3D MR image analysis of the morphology of the rear foot: application to classification of bones

Stindel, Éric; Udupa, Jayaram K.; Hirsch, Bruce Elliot; Odhner, Dewey; Couture, Christine

doi:10.1016/s0895-6111(98)00070-6

Cited by 46 publications

(26 citation statements)

References 12 publications

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“…For our experiments, we have chosen one object [the talus bone of the human foot, see Fig. 2(a)] in one of our ongoing applications, the kinematic analysis of the tarsal joints of the foot based on MR images [10], [11], [12]. This was one of the objects used in the past to evaluate the previous live methods [6], [17].…”

Section: Discussionmentioning

confidence: 99%

An ultra-fast user-steered image segmentation paradigm: live wire on the fly

Falcão

Udupa

Miyazawa

2000

IEEE Trans. Med. Imaging

248

154

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Abstract-We have been developing general user steered image segmentation strategies for routine use in applications involving a large number of data sets. In the past, we have presented three segmentation paradigms: live wire, live lane, and a three-dimensional (3-D) extension of the live-wire method. In this paper, we introduce an ultra-fast live-wire method, referred to as live wire on the fly, for further reducing user's time compared to the basic live-wire method. In live wire, 3-D/four-dimensional (4-D) object boundaries are segmented in a slice-by-slice fashion. To segment a two-dimensional (2-D) boundary, the user initially picks a point on the boundary and all possible minimum-cost paths from this point to all other points in the image are computed via Dijkstra's algorithm. Subsequently, a live wire is displayed in real time from the initial point to any subsequent position taken by the cursor. If the cursor is close to the desired boundary, the live wire snaps on to the boundary. The cursor is then deposited and a new live-wire segment is found next. The entire 2-D boundary is specified via a set of live-wire segments in this fashion. A drawback of this method is that the speed of optimal path computation depends on image size. On modestly powered computers, for images of even modest size, some sluggishness appears in user interaction, which reduces the overall segmentation efficiency. In this work, we solve this problem by exploiting some known properties of graphs to avoid unnecessary minimum-cost path computation during segmentation. In live wire on the fly, when the user selects a point on the boundary the live-wire segment is computed and displayed in real time from the selected point to any subsequent position of the cursor in the image, even for large images and even on low-powered computers. Based on 492 tracing experiments from an actual medical application, we demonstrate that live wire on the fly is 1.3-31 times faster than live wire for actual segmentation for varying image sizes, although the pure computational part alone is found to be about 120 times faster.

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Section: Discussionmentioning

confidence: 99%

An ultra-fast user-steered image segmentation paradigm: live wire on the fly

Falcão

Udupa

Miyazawa

2000

IEEE Trans. Med. Imaging

248

154

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“…Methods are also implemented in 3DVIEWNIX for various intensity-based measures [6]. We have developed methods for higherlevel analysis of object systems by describing the morphology of individual objects through morphological parameters, the inter-relationship among objects through parameters describing the architecture of the object system, and the way this inter-relationship changes when the objects move through kinematic parameters [27][28][29]. …”

Section: Discussionmentioning

confidence: 99%

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

et al. 2007

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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.

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“…In the following ten currently ongoing applications, 100s, and in some applications 1000s, of datasets have been routinely processed. Kinematic analysis of joints [8][9][10][11][12][13][14][15] and the study of joint cartilage for the investigation of osteoarthritis [16], both via MRI: In the former, our goal is to study in vivo the normal kinematics of joints (tarsal, ankle, and glenohumeral), how these are affected by joint pathologies and soft tissue injuries, and how surgical procedures are effective in restoring normal function. In the latter, our aim is to understand the MR imaging and morphological properties of the joint cartilage in an attempt to study osteoarthritis and its treatment effects.…”

Section: Introductionmentioning

confidence: 99%

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

Grevera

Udupa

Odhner

et al. 2007

Medical Imaging 2007: Visualization and Image-Guided Procedures

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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.

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3D MR image analysis of the morphology of the rear foot: application to classification of bones

Cited by 46 publications

References 12 publications

An ultra-fast user-steered image segmentation paradigm: live wire on the fly

An ultra-fast user-steered image segmentation paradigm: live wire on the fly

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

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

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