An automated vertebra identification and segmentation in CT images

Aslan, Melih S.; Ali, Asem; Rara, Ham; Farag, Aly A.

doi:10.1109/icip.2010.5651959

Cited by 19 publications

(14 citation statements)

References 14 publications

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“…Mastmeyer et al (2006) use a 3D-deformable balloon model to segment the vertebral bodies semi-automatically. Graph cuts have also been used to segment vertebrae (Aslan et al 2010). Previously, seeds are automatically placed using the matched filter, and vertebrae are identified with a statistical method based on an adaptive threshold.…”

Section: Healthy Bonementioning

confidence: 99%

Identification of fractured bone tissue from CT images

Paulano

Jiménez

Pulido

2014

Computer Methods in Biomechanics and Biomedical Engineering: Im

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The identification of fractured bone from computed tomographic (CT) images is a helpful task in medical visualisation and simulation. In many cases, specialists need to manually revise 2D and 3D CT images and detect bone fragments in order to check a fracture. The automation of this process would allow them to save time. In visualisation, it allows the reduction of image noise and the removal of undesirable parts. In simulation, the utilisation of models reconstructed from CT images of patients allows customisation of the simulation, because the result of the segmentation can be used to perform a reconstruction that provides a 3D model of the patient anatomy. In this paper, the main issues to be considered in order to identify both healthy and fractured bones are described. The identification of fractured bone requires not only to segment bone tissue but also to label bone fragments. Moreover, some fragments can appear together after the segmentation process, hence additional processing can be required. Currently proposed approaches for identifying healthy and fractured bones are analysed and classified. Finally, the aspects to be improved in the described methods are outlined and the work to do is identified.

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Section: Healthy Bonementioning

confidence: 99%

Identification of fractured bone tissue from CT images

Paulano

Jiménez

Pulido

2014

Computer Methods in Biomechanics and Biomedical Engineering: Im

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“…Segmentation of vertebral bodies. Many image processing techniques have previously been applied to the segmentation of vertebral bodies in CT. [30][31][32][33] Our algorithm is based on level sets. Level sets are evolving contours or surfaces that can expand, contract, and even split or merge.…”

Section: Iib1 Computer Algorithmmentioning

confidence: 99%

High precision semiautomated computed tomography measurement of lumbar disk and vertebral heights

Tan

Yao

et al. 2012

Medical Physics

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Purpose: Evaluation of treatments of many spine disorders requires precise measurement of the heights of vertebral bodies and disk spaces. The authors present a semiautomated computer algorithm measuring those heights from spine computed tomography (CT) scans and evaluate its precision. Methods: Eight patients underwent two spine CT scans in the same day. In each scan, five thoracolumbar vertebral heights and four disk heights were estimated using the algorithm. To assess precision, the authors computed the differences between the height measurements in the two scans, coefficients of variation (CV), and 95% limits of agreement. Intraoperator and interoperator precisions were evaluated. For local vertebral and disk height measurement (anterior, middle, posterior) the algorithm was compared to a manual mid-sagittal plane method. Results: The mean (standard deviation) interscan difference was as low as 0.043 (0.031) mm for disk heights and 0.044 (0.043) mm for vertebral heights. The corresponding 95% limits of agreement were [−0.085, 0.11] and [−0.10, 0.12] mm, respectively. Intraoperator and interoperator precision was high, with a maximal CV of 0.30%. For local vertebral and disk heights, the algorithm improved upon the precision of the manual mid-sagittal plane measurement by as much as a factor of 6 and 4, respectively. Conclusions:The authors evaluated the precision of a novel computer algorithm for measuring vertebral body heights and disk heights using short term repeat CT scans of patients. The 95% limits of agreement indicate that the algorithm can detect small height changes of the order of 0.1 mm.

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“…previously published in [86], is described. It should be noted that segmentation accuracy is measured when the VBs are separated manually.…”

Section: Refined Andmentioning

confidence: 99%

“…In this process, two methods are suggested to separate each vertebrae. The first suggestion is the manual separation, and the second one is the previously proposed automatic framework [86] as shown in Figure 51. The advantage of automatic separation is to eliminate user interaction(s).…”

Section: Vertebrae Separationmentioning

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 automated vertebra identification and segmentation in CT images

Cited by 19 publications

References 14 publications

Identification of fractured bone tissue from CT images

Identification of fractured bone tissue from CT images

High precision semiautomated computed tomography measurement of lumbar disk and vertebral heights

Probabilistic and geometric shape based segmentation methods.

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