A Novel 3D Segmentation of Vertebral Bones from Volumetric CT Images Using Graph Cuts

Aslan, Melih S.; Ali, Asem; Rara, Ham; Arnold, Ben; Farag, Aly A.; Fahmi, Rachid; Xiang, Ping

doi:10.1007/978-3-642-10520-3_49

Cited by 26 publications

(29 citation statements)

References 12 publications

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“…The simplest model of spatial interaction is the Markov Gibbs random field (MGRF) with the nearest 6-neighborhood. Therefore, for this specific model the Gibbs potential, γ, can be obtained analytically using our maximum likelihood estimator (MLE) for a generic MGRF in [13,14]. So, the resulting approximate MLE of γ is:…”

Section: Graph Cuts Segmentation Frameworkmentioning

confidence: 98%

3D vertebrae segmentation using graph cuts with shape prior constraints

Aslan

Ali

Chen

et al. 2010

2010 IEEE International Conference on Image Processing

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Osteoporosis is a bone disease characterized by a reduction in bone mass, resulting in an increased risk of fractures. To diagnose the osteoporosis accurately, bone mineral density (BMD) measurements and fracture analysis (FA) of the Vertebral bodies (VBs) are required. In this paper, we propose a robust and 3D shape based method to segment VBs in clinical computed tomography (CT) images in order to make BMD measurements and FA accurately. In this experiment, image appearance and shape information of VBs are used. In the training step, 3D shape information is obtained from a set of data sets. Then, we estimate the shape variations using a distance probabilistic model which approximates the marginal densities of the VB and background in the variability region. In the segmentation step, the Matched filter is used to detect the VB region automatically. We align the detected volume with 3D shape prior in order to be used in distance probabilistic model. Then, the graph cuts method which integrates the linear combination of Gaussians (LCG), Markov Gibbs Random Field (MGRF), and distance probabilistic model obtained from 3D shape prior is used.

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Section: Graph Cuts Segmentation Frameworkmentioning

confidence: 98%

3D vertebrae segmentation using graph cuts with shape prior constraints

Aslan

Ali

Chen

et al. 2010

2010 IEEE International Conference on Image Processing

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“…Traditional approaches such as thresholding [19][20][21][22] using only the gray level information will not work to solve the noise problem. Edge-and-contour based variational methods [10,11,[23][24][25] and spatially discrete optimization methods [26][27][28][29] using only the existing information (intensity and/ or spatial interaction) may work well to solve the noise problem. However, these methods will not be able to obtain desired segmentation when there is occlusion problem in the image.…”

Section: Minimizing -Log P(l Lflmentioning

confidence: 99%

“…Detect the VB region using [27] 2. Obtain the initial segmentation (f*) using graph cuts which integrates the intensity and spatial interaction models only.…”

Section: Algorithmmentioning

confidence: 99%

“…AsIan et al proposed various methods to segment VBs in [27,34,80,81] which can be considered as progressive VB segmentation studies. In [27], the shape model was not used and it was assumed that the detection rate of VBs was very accurate for cropping the pedicles automatically.…”

Section: B Introductionmentioning

confidence: 99%

“…In [27], the shape model was not used and it was assumed that the detection rate of VBs was very accurate for cropping the pedicles automatically. In [80], a probabilistic shape model was introduced in addition to the intensity and spatial interaction information to enhance the results.…”

Section: B Introductionmentioning

confidence: 99%

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Probabilistic and geometric shape based segmentation methods.

Aslan¹

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Aslan, Melih Seref, "Probabilistic and geometric shape based segmentation methods." (2012 Image segmentation is one of the most important problems in image processing, object recognition, computer vision, medical imaging, etc. In general, the objective of the segmentation is to partition the image into the meaningful areas using the existing (low level) information in the image and prior (high level) information which can be obtained using a number of features of an object. As stated in [1,2], the human vision system aims to extract and use as much information as possible in the image including but not limited to the intensity, possible motion of the object (in sequential images), spatial relations (interaction) as the existing information, and the shape of the object which is learnt from the experience as the prior information. The main objective of this dissertation is to couple the prior information with the existing information since the machine vision system cannot predict the prior information unless it is given.To label the image into meaningful areas, the chosen information is modelled to fit progressively in each of the regions by an optimization process. The intensity and spatial interaction (as the existing information) and shape (as the prior information) are modelled to obtain the optimum segmentation in this study.The intensity information is modelled using the Gaussian distribution. Spatial inv teraction that describes the relation between neighboring pixels/voxels is modeled by assuming that the pixel intensity depends on the intensities of the neighboring pixels. The shape model is obtained using occurrences of histogram of training shape pixels or voxels. The main objective is to capture the shape variation of the object of interest. Each pixel in the image will have three probabilities to be an object and a background class based on the intensity, spatial interaction, and shape models. These probabilistic values will guide the energy (cost) functionals in the optimization process.This dissertation proposes segmentation frameworks which has the following properties: i) original to solve some of the existing problems, ii) robust under various segmentation challenges, and iii) fast enough to be used in the real applications. In this dissertation, the models are integrated into different methods to obtain the optimum segmentation: 1) variational (can be considered as the spatially continuous), and 2) statistical (can be considered as the spatially discrete) methods.The proposed segmentation frameworks start with obtaining the initial segmentation using the intensity / spatial interaction models. The shape model, which is obtained using the training shapes, is registered to the image domain. Finally, the optimal segmentation is obtained using the optimization of the energy functionals. Experiments show that the use of the shape prior improves considerably the accuracy of the alternative methods which use only existing or both information in the image. The proposed methods are tested on the syntheti...

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An Analysis by Synthesis Approach for Automatic Vertebral Shape Identification in Clinical QCT

Reinhold

Damm

Huber

et al. 2019

Lecture Notes in Computer Science

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Quantitative computed tomography (QCT) is a widely used tool for osteoporosis diagnosis and monitoring. The assessment of cortical markers like cortical bone mineral density (BMD) and thickness is a demanding task, mainly because of the limited spatial resolution of QCT. We propose a direct model based method to automatically identify the surface through the center of the cortex of human vertebra. We develop a statistical bone model and analyze its probability distribution after the imaging process. Using an as-rigid-as-possible deformation we find the cortical surface that maximizes the likelihood of our model given the input volume. Using the European Spine Phantom (ESP) and a high resolution µCT scan of a cadaveric vertebra, we show that the proposed method is able to accurately identify the real center of cortex ex-vivo. To demonstrate the in-vivo applicability of our method we use manually obtained surfaces for comparison.

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A Novel 3D Segmentation of Vertebral Bones from Volumetric CT Images Using Graph Cuts

Cited by 26 publications

References 12 publications

3D vertebrae segmentation using graph cuts with shape prior constraints

3D vertebrae segmentation using graph cuts with shape prior constraints

Probabilistic and geometric shape based segmentation methods.

An Analysis by Synthesis Approach for Automatic Vertebral Shape Identification in Clinical QCT

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