Medical Volume Data Research Articles

Bitplane Based Volume Morphology for 3-D Image Speckle Noise Suppressing

In 3D image processing, such as medical volume data and industrial CT volume data analysis, speckle noise suppressing significantly affects their accuracy. This paper utilizes a group of volume morphology arithmetic operators, mainly including volume open and volume close, by extending area morphology into 3-D space. Using these operators, the light and dark objects of small size could be removed directly from the 3-D space of the target objects, while the connectivity of the main 3-D target objects in the volume data is still preserved. To improve the volume morphology operations efficiency, we decompose volume data in bitplanes instead of in gray scale space to reduce the binary volumes. To verify the effect of the improved volume morphology operators, they are applied to suppress speckle noises in 3-D images of coral and rat skull and compared with original volume morphology operations. Experimental results show that the algorithm proposed in this paper can significantly reduce computing time, while maintaining comparable results to original operations results.

PARTICLE-BASED TRANSPARENT FUSED VISUALIZATION APPLIED TO MEDICAL VOLUME DATA

This paper proposes a method to create 3D fusion images, such as volume–volume, volume–surface, and surface–surface fusion. Our method is based on the particle-based rendering, which uses tiny particles as rendering primitives. The method can create natural and comprehensible 3D fusion images simply by merging particles prepared for each element to be fused. Moreover, the method does not require particle sorting along the line of sight to realize right depth feel. We apply our method to realize comprehensible visualization of medical volume data.

Three-dimensional Fuzzy Kernel Regression framework for registration of medical volume data

In this work a general framework for non-rigid 3D medical image registration is presented. It relies on two pattern recognition techniques: kernel regression and fuzzy c-means clustering. The paper provides theoretic explanation, details the framework, and illustrates its application to implement three registration algorithms for CT/MR volumes as well as single 2D slices. The first two algorithms are landmark-based approaches, while the third one is an area-based technique. The last approach is based on iterative hierarchical volume subdivision, and maximization of mutual information. Moreover, a high performance Nvidia CUDA based implementation of the algorithm is presented.The framework and its applications were evaluated with a number of tests, which show that the proposed approaches achieve valuable results when compared with state-of-the-art techniques.Additional assessment was taken by expert radiologists, providing performance feedback from the final user perspective.

GPU-ACCELERATED INTERACTIVE VISUALIZATION OF 3D VOLUMETRIC DATA USING CUDA

Improving the image quality and the rendering speed have always been a challenge to the programmers involved in large scale volume rendering especially in the field of medical image processing. The paper aims to perform volume rendering using the graphics processing unit (GPU), in which, with its massively parallel capability has the potential to revolutionize this field. This work is now better with the help of GPU accelerated system. The final results would allow the doctors to diagnose and analyze the 2D computed tomography (CT) scan data using three dimensional visualization techniques. The system is used in multiple types of datasets, from 10 MB to 350 MB medical volume data. Further, the use of compute unified device architecture (CUDA) framework, a low learning curve technology, for such purpose would greatly reduce the cost involved in CT scan analysis; hence bring it to the common masses. The volume rendering has been done on Nvidia Tesla C1060 (there are 240 CUDA cores, which provides execution of data parallely) card and its performance has also been benchmarked.

Research and Realization of Medical Volume Data Interactive Cutting

Medical image visualization is an important field of scientific computing visualization. Three-dimensional segmentation plays a very important role in three-dimensional reconstruction of human tissues and region of interest. In order to observe the information of human internal tissues better and provide reliable basis for clinical diagnosis, pathological analysis and treatment, it is indispensable to do cutting on volume data. Virtual cutting is usually carried out with the user's interactive operations after three-dimensional reconstruction. This paper does research on the algorithms of cutting volume data, and proposes a series of solutions including plane cutting, bounding box cutting, ball cutting and contour surface cutting. Experiment results indicate that the algorithms are practical and efficient, which can reach favorable virtual cutting effect. By this meaning, doctors can know the internal information of volume data more clearly. Meanwhile, the amount of computation reduced.

Medical Data Visualization via a Pervasive Multi-Agent Platform

Pervasive healthcare is the field of application emerging from the combination of healthcare with pervasive computing, which is the computing paradigm that provides users with access to services in a transparent way, wherever they are and whichever their interacting device is. In this paper, a software infrastructure for pervasive healthcare is presented. Such an infrastructure aims at supporting medical practitioners with advanced pervasive access to medical data, which is also context-aware in the sense that the modality to fruit data depends on the device used by the operator and on his or her physical position within the environment. The paper also describes a service for high quality 3D rendering of medical volume data, which takes advantage of the software infrastructure to distribute the computational load upon the devices available in the environment in a completely transparent way to users.

