Shipra Mishra

CAVASS: A Computer-Assisted Visualization and Analysis Software System

The Medical Image Processing Group at the University of Pennsylvania has been developing (and distributing with source code) medical image analysis and visualization software systems for a long period of time. Our most recent system, 3DVIEWNIX, was first released in 1993. Since that time, a number of significant advancements have taken place with regard to computer platforms and operating systems, networking capability, the rise of parallel processing standards, and the development of open-source toolkits. The development of CAVASS by our group is the next generation of 3DVIEWNIX. CAVASS will be freely available and open source, and it is integrated with toolkits such as Insight Toolkit and Visualization Toolkit. CAVASS runs on Windows, Unix, Linux, and Mac but shares a single code base. Rather than requiring expensive multiprocessor systems, it seamlessly provides for parallel processing via inexpensive clusters of work stations for more time-consuming algorithms. Most importantly, CAVASS is directed at the visualization, processing, and analysis of 3-dimensional and higher-dimensional medical imagery, so support for digital imaging and communication in medicine data and the efficient implementation of algorithms is given paramount importance.

Fuzzy object modeling

To make Quantitative Radiology (QR) a reality in routine clinical practice, computerized automatic anatomy recognition (AAR) becomes essential. As part of this larger goal, we present in this paper a novel fuzzy strategy for building bodywide group-wise anatomic models. They have the potential to handle uncertainties and variability in anatomy naturally and to be integrated with the fuzzy connectedness framework for image segmentation. Our approach is to build a family of models, called the Virtual Quantitative Human, representing normal adult subjects at a chosen resolution of the population variables (gender, age). Models are represented hierarchically, the descendents representing organs contained in parent organs. Based on an index of fuzziness of the models, 32 thorax data sets, and 10 organs defined in them, we found that the hierarchical approach to modeling can effectively handle the non-linear relationships in position, scale, and orientation that exist among organs in different patients.

Feasibility of estimation of brain volume and 2-deoxy-2- 18 F-fluoro-D-glucose metabolism using a novel automated image analysis method: Application in Alzheimer's disease

The development of clinically-applicable quantitative methods for the analysis of brain fluorine-18 fluoro desoxyglucose-positron emission tomography ((18)F-FDG-PET) images is a major area of research in many neurologic diseases, particularly Alzheimers disease (AD). Region of interest visualization, evaluation, and image registration (ROVER) is a novel commercially-available software package which provides automated partial volume corrected measures of volume and glucose uptake from (18)F-FDG PET data. We performed a pilot study of ROVER analysis of brain (18)F-FDG PET images for the first time in a small cohort of patients with AD and controls. Brain (18)F-FDG-PET and volumetric magnetic resonance imaging (MRI) were performed on 14 AD patients and 18 age-matched controls. Images were subjected to ROVER analysis, and voxel-based analysis using SPM5. Volumes by ROVER were 35% lower than MRI volumes in AD patients (as hypometabolic regions were excluded in ROVER-derived volume measurement ) while average ROVER- and MRI-derived cortical volumes were nearly identical in control population. Whole brain volumes when ROVER-derived and whole brain metabolic volumetric products (MVP) were significantly lower in AD and accurately distinguished AD patients from controls (Area Under the Curve (AUC) of Receiver Operator Characteristic (ROC) curves 0.89 and 0.86, respectively). This diagnostic accuracy was similar to voxel-based analyses. Analysis by ROVER of (18)F-FDG-PET images provides a unique index of metabolically-active brain volume, and can accurately distinguish between AD patients and controls as a proof of concept. In conclusion, our findings suggest that ROVER may serve as a useful quantitative adjunct to visual or regional assessment and aid analysis of whole-brain metabolism in AD and other neurologic and psychiatric diseases.

CAVASS: a computer assisted visualization and analysis software system - visualization aspects

The Medical Image Processing Group (MIPG) at the University of Pennsylvania has been developing and distributing medical image analysis and visualization software systems for a long period of time. Our most recent system, 3DVIEWNIX, was first released in 1993. Since that time, a number of significant advancements have taken place with regard to computer platforms and operating systems, networking capability, the rise of parallel processing standards, and the development of open source toolkits. The development of CAVASS by our group is the next generation of 3DVIEWNIX. CAVASS will be freely available, open source, and is integrated with toolkits such as ITK and VTK. CAVASS runs on Windows, Unix, and Linux but shares a single code base. Rather than requiring expensive multiprocessor systems, it seamlessly provides for parallel processing via inexpensive COWs (Cluster of Workstations) for more time consuming algorithms. Most importantly, CAVASS is directed at the visualization, processing, and analysis of medical imagery, so support for 3D and higher dimensional medical image data and the efficient implementation of algorithms is given paramount importance. This paper focuses on aspects of visualization. All of the most of the popular modes of visualization including various 2D slice modes, reslicing, MIP, surface rendering, volume rendering, and animation are incorporated into CAVASS.

CAVASS: a computer-assisted visualization and analysis software system - image processing aspects

The development of the concepts within 3DVIEWNIX and of the software system 3DVIEWNIX itself dates back to the 1970s. Since then, a series of software packages for Computer Assisted Visualization and Analysis (CAVA) of images came out from our group, 3DVIEWNIX released in 1993, being the most recent, and all were distributed with source code. CAVASS, an open source system, is the latest in this series, and represents the next major incarnation of 3DVIEWNIX. It incorporates four groups of operations: IMAGE PROCESSING (including ROI, interpolation, filtering, segmentation, registration, morphological, and algebraic operations), VISUALIZATION (including slice display, reslicing, MIP, surface rendering, and volume rendering), MANIPULATION (for modifying structures and surgery simulation), ANALYSIS (various ways of extracting quantitative information). CAVASS is designed to work on all platforms. Its key features are: (1) most major CAVA operations incorporated; (2) very efficient algorithms and their highly efficient implementations; (3) parallelized algorithms for computationally intensive operations; (4) parallel implementation via distributed computing on a cluster of PCs; (5) interface to other systems such as CAD/CAM software, ITK, and statistical packages; (6) easy to use GUI. In this paper, we focus on the image processing operations and compare the performance of CAVASS with that of ITK. Our conclusions based on assessing performance by utilizing a regular (6 MB), large (241 MB), and a super (873 MB) 3D image data set are as follows: CAVASS is considerably more efficient than ITK, especially in those operations which are computationally intensive. It can handle considerably larger data sets than ITK. It is easy and ready to use in applications since it provides an easy to use GUI. The users can easily build a cluster from ordinary inexpensive PCs and reap the full power of CAVASS inexpensively compared to expensive multiprocessing systems which are less efficient for CAVA operations.

Introducing CAVASS: a Computer-Assisted Visualization and Analysis Software System

The Medical Image Processing Group (MIPG) at the University of Pennsylvania has been developing (and distributing with source code) medical image analysis and visualization software systems for a long period of time. Our most recent system, 3DVIEWNIX, was first released in 1993. Since that time, a number of significant advancements have taken place with regard to computer platforms and operating systems, networking capability, the rise of parallel processing standards, and the development of open source toolkits. The development of CAVASS by our group is the next generation of 3DVIEWNIX. CAVASS will be freely available, open source, and is integrated with toolkits such as ITK and VTK. CAVASS runs on Windows, Unix, and Linux but shares a single code base. Rather than requiring expensive multiprocessor systems, it seamlessly provides for parallel processing via inexpensive COWs (Cluster of Workstations) for more time consuming algorithms. Most importantly, CAVASS is directed at the visualization, processing, and analysis of 3D and higher dimensional medical imagery, so support for DICOM data and the efficient implementation of algorithms is given paramount importance.