Catálogo de publicaciones - libros
Medical Image Computing and Computer-Assisted Intervention: MICCAI 2006: 9th International Conference, Copenhagen, Denmark, October 1-6, 2006,Proceedings, Part I
Rasmus Larsen ; Mads Nielsen ; Jon Sporring (eds.)
En conferencia: 9º International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) . Copenhagen, Denmark . October 1, 2006 - October 6, 2006
Resumen/Descripción – provisto por la editorial
No disponible.
Palabras clave – provistas por la editorial
Image Processing and Computer Vision; Pattern Recognition; Computer Graphics; Artificial Intelligence (incl. Robotics); Imaging / Radiology; Health Informatics
Disponibilidad
Institución detectada | Año de publicación | Navegá | Descargá | Solicitá |
---|---|---|---|---|
No detectada | 2006 | SpringerLink |
Información
Tipo de recurso:
libros
ISBN impreso
978-3-540-44707-8
ISBN electrónico
978-3-540-44708-5
Editor responsable
Springer Nature
País de edición
Reino Unido
Fecha de publicación
2006
Información sobre derechos de publicación
© Springer-Verlag Berlin Heidelberg 2006
Tabla de contenidos
doi: 10.1007/11866565_11
Segmenting Lung Fields in Serial Chest Radiographs Using Both Population and Patient-Specific Shape Statistics
Yonghong Shi; Feihu Qi; Zhong Xue; Kyoko Ito; Hidenori Matsuo; Dinggang Shen
This paper presents a new deformable model using both population-based and patient-specific shape statistics to segment lung fields from serial chest radiographs. , a modified scale-invariant feature transform (SIFT) local descriptor is used to characterize the image features in the vicinity of each pixel, so that the deformable model deforms in a way that seeks for the region with similar SIFT local descriptors. , the deformable model is constrained by both population-based and patient-specified shape statistics. Initially, population-based shape statistics takes most of the rules when the number of serial images is small; gradually, patient-specific shape statistics takes more rules after a sufficient number of segmentation results on the same patient have been obtained. The proposed deformable model can adapt to the shape variability of different patients, and obtain more robust and accurate segmentation results.
- Segmentation | Pp. 83-91
doi: 10.1007/11866565_12
4D Shape Priors for a Level Set Segmentation of the Left Myocardium in SPECT Sequences
Timo Kohlberger; Daniel Cremers; Mikaël Rousson; Ramamani Ramaraj; Gareth Funka-Lea
We develop a 4D (3D plus time) statistical shape model for implicit level set based shape representations. To this end, we represent hand segmented training sequences of the left ventricle by respective 4-dimensional embedding functions and approximate these by a principal component analysis. In contrast to recent 4D models on explicit shape representations, the implicit shape model developed in this work does not require the computation of point correspondences which is known to be quite challenging, especially in higher dimensions. Experimental results on the segmentation of SPECT sequences of the left myocardium confirm that the 4D shape model outperforms respective 3D models, because it takes into account a statistical model of the temporal shape evolution.
- Segmentation | Pp. 92-100
doi: 10.1007/11866565_13
Cell Segmentation Using Coupled Level Sets and Graph-Vertex Coloring
Sumit K. Nath; Kannappan Palaniappan; Filiz Bunyak
Current level-set based approaches for segmenting a large number of objects are computationally expensive since they require a unique level set per object (the -level set paradigm), or level sets when using a multiphase interface tracking formulation. Incorporating energy-based coupling constraints to control the topological interactions between level sets further increases the computational cost to (). We propose a new approach, with dramatic computational savings, that requires only four, or fewer, level sets for an arbitrary number of similar objects (like cells) using the Delaunay graph to capture spatial relationships. Even more significantly, the coupling constraints (energy-based and topological) are incorporated using just constant O(1) complexity. The explicit topological coupling constraint, based on predicting contour collisions between adjacent level sets, is developed to further prevent false merging or absorption of neighboring cells, and also reduce fragmentation during level set evolution. The proposed four-color level set algorithm is used to efficiently and accurately segment hundreds of individual epithelial cells within a moving monolayer sheet from time-lapse images of wound healing without any false merging of cells.
- Segmentation | Pp. 101-108
doi: 10.1007/11866565_14
3D Histological Reconstruction of Fiber Tracts and Direct Comparison with Diffusion Tensor MRI Tractography
Julien Dauguet; Sharon Peled; Vladimir Berezovskii; Thierry Delzescaux; Simon K. Warfield; Richard Born; Carl-Fredrik Westin
A classical neural tract tracer, WGA-HRP, was injected at multiple sites within the brain of a macaque monkey. Histological sections of the labeled fiber tracts were reconstructed in 3D, and the fibers were segmented and registered with the anatomical post-mortem MRI from the same animal. Fiber tracing along the same pathways was performed on the DTI data using a classical diffusion tracing technique. The fibers derived from the DTI were compared with those segmented from the histology in order to evaluate the performance of DTI fiber tracing. While there was generally good agreement between the two methods, our results reveal certain limitations of DTI tractography, particularly at regions of fiber tract crossing or bifurcation.