Interactive curved planar reformation based on snake model

Visualization of tortuous tissues such as tracheas plays a very important role in medical image processing. Displaying them in a curved plane for diagnosis is a better function which is called curved planar reformation (CPR). In this manuscript, we use snake model to generate contours of tubular structures in all slices selected by users of medical volume data sets. Then, centerline of these structures can be obtained. CPR of these structures segmented by snake model is realized based on the obtained contours and centerlines. We use B-spline to generate a contour along tubular structures through points determined by mouse in three view planes. An improved method is used to initialize a snake contours before using gradient vector flow (GVF) snake model generating the final contour. A head aneurysm data set scanned with computed tomography (CT) is used to illustrate the performance of our CPR method. Furthermore some enhancements are introduced to our method: adjustment of window width and window level, rotating CPR.

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Exploration of Medical Volume Data

Exploration of Medical Volume Data

Acquisition, Analysis, and Interpretation of Medical Volume Data

Acquisition, Analysis, and Interpretation of Medical Volume Data

High performance approach for inner structures visualisation in medical data

The capacity and fidelity of image-based diagnosis were extended due to the evolution of medical image acquisition techniques. Such data is usually visualised through volume rendering, which denotes a set of techniques used to present three-dimensional images with the main goal of showing the interior of a volume and enabling the identification of its inner regions and structures. Several tools described in the literature are dedicated to explore different ways of incorporating seeing-through capabilities into volume rendering techniques. However, these tools are based on visualisation algorithms and are usually computationally intensive, especially when working with large datasets. An alternative to optimise rendering time is to use high performance programming to implement such tools, thus providing a faster response to user interaction. This paper presents a new approach to visualise inner structures in medical volume data using a parallel ray casting algorithm to allow user interaction with the volume.

Analysis of kernel method for surface curvature estimation

Abstract We examine a method of curvature estimation that computes the curvature directly from three dimensional data. We refer to this as the kernel method of curvature estimation and our experiments indicate that several parameters be modified from those originally suggested to achieve more accurate and reliable results. This improved performance is essential for analysis of medical volume data by computer aided diagnosis algorithms, many of which use curvature estimation for shape computations and pattern recognition. We also examine cases in which the kernel method yields inaccurate responses based on specific topologies.

Objective Evaluation of Three-dimensional Image Registration Algorithms – Tools for Optimization and Evaluation

The registration of medical volume data sets plays an important role when different images or modalities are used during computer-assisted surgical procedures. Nevertheless, it is often questionable how robust and accurate the underlying algorithms really are. Therefore, the goal is to foster the establishment of methods for an objective evaluation. To reliably calculate the accuracy of registration algorithms, a reference transformation must be known. Due to the unknown perfect registration for real clinical data, the simulation of realistic data and successive affine transformations are employed. The simulation is based on models of the respective imaging modality where the dominant physical effects are taken into account. This gives the user full control over all simulation and transformation parameters. Finally, suitable quality measures are applied which allow a systematic evaluation of image registration accuracy by comparing the known theoretical result and the transformation calculated by the algorithm under investigation. During the development of a new registration algorithm, the presented method proved to be a very valuable tool for optimization and evaluation of registration accuracy, since it allows objective numerical comparison of the calculated results. The presented method can be used during the development of algorithms for optimization and for quantitative comparison of different registration schemes. The respective software tool can automatically generate and transform simulated but realistic data. Employing suitable numerical quality measures, an objective evaluation of registration results can be easily obtained. Still, the validity of the relatively simple models has to be verified to draw reliable conclusions with respect to real data.

Nonphotorealistic rendering of medical volume data

The article introduces volumetric hatching, a novel technique that produces pen-and-ink-style images from medical volume data. Unlike previous approaches that generate full-surface models, our technique uses characteristics of the volume near the stroke being produced to generate a local intermediate surface. Because global isosurfaces can't exactly model many medical subjects, our volume-based method has considerable advantages. Our method is largely insensitive to surface artifacts. We focus on hatching with line strokes to portray muscles, intestines, brains, and so on. Hatching with line strokes requires determining not just the position of the line strokes, but also their orientation. Thus, the strokes not only illustrate the subject's shape, but also describe its character in some way - for example, by displaying fiber orientations for muscles.

