model-based image segmentation is a popular approach for the segmentation of anatomical structures from medical images because it includes prior knowledge about the shape and appearance of structures of interest. This...
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model-based image segmentation is a popular approach for the segmentation of anatomical structures from medical images because it includes prior knowledge about the shape and appearance of structures of interest. This paper focuses on the formulation of a novel appearance prior that can cope with large variability between subjects, for instance due to the presence of pathologies. Instead of relying on Principal Component Analysis such as in Statistical Appearance models, our approach relies on a multimodal intensity profile atlas from which a point may be assigned to several profile modes consisting of a mean profile and its covariance matrix. These profile modes are first estimated without any intra-subject registration through a boosted EM classification based on spectral clustering. Then, they are projected on a reference mesh whose role is to store the appearance information in a common geometric representation. We show that this prior leads to better performance than the classical monomodal Principal Component Analysis approach while relying on fewer profile modes. (c) 2013 Elsevier Inc. All rights reserved.
Background: Medical image computing is of growing importance in medical diagnostics and image-guided therapy. Nowadays, image analysis systems integrating advanced image computing methods are used in practice e.g. to ...
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Background: Medical image computing is of growing importance in medical diagnostics and image-guided therapy. Nowadays, image analysis systems integrating advanced image computing methods are used in practice e.g. to extract quantitative image parameters or to support the surgeon during a navigated intervention. However, the grade of automation, accuracy, reproducibility and robustness of medical image computing methods has to be increased to meet the requirements in clinical routine. Objectives: In the focus theme, recent developments and advances in the field of modeling and model-basedimage analysis are described. The introduction of models in the image analysis process enables improvements of image analysis algorithms in terms of automation, accuracy, reproducibility and robustness. Furthermore, model-basedimage computing techniques open up new perspectives for prediction of organ changes and risk analysis of patients. Methods: Selected contributions are assembled to present latest advances in the field. The authors were invited to present their recent work and results based on their outstanding contributions to the Conference on Medical image Computing BVM 2011 held at the University of Lubeck, Germany. All manuscripts had to pass a comprehensive peer review. Results: modeling approaches and model-basedimage analysis methods showing new trends and perspectives in model-based medical image computing are described. Complex models are used in different medical applications and medical images like radiographic images, dual-energy CT images, MR images, diffusion tensor images as well as microscopic images are analyzed. The applications emphasize the high potential and the wide application range of these methods. Conclusions: The use of model-basedimage analysis methods can improve segmentation quality as well as the accuracy and reproducibility of quantitative image analysis. Furthermore, image-basedmodels enable new insights and can lead to a deeper understand
Deformable models have already been successfully applied to the semi-automatic segmentation of organs from medical images. We present an approach which enables the fully automatic segmentation of the heart from multi-...
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ISBN:
(纸本)9780819466303
Deformable models have already been successfully applied to the semi-automatic segmentation of organs from medical images. We present an approach which enables the fully automatic segmentation of the heart from multi- slice computed tomography images. Compared to other approaches, we address the complete segmentation chain comprising both model initialization and adaptation. A multi-compartment mesh describing both atria, both ventricles, the myocardium around the left ventricle and the trunks of the great vessels is adapted to an image volume. The adaptation is performed in a coarse-to-fine manner by progressively relaxing constraints on the degrees of freedom of the allowed deformations. First, the mesh is translated to a rough estimate of the heart's center of mass. Then, the mesh is deformed under the action of image forces. We first constrain the space of deformations to parametric transformations, compensating for global misalignment of the model chambers. Finally, a deformable adaptation is performed to account for more local and subtle variations of the patient's anatomy. The whole heart segmentation was quantitatively evaluated on 25 volume images and qualitatively validated on 42 clinical cases. Our approach was found to work fully automatically in 90% of cases with a mean surface-to-surface error clearly below 1.0 mm. Qualitatively, expert reviewers rated the overall segmentation quality as 4.2 +/- 0.7 on a 5-point scale.
An efficient way to improve the robustness of the segmentation of medical images with deformable models is to use a priori shape knowledge during the adaptation process. In this work, we investigate how the modeling o...
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ISBN:
(纸本)0819464236
An efficient way to improve the robustness of the segmentation of medical images with deformable models is to use a priori shape knowledge during the adaptation process. In this work, we investigate how the modeling of the shape variability in shape-constrained deformable models influences both the robustness and the accuracy of the segmentation of cardiac multi-slice CT images. Experiments are performed for a complex heart model, which comprises 7 anatomical parts, namely the four chambers, the myocardium, and trunks of the aorta and the pulmonary artery. In particular, we compare a common shape variability modeling technique based on principal component analysis (PCA) with a more simple approach, which consists of assigning an individual affine transformation to each anatomical sub-region of the heart model. We conclude that the piecewise affine modeling leads to the smallest segmentation error, while simultaneously offering the largest flexibility without the need for training data covering the range of possible shape variability, as required by PCA.
A method is proposed to segment digital posterior-anterior chest X-ray images. The segmentation is achieved through the registration of a deformable prior model, describing the anatomical structures of interest, to th...
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ISBN:
(纸本)3540290699
A method is proposed to segment digital posterior-anterior chest X-ray images. The segmentation is achieved through the registration of a deformable prior model, describing the anatomical structures of interest, to the X-ray image. The deformation of the model is performed using a deformation grid. A coarse matching of the model is done using anatomical landmarks automatically extracted from the image, and maps of oriented edges axe used to guide the deformation process, optimized with a probabilistic genetic algorithm. The method is applied to extract the ribcage and delineate the mediastinum and diaphragms. The segmentation is needed for defining the lungs region, used in computer-aided diagnosis systems.
The task of fitting parametric curve models to the boundaries of perceptually meaningful image regions is a key problem in computer vision with numerous applications, such as imagesegmentation, pose estimation, objec...
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The task of fitting parametric curve models to the boundaries of perceptually meaningful image regions is a key problem in computer vision with numerous applications, such as imagesegmentation, pose estimation, object tracking, and 3-D reconstruction. In this article, we propose the Contracting Curve Density (CCD) algorithm as a solution to the curve-fitting problem. The CCD algorithm extends the state-of-the-art in two important ways. First, it applies a novel likelihood function for the assessment of a fit between the curve model and the image data. This likelihood function can cope with highly inhomogeneous image regions, because it is formulated in terms of local image statistics. The local image statistics are learned on the fly from the vicinity of the expected curve. They provide therefore locally adapted criteria for separating the adjacent image regions. These local criteria replace often used predefined fixed criteria that rely on homogeneous image regions or specific edge properties. The second contribution is the use of blurred curve models as efficient means for iteratively optimizing the posterior density over possible model parameters. These blurred curve models enable the algorithm to trade-off two conflicting objectives, namely heaving a large area of convergence and achieving high accuracy. We apply the CCD algorithm to several challenging imagesegmentation and 3-D pose estimation problems. Our experiments with RGB images show that the CCD algorithm achieves a high level of robustness and sub-pixel accuracy even in the presence of severe texture, shading, clutter, partial occlusion, and strong changes of illumination.
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