A New Approach for Cervical Vertebrae Segmentation

Mahmoudi, Saïd; Benjellοun, Mohammed

doi:10.1007/978-3-540-76725-1_78

Cited by 11 publications

(8 citation statements)

References 10 publications

(18 reference statements)

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“…Thus, there is a vast literature of segmentation from X-rays and Dual-energy X-ray Absorptiometry (DXA) images [13], [14], [15], [16], [2], [17], [18], from CT images [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [12], and from MR images [29], [30], [31]. However, in this section we are going to focus on the methods that directly or indirectly address the vertebral overlap.…”

Section: B Vertebra Segmentation Methodsmentioning

confidence: 99%

Statistical Interspace Models (SIMs): Application to Robust 3D Spine Segmentation

Castro-Mateos¹,

Pozo²,

Pereañez

et al. 2015

IEEE Trans. Med. Imaging

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Statistical shape models (SSM) are used to introduce shape priors in the segmentation of medical images. However, such models require large training datasets in the case of multi-object structures, since it is required to obtain not only the individual shape variations but also the relative position and orientation among objects. A solution to overcome this limitation is to model each individual shape independently. However, this approach does not take into account the relative position, orientations and shapes among the parts of an articulated object, which may result in unrealistic geometries, such as with object overlaps. In this article, we propose a new Statistical Model, the Statistical Interspace Model (SIM), which provides information about the interaction of all the individual structures by modeling the interspace between them. The SIM is described using relative position vectors between pair of points that belong to different objects that are facing each other. These vectors are divided into their magnitude and direction, each of these groups modeled as independent manifolds. The SIM was included in a segmentation framework that contains an SSM per individual object. This framework was tested using three distinct types of datasets of CT images of the spine. Results show that the SIM completely eliminated the inter-process overlap while improving the segmentation accuracy.

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Section: B Vertebra Segmentation Methodsmentioning

confidence: 99%

Statistical Interspace Models (SIMs): Application to Robust 3D Spine Segmentation

Castro-Mateos¹,

Pozo²,

Pereañez

et al. 2015

IEEE Trans. Med. Imaging

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“…Numerous other approaches exist in the literature and without being exhaustive, let's mention the generalized Hough transform [10], [25] and the template matching [16].…”

Section: Introductionmentioning

confidence: 99%

Points of interest detection in cervical spine radiographs by polygonal approximation

Lecron

Benjelloun

Mahmoudi

2010

2010 2nd International Conference on Image Processing Theory, Tools and Applications

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In this paper, we introduce a robust approach to detect points of interest in cervical spine radiographs. The perspective of this work is to segment the vertebrae on X-Ray images for the analysis of the vertebral mobility. In previous work, we proposed a segmentation technique based on Active Shape Model. The extraction and the detection of the vertebra corners can contribute to the automatic initialization of the Active Shape Model search and can give valuable information about the spine curvature. Here, we present the benefits of the polygonal approximation dedicated to the points of interest detection. The methodology developed here is composed of 3 stages: a contrast limited adaptive histogram equalization, a Canny edge detection filter and an edge polygonal approximation. The first histogram equalization step is a pretraitment needed to improve the image quality in order to perform a better contour detection. The Canny operator detects the edges in the radiograph which are used as an input to the polygonal approximation. The edges become segment lines whose intersections define corners. We compare the results obtained with our approach based on the polygonal approximation to results coming from the Harris corner detector.

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“…Furthermore the method is semiautomatic as the process begins by manual identification of vertebral end-plates [ 15 ]. In an effort to reduce computational demands, a more recent study presented a semiautomatic analysis of the cervical spine using Harris corner point detection algorithm [ 16 , 17 ]. This report for the first time demonstrates analysis of global curvature change of the cervical spine using healthy spines without degenerative conditions.…”

Section: Introductionmentioning

confidence: 99%

Image Segmentation and Analysis of Flexion-Extension Radiographs of Cervical Spines

Enikov

Anton

2014

Journal of Medical Engineering

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We present a new analysis tool for cervical flexion-extension radiographs based on machine vision and computerized image processing. The method is based on semiautomatic image segmentation leading to detection of common landmarks such as the spinolaminar (SL) line or contour lines of the implanted anterior cervical plates. The technique allows for visualization of the local curvature of these landmarks during flexion-extension experiments. In addition to changes in the curvature of the SL line, it has been found that the cervical plates also deform during flexion-extension examination. While extension radiographs reveal larger curvature changes in the SL line, flexion radiographs on the other hand tend to generate larger curvature changes in the implanted cervical plates. Furthermore, while some lordosis is always present in the cervical plates by design, it actually decreases during extension and increases during flexion. Possible causes of this unexpected finding are also discussed. The described analysis may lead to a more precise interpretation of flexion-extension radiographs, allowing diagnosis of spinal instability and/or pseudoarthrosis in already seemingly fused spines.

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A New Approach for Cervical Vertebrae Segmentation

Cited by 11 publications

References 10 publications

Statistical Interspace Models (SIMs): Application to Robust 3D Spine Segmentation

Statistical Interspace Models (SIMs): Application to Robust 3D Spine Segmentation

Points of interest detection in cervical spine radiographs by polygonal approximation

Image Segmentation and Analysis of Flexion-Extension Radiographs of Cervical Spines

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