A topology-based filling algorithm

In this paper we present a fast-recursive fully automated topology-based algorithm for filling in the interiors of objects that appear in a binary image. The algorithm is based on two topological properties of the image contours: perimeter coincidence and interiority. Based on these parameters the algorithm performs a tree-structured region classification. The final filling is done according to the labels of the tree leaves. The algorithm is well suited to cases in which contours overlap and ambiguities may arise about which regions to fill. The algorithm has been tested with three-dimensional solid objects given by surface representations, both for simple man-made CAD objects and for complicated images taken from human computer tomographies. A table shows the execution times taken by the algorithm for several medical volume data sets.

Growing-cube isosurface extraction algorithm for medical volume data

In medical applications, three-dimensional volume data such as CT and MRI are gathered from medical-imaging devices. Marching cube (MC) algorithm is a common routine to extract isosurfaces from volume data. The MC algorithm generates the massive number of triangles to represent an isosurface. It is difficult to render this amount of triangles in real-time on general workstations. In this paper, we present a growing-cube algorithm to reduce the number of triangles generated by the MC algorithm. Growing-cube algorithm uses a surface tracker to avoid exhaustive searching isosurfaces cell-by-cell and, therefore, it saves computation time. During surface tracking, the growing-cube algorithm adaptively merges surfaces contained in the tracked cells to reduce the number of triangles. Surfaces are merged as long as the error is within user-specified error thresholds. Therefore, the proposed algorithm can generate a variable resolution of isosurfaces according to these error parameters.

Tissue classification based on 3D local intensity structures for volume rendering

This paper describes a novel approach to tissue classification using three-dimensional (3D) derivative features in the volume rendering pipeline. In conventional tissue classification for a scalar volume, tissues of interest are characterized by an opacity transfer function defined as a one-dimensional (1D) function of the original volume intensity. To overcome the limitations inherent in conventional 1D opacity functions, we propose a tissue classification method that employs a multidimensional opacity function, which is a function of the 3D derivative features calculated from a scalar volume as well as the volume intensity. Tissues of interest are characterized by explicitly defined classification rules based on 3D filter responses highlighting local structures, such as edge, sheet, line, and blob, which typically correspond to tissue boundaries, cortices, vessels, and nodules, respectively, in medical volume data. The 3D local structure filters are formulated using the gradient vector and Hessian matrix of the volume intensity function combined with isotropic Gaussian blurring. These filter responses and the original intensity define a multidimensional feature space in which multichannel tissue classification strategies are designed. The usefulness of the proposed method is demonstrated by comparisons with conventional single-channel classification using both synthesized data and clinical data acquired with CT (computed tomography) and MRI (magnetic resonance imaging) scanners. The improvement in image quality obtained using multichannel classification is confirmed by evaluating the contrast and contrast-to-noise ratio in the resultant volume-rendered images with variable opacity values.

Classification and Review onD Visualization Algorithms to Medical Volume Data

Classification and Review onD Visualization Algorithms to Medical Volume Data

An interactive visualization and navigation tool for medical volume data

In order to make direct volume rendering practicable convenient visualization options and data analysis tools have to be integrated. For example, direct rendering of semi-transparent volume objects with integrated display of lighted iso-surfaces is one important challenge especially in medical applications. Furthermore, explicit use of multi-dimensional image processing operations often helps to optimize the exploration of the available data sets. On the other hand, only if interactive frame rates can be guaranteed, such visualization tools will be accepted in medical planning and surgery simulation systems. In this paper we propose a volume visualization tool for large scale medical volume data which takes advantage of hardware-assisted 3D texture interpolation and convolution operations. We demonstrate how to use the 3D texture mapping capability of high-end graphics workstations to display arbitrary iso-surfaces which can be directly illuminated to enhance the spatial relations between objects. Back-to-front 3D texture slicing is used to simultaneously display semi-transparent material densities. In order to enable on-the-fly data analysis, first approaches using hardware-assisted-convolution operations have been integrated. An implementation of the proposed method based on the OpenInventor rendering toolkit is described offering interactive frame rates at high image quality including sophisticated user interactions.

Multimodality visualization of medical volume data

New developments in 3-D volume acquisitions are creating a rapidly increasing demand for integrating multimodality 3-D visualization. In order to accomplish routine clinical multimodality visualization, many issues have to be dealt with, such as techniques for accurate spatial registration, integrated representation, suitable graphical user interfaces, and obtaining adequate rendering speeds. The aim of this experience paper is 2-fold. First, it presents various results from our research on multimodality visualization/registration. Second, this paper explicitly addresses practical problems and findings related to software development and multimodality registration/visualization. We hope that this will give colleagues a better understanding in some of these issues based on our experience, including notably our mistakes.