Segmentation of Lumbar Vertebrae Using Part-Based Graphs and Active Appearance Models

Roberts, Martin G.; Cootes, T. F.; Pacheco, Elisa; Oh, Teik Chooi; Adams, Judith E.

doi:10.1007/978-3-642-04271-3_123

Cited by 40 publications

(31 citation statements)

References 13 publications

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“…In order to overcome limitations of Active Appearance Models, a number of researchers suggested different methods to impose locality in appearance modeling. Roberts and Cootes, [2], proposed a method to capture localized texture variations using linked active appearance models. Delac et al [3], proposed a statistical appearance model using Independent Component Analysis (ICA).…”

Section: Related Workmentioning

confidence: 99%

Multiscale Sparse Appearance Modeling and Simulation of Pathological Deformations

Zewail¹,

Hag-ElSafi²

2017

IJIVP

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Machine learning and statistical modeling techniques has drawn much interest within the medical imaging research community. However, clinically-relevant modeling of anatomical structures continues to be a challenging task. This paper presents a novel method for multiscale sparse appearance modeling in medical images with application to simulation of pathological deformations in X-ray images of human spine. The proposed appearance model benefits from the non-linear approximation power of Contourlets and its ability to capture higher order singularities to achieve a sparse representation while preserving the accuracy of the statistical model. Independent Component Analysis is used to extract statistical independent modes of variations from the sparse Contourlet-based domain. The new model is then used to simulate clinically-relevant pathological deformations in radiographic images.

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Section: Related Workmentioning

confidence: 99%

Multiscale Sparse Appearance Modeling and Simulation of Pathological Deformations

Zewail¹,

Hag-ElSafi²

2017

IJIVP

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“…Accurate detection and extraction of bone contours from fluoroscopic images is an essential component for computer analysis of medical images for diagnosis [1][2] [3], planning [4][5] [6] or 3D reconstruction of anatomic structures [7][8] [9] [10]. Fluoroscopic images can vary a lot in terms of brightness and contrast as well as in the imaged region of anatomy.…”

Section: Motivationmentioning

confidence: 99%

“…Fluoroscopic images can vary a lot in terms of brightness and contrast as well as in the imaged region of anatomy. Therefore conventional segmentation techniques [1][5] [6] can not offer a satisfactory solution and model based segmentation is usually implemented to obtain robust and accurate results [3] [7][11] [12] [13].…”

Section: Motivationmentioning

confidence: 99%

“…In [3][12] [14][13] [15], 2D statistical models, Adaptive Shape Model (ASM) or Adapative Appearance Model (ASM), are constructed from a training image set under the assumption that the images are taken from a certain view direction. 2D statistical models can contain both the shape and texture information learnt from training data set, which is helpful in improving robustness and accuracy in noisy images.…”

Section: Motivationmentioning

confidence: 99%

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Automatic Extraction of Femur Contours from Calibrated Fluoroscopic Images

Dong

Ballester

Zheng

2007

2007 IEEE Workshop on Applications of Computer Vision (WAC v '07)

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Automatic identification and extraction of bone contours from x-ray images is an essential first step task for further medical image analysis. In this paper we propose a 3D statistical model based framework for the proximal femur contour extraction from calibrated x-ray images. The automatic initialization is solved by an Estimation of Bayesian Network Algorithm to fit a multiple component geometrical model to the x-ray data. The contour extraction is accomplished by a non-rigid 2D/3D registration between a 3D statistical model and the x-ray images, in which bone contours are extracted by a graphical model based Bayesian inference. Preliminary experiments on clinical data sets verified its validity. [10] 3D statistical models are used for 2D segmentation and 3D reconstruction from calibrated 2D fluoroscopic images (location and orientation of the fluoroscopic source w.r.t. the image acquisition planes are known). Compared with 2D statistical modes, 3D statistical model usually only contains shape information but not the intensity information on the 2D images. In principle it can be used for segmenting an image taken from an arbitary view direction. 3D statistical model also need an initialization, which is usually manually defined [7][9]. Due to the dense mesh of the 3D statistical model [16], fully automated solutions based on evolutionary algorithm is computational expensive [17]. MotivationIn this paper we propose a 3D statistical model based fully automatic segmentation framework for calibrated fluoroscopic images. In our approach, the initialization is accomplished by an Estimation of Bayesian Network Algorithm on a simplified multiple component model instead of the triangulated surface mesh of the 3D model, which reduces the computational complexity. The statistical model based fine shape extraction is achieved by a Bayesian inference on a Bayesian network, which encodes the shape and texture information of the model and therefore enhances the robustness and accuracy of the contour extraction. Related WorkBayesian network based approach [18][19][20] is used to identify or track object such as human body, which is composed with multiple subparts and among the subparts structral or kinematic constrains exist. The Bayesian network embeds the subparts constraints in a graphical model associated with image observations. Bayesian network is also exploited for finding deformable shapes

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“…Due to the clinical importance of vertebrae segmentation, there is a large body of research in the literature [3][4][5][6][7][8][9][10][11]. Based on this literature, arguably the most successful segmentation method is the statistical shape model (SSM).…”

Section: Introductionmentioning

confidence: 99%

Improving an Active Shape Model with Random Classification Forest for Segmentation of Cervical Vertebrae

Arif

Gundry

Knapp

et al. 2016

Lecture Notes in Computer Science

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Abstract. X-ray is a common modality for diagnosing cervical vertebrae injuries. Many injuries are missed by emergency physicians which later causes life threatening complications. Computer aided analysis of X-ray images has the potential to detect missed injuries. Segmentation of the vertebrae is a crucial step towards automatic injury detection system. Active shape model (ASM) is one of the most successful and popular method for vertebrae segmentation. In this work, we propose a new ASM search method based on random classification forest and a kernel density estimation-based prediction technique. The proposed method have been tested on a dataset of 90 emergency room X-ray images containing 450 vertebrae and outperformed the classical Mahalanobis distancebased ASM search and also the regression forest-based method.

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Segmentation of Lumbar Vertebrae Using Part-Based Graphs and Active Appearance Models

Cited by 40 publications

References 13 publications

Multiscale Sparse Appearance Modeling and Simulation of Pathological Deformations

Multiscale Sparse Appearance Modeling and Simulation of Pathological Deformations

Automatic Extraction of Femur Contours from Calibrated Fluoroscopic Images

Improving an Active Shape Model with Random Classification Forest for Segmentation of Cervical Vertebrae

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