Swamy Laxminarayan

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.

UNWIRED E-MED: the next generation of wireless and internet telemedicine systems

The movement of telemedicine to wireless and mobile Internet applications is imminent in the next few years. This migration from desktop platforms to wireless and mobile configurations will have a significant impact on future healthcare delivery systems and their globalization. Recent telecommunications and biomedical computing advances will significantly enhance the current methodologies of telemedicine and telecare systems. This editorial will present some of the evolutionary issues and important aspects that have to be considered in developing technologies for the next generation of Internet and third generation of mobile systems geared for future telemedical applications. These will provide new dimensions to existing medical services and areas of outreach that are not possible with the current generation that will have tremendous impact on how healthcare delivery will be shaped for the 21st century.

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.

Biomedical information technology: medicine and health care in the digital future

Advancements in medicine and health care are being significantly influenced by the exploding information technology developments. The IEEE Transactions on Information Technology in Biomedicine will address the applications and the infrastructure innovations that would harness biomedical and health care programs in the 21st century.

PDE and Level Sets: Algorithmic Approaches to Static and Motion Imagery

The Contributors. 1. Review of PDEs and Level Sets. 2. Level Set Extentions, Flows and Crack Propagation 3. Geometric Regularizers for Level Sets/PDE Image Processing 4. Partial Differential Equations in Image Processing. 5. Segmentation of Motion Imagery Using PDEs. 6. Motion Image Segmentation Using Deformable Models. 7. Medical Image Segmentation Using Level Sets and PDEs. 8. Subjective Surfaces. 9. The Future of PDEs and Level Sets. 10. Index Words.

Status of Mobile Computing in Health Care: An Evidence Study

Information technology (IT) has played an impacting role in helping healthcare professionals increasingly recognize the value of IT in clinical medicine, disease management, treatment strategies and other related areas. Of the numerous IT applications currently pursued, the use of telemedicine is a major force in addressing the healthcare delivery issues. As evidenced by a number of studies, telemedicine has come to play a significant mode of delivery and treatment, especially in the remote rural areas. Despite the growing interest, the R&D advances and the vast number of telemedicine applications currently ongoing, there still seem to be only a few quantitative studies characterizing the economic aspects of telemedicine and how cost-effective such solutions are. Of particular interest to the healthcare community is the effectiveness of the emerging mobile communications in telemedicine especially in home healthcare environment. In this paper we report on a profile of the current status of the use of mobile devices based on an extensive literature search of 213 articles. In particular we have focused our research on reports of the cost effectiveness of mobile telemedicine applications with respect to improved quality of care and medical error reduction. Preliminary results are reported in the paper, with an extended discussion to be provided in the final conference presentation, based on outcomes obtained from a more robust data analysis.

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).

A comparison of state-of-the-art diffusion imaging techniques for smoothing medical/non-medical image data

Partial differential equations (PDEs) have dominated image processing research. The three main reasons for their success are: (1) 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; (2) their ability to solve PDEs in the level set framework using finite difference methods; and (3) their easy extension to a higher dimensional space. The paper is an attempt to summarize PDEs and their solutions applied to image diffusion. The paper first presents the fundamental diffusion equation. Next, the multi-channel anisotropic diffusion imaging is presented, followed by tensor non-linear anisotropic diffusion. We also present the anisotropic diffusion based on PDE and the Tukey/Huber weight function for image noise removal. The paper also covers the recent growth of image denoising using the curve evolution approach and image denoising using histogram modification based on PDE. Finally, the paper presents non-linear image denoising. Examples covering both synthetic and real world images are presented.

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.

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.