Melih S. Aslan

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

Bone mineral density (BMD ) measurements and fracture analysis of the spine bones are restricted to the Vertebral bodies (VBs ). In this paper, we present a novel and fast 3D segmentation framework of VBs in clinical CT images using the graph cuts method. The Matched filter is employed to detect the VB region automatically. In the graph cuts method, a VB (object) and surrounding organs (background) are represented using a gray level distribution models which are approximated by a linear combination of Gaussians (LCG) to better specify region borders between two classes (object and background). Initial segmentation based on the LCG models is then iteratively refined by using MGRF with analytically estimated potentials. In this step, the graph cuts is used as a global optimization algorithm to find the segmented data that minimize a certain energy function, which integrates the LCG model and the MGRF model. Validity was analyzed using ground truths of data sets (expert segmentation) and the European Spine Phantom (ESP ) as a known reference. Experiments on the data sets show that the proposed segmentation approach is more accurate than other known alternatives.

3D vertebrae segmentation using graph cuts with shape prior constraints

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.

An automated vertebra identification and segmentation in CT images

In this paper, we propose a new 3D framework to identify and segment VBs and TBs in clinical computed tomography (CT) images without any user intervention. The Matched filter is employed to detect the VB region automatically on axial axis. To identify the VB on coronal and sagittal axis, we use a new developed approach based on 4 points automatically placed on cortical shell. To segment the identified VB, the graph cuts method which integrates a linear combination of Gaussians (LCG) and Markov Gibbs Random Field (MGRF) are used. Then, the cortical and trabecular bones are segmented using local volume growing methods. Experiments on the data sets show that the proposed segmentation approach is more accurate than other known alternatives.

A novel probabilistic simultaneous segmentation and registration using level set

We propose a new shape-based segmentation approach using the statistical shape prior and level sets method. The segmentation depends on the image information and shape prior. Training shapes are grouped to form a probabilistic model. The shape model is embedded into the image domain taking in consideration the evolution of a contour represented by a level set function. The evolution of the front gathers information from the image intensities and shape prior. The segmentation approach is applied in segmenting the vertebral bodies in CT images. Our results shows that the technique is accurate and robust compared with the other alternative in the literature.

A new shape based segmentation framework using statistical and variational methods

In this paper, we propose a new shape based segmentation and registration of the vertebral bodies (VBs) in clinical computed tomography (CT) images. The VB and surrounding organs have very close gray level information and there are no strong edges in some CT images. To overcome these challenges, image appearance and shape information of VBs are used. There are three phases of our experiments: i) the detection of the VB region using the Matched filter, ii) initial segmentation using the graph cuts which integrates the intensity and spatial interaction models, iii) registration of the shape priors and initially segmented region to obtain the final segmentation. Preliminary results show that our proposed algorithm gives very encouraging results and can solve many segmentation and registration problems.

Probabilistic shape-based segmentation method using level sets

In this study, a novel probabilistic, geometric and dynamic shape-based level sets method is proposed. The shape prior is coupled with the intensity information to enhance the segmentation results. The two-dimensional principal component analysis method is applied on the training shapes to represent the shape variation with enough number of shape projections in the training step. The shape model is constructed using the implicit representation of the projected shapes. A new energy functional is proposed (i) to embed the shape model into the image domain and (ii) to estimate the shape coefficients. The proposed method is validated on synthetic and clinical images with various challenges such as the noise, occlusion and missing information. The authors compare their method with some of related works. Experiments show that the proposed segmentation method is more accurate and robust than other alternatives under different challenges. * Note: Colour figures are available in the online version of this paper.

3D Vertebral Body Segmentation Using Shape Based Graph Cuts

Bone mineral density (BMD) measurements and fracture analysis of the spine bones are restricted to the Vertebral bodies (VBs). In this paper, we propose a novel 3D shape based method to segment VBs in clinical computed tomography (CT) images without any user intervention. The proposed method depends on both image appearance and shape information. 3D shape information is obtained from a set of training 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. To segment a VB, 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. Experiments on the data sets show that the proposed segmentation approach is more accurate than other known alternatives.

A new segmentation and registration approach for vertebral body analysis

To diagnose the osteoporosis accurately, the bone mineral density (BMD) measurements of the vertebral bodies (VBs) are required. In this paper, we propose a new segmentation and registration method in order to assist the BMD measurements and fracture analysis (FA) accurately. In this experiment, image appearance and shape information of VBs are used. Our shape model is required to be registered to the testing image to avoid user interaction(s). Our proposed framework has four phases: i) the detection of vertebral body (VB) using the Matched filter, ii) initial segmentation using the intensity and spatial interaction models, iii) the registration of the shape prior and initially segmented image by matching a vector distance function (VDF), and iv) final segmentation using graph cuts which integrates intensity, spatial interaction and shape prior. Preliminary results show that our new algorithm is very promising and can solve many segmentation and registration problems.

3D Vertebrae Segmentation in CT Images with Random Noises

Exposure levels (X-ray tube amperage and peak kilovoltage) are associated with various noise levels and radiation dose. When higher exposure levels are applied, the images have higher signal to noise ratio (SNR) in the CT images. However, the patient receives higher radiation dose in this case. In this paper, we use our robust 3D framework to segment vertebral bodies (VBs) in clinical computed tomography (CT) images with different noise levels. The Matched filter is employed to detect the VB region automatically. In the graph cuts method, a VB (object) and surrounding organs (background) are represented using a gray level distribution models which are approximated by a linear combination of Gaussians (LCG). Initial segmentation based on the LCG models is then iteratively refined by using Markov Gibbs random field(MGRF) with analytically estimated potentials. Experiments on the data sets show that the proposed segmentation approach is more accurate and robust than other known alternatives.

Segmentation of trabecular bones from Vertebral bodies in volumetric CT spine images

We present a 3D segmentation technique of trabecular (cancellous) bones in CT images of Vertebral bodies (VBs). In order to be used for Bone Mineral Density (BMD) measurements, the cortical and trabecular bones are subsequently segmented using graph cuts method and local volume growing methods separately. In the final step, we measure our segmentation accuracy for each method. Validity was analyzed using ground truths of data sets and the European Spine Phantom (ESP). Preliminary results are very encouraging and a reproducibility of the results was achieved for 16 data sets. The average segmentation error is below 2.0% for both methods.