Given the pre- and post-contrast gadolinium magnetic resonance (MR) images of any body organ, the uptake curve estimation has diagnostic utility in the area of medicine. The rate of absorption of the contrast agent (gadolinium) by lesions can show what type of lesion it is and can also be an indication of the malignancy stage. The differences in contrast enhancement rates can be used to distinguish between benign and malignant lesions. Oncologists/radiologists and internists can classify the type of malignancy by looking at the quantitative characteristics of the tissue signal enhancement. The slopes of the uptake curve can indicate the level and type of malignancy. Such a classification is called functional segmentation. This paper presents the user-friendly MR Breast Perfusion Analysis System (BPAS), based on Motif using C/C++ and X Windows libraries, that runs on Digital Unix and XP1000 workstations supporting the Unix and Linux operating systems, respectively. We tested our software on 20 patient studies from the data collected from two major sites in the USA and Europe.

Basic principles of image generation for ultrasound, X-rays, magnetic resonance, computed tomography and positron emission tomography

The usual aim in generating any kind of image in a body organ is to decide whether or not an abnormality is present and/or to follow the temporal variations of the abnormality during the course of a therapeutic treatment. Therefore, the two main goals of a human expert observer are: to detect the abnormality and to recognize it as such. Logically, the detection must occur prior to any useful recognition, however both procedures can be aided by improving the quality of the image by means of any post-processing techniques.