- Analysis of Diffusion Tensor MRI | Pp. 109-116
doi: 10.1007/11866565_15
Rician Noise Removal in Diffusion Tensor MRI
Saurav Basu; Thomas Fletcher; Ross Whitaker
Rician noise introduces a bias into MRI measurements that can have a significant impact on the shapes and orientations of tensors in diffusion tensor magnetic resonance images. This is less of a problem in structural MRI, because this bias is signal dependent and it does not seriously impair tissue identification or clinical diagnoses. However, diffusion imaging is used extensively for quantitative evaluations, and the tensors used in those evaluations are biased in ways that depend on orientation and signal levels. This paper presents a strategy for filtering diffusion tensor magnetic resonance images that addresses these issues. The method is a maximum estimation technique that operates directly on the diffusion weighted images and accounts for the biases introduced by Rician noise. We account for Rician noise through a data likelihood term that is combined with a spatial smoothing prior. The method compares favorably with several other approaches from the literature, including methods that filter diffusion weighted imagery and those that operate directly on the diffusion tensors.
- Analysis of Diffusion Tensor MRI | Pp. 117-125
doi: 10.1007/11866565_16
Anisotropy Creases Delineate White Matter Structure in Diffusion Tensor MRI
Gordon Kindlmann; Xavier Tricoche; Carl-Fredrik Westin
Current methods for extracting models of white matter architecture from diffusion tensor MRI are generally based on fiber tractography. For some purposes a compelling alternative may be found in analyzing the first and second derivatives of diffusion anisotropy. are ridges and valleys of locally extremal anisotropy, where the gradient of anisotropy is orthogonal to one or more eigenvectors of its Hessian. We propose that anisotropy creases provide a basis for extracting a skeleton of white matter pathways, in that ridges of anisotropy coincide with interiors of fiber tracts, and valleys of anisotropy coincide with the interfaces between adjacent but distinctly oriented tracts. We describe a crease extraction algorithm that generates high-quality polygonal models of crease surfaces, then demonstrate the method on a measured diffusion tensor dataset, and visualize the result in combination with tractography to confirm its anatomic relevance.
- Analysis of Diffusion Tensor MRI | Pp. 126-133
doi: 10.1007/11866565_17
Evaluation of 3-D Shape Reconstruction of Retinal Fundus
Tae Eun Choe; Isaac Cohen; Gerard Medioni; Alexander C. Walsh; SriniVas R. Sadda
We present a method for the 3-D shape reconstruction of the retinal fundus from stereo paired images. Detection of retinal elevation plays a critical role in the diagnosis and management of many retinal diseases. However, since the shape of ocular fundus is nearly planar, its 3-D depth range is very narrow. Therefore, we use the location of vascular bifurcations and a plane+parallax approach to provide a robust estimation of the epipolar geometry. Matching is then performed using a mutual information algorithm for accurate estimation of the disparity maps. To validate our results, in the absence of camera calibration, we compared the results with measurements from the current clinical gold standard, optical coherence tomography (OCT).
- Shape Analysis and Morphometry | Pp. 134-141
doi: 10.1007/11866565_18
Comparing the Similarity of Statistical Shape Models Using the Bhattacharya Metric
K. O. Babalola; T. F. Cootes; B. Patenaude; A. Rao; M. Jenkinson
A variety of different methods of finding correspondences across sets of images to build statistical shape models have been proposed, each of which is likely to result in a different model. When dealing with large datasets (particularly in 3D), it is difficult to evaluate the quality of the resulting models. However, if the different methods are successfully modelling the true underlying shape variation, the resulting models should be similar. If two different techniques lead to similar models, it suggests that they are indeed approximating the true shape change. In this paper we explore a method of comparing statistical shape models by evaluating the Bhattacharya overlap between their implied shape distributions. We apply the technique to investigate the similarity of three models of the same 3D dataset constructed using different methods.
- Shape Analysis and Morphometry | Pp. 142-150
doi: 10.1007/11866565_19
Improving Segmentation of the Left Ventricle Using a Two-Component Statistical Model
Sebastian Zambal; Jiří Hladůvka; Katja Bühler
Quality of segmentations obtained by 3D Active Appearance Models (AAMs) crucially depends on underlying training data. MRI heart data, however, often come noisy, incomplete, with respiratory-induced motion, and do not fulfill necessary requirements for building an AAM. Moreover, AAMs are known to fail when attempting to model local variations. Inspired by the recent work on split models [1] we propose an alternative to the methods based on pure 3D AAM segmentation. We interconnect a set of 2D AAMs by a 3D shape model. We show that our approach is able to cope with imperfect data and improves segmentations by 11% on average compared to 3D AAMs.
- Shape Analysis and Morphometry | Pp. 151-158
doi: 10.1007/11866565_20
An Approach for the Automatic Cephalometric Landmark Detection Using Mathematical Morphology and Active Appearance Models
Sylvia Rueda; Mariano Alcañiz
Cephalometric analysis of lateral radiographs of the head is an important diagnosis tool in orthodontics. Based on manually locating specific landmarks, it is a tedious, time-consuming and error prone task. In this paper, we propose an automated system based on the use of Active Appearance Models (AAMs). Special attention has been paid to clinical validation of our method since previous work in this field used few images, was tested in the training set and/or did not take into account the variability of the images. In this research, a top-hat transformation was used to correct the intensity inhomogeneity of the radiographs generating a consistent training set that overcomes the above described drawbacks. The AAM was trained using 96 hand-annotated images and tested with a leave-one-out scheme obtaining an average accuracy of 2.48mm. Results show that AAM combined with mathematical morphology is the suitable method for clinical cephalometric applications.
- Shape Analysis and Morphometry | Pp. 159-166