Specially adapted interactive tools for an improved 3D-segmentation of the spine

Kaminsky, Jan; Klinge, Petra M.; Rodt, Thomas; Bokemeyer, Martin; Luedemann, Wolf; Samii, Madjid

doi:10.1016/j.compmedimag.2003.12.001

Cited by 50 publications

(40 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…They also reported that their algorithm can be used to analyze three vertebrae in less than 10min. This timing is far from the real time required for clinical applications but it is a huge improvement compared to the timing of 1 − 2h reported in [7]. Other techniques have been developed to segment skeletal structures and can be found for instance in [8,9] and the references therein.…”

Section: Introductionmentioning

confidence: 99%

An automated vertebra identification and segmentation in CT images

Aslan

Ali

Rara

et al. 2010

2010 IEEE International Conference on Image Processing

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

An automated vertebra identification and segmentation in CT images

Aslan

Ali

Rara

et al. 2010

2010 IEEE International Conference on Image Processing

View full text Add to dashboard Cite

show abstract

“…There exists a wide range of approaches in the existing literature for spine and vertebral segmentation [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20]. Table I lists the main papers and reported segmentation results on spine segmentation in the literature.…”

Section: Introductionmentioning

confidence: 99%

Accurate Segmentation of Vertebral Bodies and Processes Using Statistical Shape Decomposition and Conditional Models

Pereañez

Lekadir

Castro-Mateos

et al. 2015

IEEE Trans. Med. Imaging

View full text Add to dashboard Cite

Abstract-Detailed segmentation of the vertebrae is an important pre-requisite in various applications of image-based spine assessment, surgery and biomechanical modeling. In particular, accurate segmentation of the processes is required for imageguided interventions, for example for optimal placement of bone grafts between the transverse processes. Furthermore, the geometry of the processes is now required in musculoskeletal models due to their interaction with the muscles and ligaments. In this paper, we present a new method for detailed segmentation of both the vertebral bodies and processes based on statistical shape decomposition and conditional models. The proposed technique is specifically developed with the aim to handle the complex geometry of the processes and the large variability between individuals. The key technical novelty in this work is the introduction of a part-based statistical decomposition of the vertebrae, such that the complexity of the subparts is effectively reduced, and model specificity is increased. Subsequently, in order to maintain the statistical and anatomic coherence of the ensemble, conditional models are used to model the statistical inter-relationships between the different subparts. For shape reconstruction and segmentation, a robust model fitting procedure is used to exclude improbable inter-part relationships in the estimation of the shape parameters. Segmentation results based on a dataset of 30 healthy CT scans and a dataset of 10 pathological scans show a point-to-surface error improvement of 20% and 17% respectively, and the potential of the proposed technique for detailed vertebral modeling.

show abstract

“…The authors reported that complete analysis of three vertebrae took approximately 10 minutes in 2006 on a high standard computer system. This timing is far from the real time required for clinical applications but it is a huge improvement compared to the timing of 1 -2h reported in [54].…”

Section: B Introductionmentioning

confidence: 99%

“…• If the proposed method in the Chapter 3 is compared with most of the published bone segmentation methods (such as in [36,51,54]), the large execution time is reduced effectively.…”

Section: A What Is Accomplishedmentioning

confidence: 99%

See 1 more Smart Citation

Probabilistic and geometric shape based segmentation methods.

Aslan¹

View full text Add to dashboard Cite

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...

show abstract

Specially adapted interactive tools for an improved 3D-segmentation of the spine

Cited by 50 publications

References 34 publications

An automated vertebra identification and segmentation in CT images

An automated vertebra identification and segmentation in CT images

Accurate Segmentation of Vertebral Bodies and Processes Using Statistical Shape Decomposition and Conditional Models

Probabilistic and geometric shape based segmentation methods.

Contact Info

Product

Resources

About