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Publications (6)15.73 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: Osteophyte is an additional bony growth on a normal bone surface limiting or stopping motion at a deteriorating joint. Detection and quantification of osteophytes from computed tomography (CT) images is helpful in assessing disease status as well as treatment and surgery planning. However, it is difficult to distinguish between osteophytes and healthy bones using simple thresholding or edge/texture features due to the similarity of their material composition. In this paper, we present a new method primarily based on the active shape model (ASM) to solve this problem and evaluate its application to the anterior cruciate ligament transaction (ACLT) rabbit femur model via micro-CT imaging. The common idea behind most ASM-based segmentation methods is to first build a parametric shape model from a training dataset and then apply the model to find a shape instance in a target image. A common challenge with such approaches is that a diseased bone shape is significantly altered at regions with osteophyte deposition misguiding an ASM method and eventually leading to suboptimum segmentations. This difficulty is overcome using a new partial-ASM method that uses bone shape over healthy regions and extrapolates it over the diseased region according to the underlying shape model. Finally, osteophytes are segmented by subtracting partial-ASM-derived shape from the overall diseased shape. Also, a new semiautomatic method is presented in this paper for efficiently building a 3-D shape model for an anatomic region using manual reference of a few anatomically defined fiducial landmarks that are highly reproducible on individuals. Accuracy of the method has been examined on simulated phantoms while reproducibility and sensitivity have been evaluated on micro-CT images of 2-, 4- and 8-week post-ACLT and sham-treated rabbit femurs. Experimental results have shown that the method is highly accurate ( R <sup>bm 2</sup>=0.99), reproducible (ICC = 0.97), and sensitive in detecting dis- - ease progression (p values: 0.065, 0.001, and <;0.001 for 2 weeks versus 4 weeks, 4 weeks versus 8 weeks, and 2 weeks versus 8 weeks, respectively).
    IEEE Transactions on Biomedical Engineering 09/2011; · 2.35 Impact Factor
  • Bone 01/2011; 48. · 4.46 Impact Factor
  • Bone 01/2011; 48. · 4.46 Impact Factor
  • Bone 06/2010; 47. · 4.46 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Osteophyte is an additional bony growth on a normal bone surface limiting or stopping motion in a deteriorating joint. Detection and quantification of osteophytes from CT images is helpful in assessing disease status as well as treatment and surgery planning. However, it is difficult to segment osteophytes from healthy bones using simple thresholding or edge/texture features in CT imaging. Here, we present a new method, based on active shape model (ASM), to solve this problem and evaluate its application to ex vivo muCT images in an ACLT rabbit femur model. The common idea behind most ASM based segmentation methods is to first build a parametric shape model from a training dataset and during application, find a shape instance from the model that optimally fits to target image. However, it poses a fundamental difficulty for the current application because a diseased bone shape is significantly altered at regions with osteophyte deposition misguiding an ASM method that eventually leads to suboptimum segmentation results. Here, we introduce a new partial ASM method that uses bone shape over healthy regions and extrapolate its shape over diseased region following the underlying shape model. Once the healthy bone region is detected, osteophyte is segmented by subtracting partial-ASM derived shape from the overall diseased shape. Also, a new semi-automatic method is presented in this paper for efficiently building a 3D shape model for rabbit femur. The method has been applied to muCT images of 2-, 4-, and 8-week post ACLT and sham-treated rabbit femurs and results of reproducibility and sensitivity analyses of the new osteophyte segmentation method are presented.
    Proc SPIE 03/2010;
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    ABSTRACT: Osteoarthritis (OA) is the most common chronic joint disease, which causes the cartilage between the bone joints to wear away, leading to pain and stiffness. Currently, progression of OA is monitored by measuring joint space width using x-ray or cartilage volume using MRI. However, OA affects all periarticular tissues, including cartilage and bone. It has been shown previously that in animal models of OA, trabecular bone (TB) architecture is particularly affected. Furthermore, relative changes in architecture are dependent on the depth of the TB region with respect to the bone surface and main direction of load on the bone. The purpose of this study was to develop a new method for accurately evaluating 3D architectural changes induced by OA in TB. Determining the TB test domain that represents the same anatomic region across different animals is crucial for studying disease etiology, progression and response to therapy. It also represents a major technical challenge in analyzing architectural changes. Here, we solve this problem using a new active shape model (ASM)-based approach. A new and effective semi-automatic landmark selection approach has been developed for rabbit distal femur surface that can easily be adopted for many other anatomical regions. It has been observed that, on average, a trained operator can complete the user interaction part of landmark specification process in less than 15 minutes for each bone data set. Digital topological analysis and fuzzy distance transform derived parameters are used for quantifying TB architecture. The method has been applied on micro-CT data of excised rabbit femur joints from anterior cruciate ligament transected (ACLT) (n = 6) and sham (n = 9) operated groups collected at two and two-to-eight week post-surgery, respectively. An ASM of the rabbit right distal femur has been generated from the sham group micro-CT data. The results suggest that, in conjunction with ASM, digital topological parameters are suitable for analyzing architectural changes induced by OA.
    Proc SPIE 03/2007;