Toward 3D spatial dynamic field simulation within GIS using kinetic Voronoi diagram and Delaunay tetrahedralization

Beni, Leila Hashemi; Mostafavi, Mir Abolfazl; Pouliot, Jacynthe; Gavrilova, Marina L.

doi:10.1080/13658811003601430

Cited by 19 publications

(10 citation statements)

References 41 publications

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“…This alternative DTI projection is called tractography and even though it only shows the fluid carrying in the fiber tracks the datastructures used to represent the 3D composite is in a 3D pixel space, or "voxel." An alternative vector space or as continuous fields in geo-spatial computation could be used to represent only the regions on interest (Mostafavi et al, 2010;Beni et al, 2011).…”

Section: D Analysis Vs 3d Reconstructionsmentioning

confidence: 99%

Survey of Image Processing Techniques for Brain Pathology Diagnosis: Challenges and Opportunities

Čeněk

York

et al. 2018

Front. Robot. AI

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In recent years, a number of new products introduced to the global market combine intelligent robotics, artificial intelligence and smart interfaces to provide powerful tools to support professional decision making. However, while brain disease diagnosis from the brain scan images is supported by imaging robotics, the data analysis to form a medical diagnosis is performed solely by highly trained medical professionals. Recent advances in medical imaging techniques, artificial intelligence, machine learning and computer vision present new opportunities to build intelligent decision support tools to aid the diagnostic process, increase the disease detection accuracy, reduce error, automate the monitoring of patient's recovery, and discover new knowledge about the disease cause and its treatment. This article introduces the topic of medical diagnosis of brain diseases from the MRI based images. We describe existing, multi-modal imaging techniques of the brain's soft tissue and describe in detail how are the resulting images are analyzed by a radiologist to form a diagnosis. Several comparisons between the best results of classifying natural scenes and medical image analysis illustrate the challenges of applying existing image processing techniques to the medical image analysis domain. The survey of medical image processing methods also identified several knowledge gaps, the need for automation of image processing analysis, and the identification of the brain structures in the medical images that differentiate healthy tissue from a pathology. This survey is grounded in the cases of brain tumor analysis and the traumatic brain injury diagnoses, as these two case studies illustrate the vastly different approaches needed to define, extract, and synthesize meaningful information from multiple MRI image sets for a diagnosis. Finally, the article summarizes artificial intelligence frameworks that are built as multi-stage, hybrid, hierarchical information processing work-flows and the benefits of applying these models for medical diagnosis to build intelligent physician's aids with knowledge transparency, expert knowledge embedding, and increased analytical quality.

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Section: D Analysis Vs 3d Reconstructionsmentioning

confidence: 99%

Survey of Image Processing Techniques for Brain Pathology Diagnosis: Challenges and Opportunities

Čeněk

York

et al. 2018

Front. Robot. AI

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“…Directional HOI is about relative positioning properties with distance information. Despite the importance of direction in spatio-temporal analysis, directional HOI has attracted no attention even though some studies investigated and applied geometrical HOI [2], [3], [4] and topological HOI [5].…”

Section: Introductionmentioning

confidence: 99%

“…HOI for "whatif" analysis allows users to deal with the complexity of trajectory analysis especially when HOI is required. HOI for trajectory analysis has received little attention in the literature [2], [3], and existing research focuses on geometrical HOI. Directional HOI for trajectory is of particular interest when direction plays a critical role.…”

Section: Introductionmentioning

confidence: 99%

Directional Higher Order Information for Spatio-Temporal Trajectory Dataset

Wang

Lee

2014

2014 IEEE International Conference on Data Mining Workshop

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Higher order information includes k-nearest neighbor information and k-order region information that are of great importance when the first order or lower order information is not functioning. Despite of the importance of direction in spatio-temporal analysis, directional higher order information has received almost no attention. This paper introduces a new directional higher order information dissimilarity measure that combines topological and geometrical information for spatio-temporal trajectories. It also presents a spider chart-like visualisation approach for directional higher order information and demonstrates the usefulness of this measure with a case study from top-k trajectory mining.Keywords-Higher order information; spatio-temporal trajectory; higher order Voronoi diagrams; directional information; I. INTRODUCTIONHigher Order Information (HOI) includes k-nearest neighbor information and k-order region information that are of great importance when the first order or lower order information is not functioning properly. Uncertainty in modern society requires "what-if" analysis to explore different scenarios for better decision making. HOI provides necessary information for "what-if" analysis, and it has been researched for many years with various applications in GIS and emergency management [1].Based on the spatio-temporal characteristics of HOI, HOI can be classified into three different types: namely geometrical HOI, topological HOI and directional HOI. Directional HOI is about relative positioning properties with distance information. Despite the importance of direction in spatio-temporal analysis, directional HOI has attracted no attention even though some studies investigated and applied geometrical HOI [2], [3], [4] and topological HOI [5]. Trajectory is a typical example of spatio-temporal data capable of representing object dynamics. Trajectory of moving objects such as cars, humans, birds, or other objects is useful in decision making processes in many application domains such as animal movement [6], sport team strategies [7], surveillance analysis [8], [9], transport analysis [10], [11], emergency management [1], and supermarket shopping paths [12]. Improvement in tracking and sensing technologies facilitates a collection of vast volume of usergenerated trajectory data at very low cost. Researchers have

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“…Using the parameters of the 3D Voronoi cell, each particle can be assigned a certain importance according to its volume, surface area or their reciprocal and their combination (Equations (2) and (3)). In actual applications, a certain density or tension can be imposed on the 3D Voronoi cell to control these parameters [18].…”

Section: Importance Valuementioning

confidence: 99%

“…The analysis of spatial points has recently been simplified with the development of computer recognition programs and visualization methods that can identify spatial points based on their spatial and temporal characteristics [15][16][17][18][19]. The resulting dataset, which is a set of points distributed in space, is known as a spatial point pattern.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Point Cluster Analysis Using a 3D Voronoi Diagram with Applications in Point Cloud Segmentation

Ying

et al. 2015

IJGI

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Three-dimensional (3D) point analysis and visualization is one of the most effective methods of point cluster detection and segmentation in geospatial datasets. However, serious scattering and clotting characteristics interfere with the visual detection of 3D point clusters. To overcome this problem, this study proposes the use of 3D Voronoi diagrams to analyze and visualize 3D points instead of the original data item. The proposed algorithm computes the cluster of 3D points by applying a set of 3D Voronoi cells to describe and quantify 3D points. The decompositions of point cloud of 3D models are guided by the 3D Voronoi cell parameters. The parameter values are mapped from the Voronoi cells to 3D points to show the spatial pattern and relationships; thus, a 3D point cluster pattern can be highlighted and easily recognized. To capture different cluster patterns, continuous progressive clusters and segmentations are tested. The 3D spatial relationship is shown to facilitate cluster detection. Furthermore, the generated segmentations of real 3D data cases are exploited to demonstrate the feasibility of our approach in detecting different spatial clusters for continuous point cloud segmentation.

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Toward 3D spatial dynamic field simulation within GIS using kinetic Voronoi diagram and Delaunay tetrahedralization

Cited by 19 publications

References 41 publications

Survey of Image Processing Techniques for Brain Pathology Diagnosis: Challenges and Opportunities

Survey of Image Processing Techniques for Brain Pathology Diagnosis: Challenges and Opportunities

Directional Higher Order Information for Spatio-Temporal Trajectory Dataset

Point Cluster Analysis Using a 3D Voronoi Diagram with Applications in Point Cloud Segmentation

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