NDLI: A New Osteophyte Segmentation Algorithm Using the Partial Shape Model and Its Applications to Rabbit Femur Anterior Cruciate Ligament Transection via Micro-CT Imaging

Content Provider	IEEE Xplore Digital Library
Author	Saha, P.K. Liang, G. Elkins, J.M. Coimbra, A. Duong, L.T. Williams, D.S. Sonka, M.
Copyright Year	1964
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^{\bm 2}=0.99), reproducible (ICC = 0.97), and sensitive in detecting disease 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).
Sponsorship	IEEE Engineering in Medicine and Biology Society
Page Count	16
File Size	1594950
Starting Page	2212
Ending Page	2227
File Format	PDF
ISSN	00189294
Volume Number	58
Issue Number	8
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Shape Bones Three dimensional displays Image segmentation Manifolds Computed tomography Training segmentation Active shape model (ASM) anterior cruciate ligament transection (ACLT) micro-computed tomography (micro-CT) imaging osteophyte rabbit femur
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering

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