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A novel noninvasive approach for early diagnosis of prostate cancer from Dynamic Contrast enhanced Magnetic Resonance Imaging is proposed. The proposed approach consists of four main steps. The first step is to isolate the prostate from the surrounding anatomical structures based on a Maximum a Posteriori estimate of a new log-likelihood function that accounts for the shape priori, the spatial interaction,...
We propose a particle filtering framework for rigid registration of a model image to a time-series of partially observed images. The method incorporates a model-based segmentation technique in order to track the pose dynamics of an underlying observed object with time. An applicable algorithm is derived by employing the proposed framework for registration of a 3D model of an anatomical structure,...
This paper presents a set of segmentation methods for various types of 3D point clouds. Segmentation of dense 3D data (e.g. Riegl scans) is optimised via a simple yet efficient voxelisation of the space. Prior ground extraction is empirically shown to significantly improve segmentation performance. Segmentation of sparse 3D data (e.g. Velodyne scans) is addressed using ground models of non-constant...
In this paper, a stochastic approach for extracting the articulated 3D human postures by synchronized multiple cameras in the high-dimensional configuration spaces is presented. Annealed Particle Filtering (APF) [1] seeks for the globally optimal solution of the likelihood. We improve and extend the APF with local memorization to estimate the suited kinematic postures for a volume sequence directly...
This work deals with a new technique for the estimation of the parameters and number of components in a finite mixture model. The learning procedure is performed by means of a expectation maximization (EM) methodology. The key feature of our approach is related to a top-down hierarchical search for the number of components, together with the integration of the model selection criterion within a modified...
For the 3D modeling of walking humans the determination of body pose and extraction of body parts, from the sensed 3D range data, are challenging image processing problems. Real body data may have holes because of self-occlusions and grazing angle views. Most of the existing modeling methods rely on direct fitting a 3D model into the data without considering the fact that the parts in an image are...
It is true that Terrestrial Laser Scanning (TLS) can quickly acquire the detailed and accurate three-dimensional data of ground objects, thus it is widely used in digital city, the protection of historic buildings and the deformation monitor of bridges, etc. However, the huge data of the point clouds would create huge TIN (Triangulated Irregular Network) models. It is difficult to use these data on...
Clustering algorithms have been popularly applied in tissue segmentation in MRI. However, traditional clustering algorithms could not take advantage of some prior knowledge of data even when it does exist. In this paper, we propose a new approach to tissue segmentation of 3D brain MRI using semi-supervised spectral clustering. Spectral clustering algorithm is more powerful than traditional clustering...
We present an algorithm for extracting 3D canonical scattering features observed over sparse, bistatic SAR apertures. The input to the algorithm is a collection of noisy bistatic measurements which are, in general, collected over nonlinear flight paths. The output of the algorithm is a set of canonical scattering features that describe the 3D scene geometry. The algorithm employs a pragmatic approach...
The segmentation of vascular structures in 3D medical images is of great importance for many clinical applications, ranging from the detection and measurement of vascular disease to providing information for surgical intervention. Accurate and robust vascular segmentation is made difficult by variations in the vessel's contrast enhancement and its surrounding background, both within the same patient...
This paper proposes a parametric, multivariate method for the joint detection and segmentation of brain activation based on fMRI data. The proposed technique uses region based level sets to separate between the task-related and non-task-related regions and performs, at each iteration of level set evolution, a separate multivariate linear model (MLM) analysis in each of the two regions. Simulations...
In this paper, we propose a 3D mesh simplification algorithm based on meaningful segmentation and progressive mesh. This method can successfully preserves appearance and closeness of the original mesh during the simplification process. The experiment results support the conclusion obviously.
Nuclear imaging serves as an important tool for the visualization and analysis of nebulized radiolabeled particle deposition in the lung, enabling assessment of both nebulizer properties and lung function. To date, most research in this field has been focused on mathematical modeling from empirical data. This work examines the use of high-resolution 3D CT/SPECT imaging technology accompanied by automated...
We present a novel variant of the RANSAC algorithm that is much more efficient, in particular when dealing with problems with low inlier ratios. Our algorithm assumes that there exists some grouping in the data, based on which we introduce a new binomial mixture model rather than the simple binomial model as used in RANSAC. We prove that in the new model it is more efficient to sample data from a...
This paper presents a hybrid segmentation approach for medical images that requires minimal manual initialization by integrating the fuzzy connectedness and Voronoi diagram classification algorithms. We start with a fuzzy connectedness filter to generate a sample of tissue from a region to be segmented and obtain image statistics that constitute the homogeneity operator to be used in the next stage...
In this paper, we address the complex problem of rapid modeling of large-scale areas and present a novel approach for the automatic reconstruction of cities from remote sensor data. The goal in this work is to automatically create lightweight, watertight polygonal 3D models from LiDAR data (Light Detection and Ranging) captured by an airborne scanner. This is achieved in three steps: preprocessing,...
This research effort focuses on the historically-difficult problem of creating large-scale (city size) scene models from sensor data, including rapid extraction and modeling of geometry models. The solution to this problem is sought in the development of a novel modeling system with a fully automatic technique for the extraction of polygonal 3D models from LiDAR (Light Detection And Ranging) data...
This paper describes a method to localize 3D objects, which is the extension of the segment-based object recognition method to use on a STL CAD model. Models for localization are automatically generated using contour generators, which are estimated by occluding contours of projected images of the CAD model from multiple viewing directions and depth images computed with a graphics accelerator. In addition,...
In this paper we propose a new supervised active contour model evolving with Haralick texture features. This model is divided in two stages. First, we use a supervised step where the user defines an ideal segmentation on a learning image. A linear programming model, modeling the behavior of the active contour, is then used to determine the weights of the Haralick features leading to the optimal segmentation...
In this paper, we propose using multi-scale Conditional Random Fields to classes 3D outdoor terrestrial laser scanned data. We improved Lim and Suterpsilas methods by introducing regional edge potentials in addition to the local edge and node potentials in the multi-scale Conditional Random Fields, and only a relatively small amount of increment in the computation time is required to achieve the improved...
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