Enhanced perception in volume visualization

Due to the nature of scientic data sets, the generation of convenient visualizations may be a difficult task, but crucial to correctly convey the relevant information of the data. When working with complex volume models, such as the anatomical ones, it is important to provide accurate representations, since a misinterpretation can lead to serious mistakes while diagnosing a disease or planning surgery. In these cases, enhancing the perception of the features of interest usually helps to properly understand the data. Throughout years, researchers have focused on different methods to improve the visualization of volume data sets. For instance, the definition of good transfer functions is a key issue in Volume Visualization, since transfer functions determine how materials are classified. Other approaches are based on simulating realistic illumination models to enhance the spatial perception, or using illustrative effects to provide the level of abstraction needed to correctly interpret the data. This thesis contributes with new approaches to enhance the visual and spatial perception in Volume Visualization. Thanks to the new computing capabilities of modern graphics hardware, the proposed algorithms are capable of modifying the illumination model and simulating illustrative motifs in real time. In order to enhance local details, which are useful to better perceive the shape and the surfaces of the volume, our first contribution is an algorithm that employs a common sharpening operator to modify the lighting applied. As a result, the overall contrast of the visualization is enhanced by brightening the salient features and darkening the deeper regions of the volume model. The enhancement of depth perception in Direct Volume Rendering is also covered in the thesis. To do this, we propose two algorithms to simulate ambient occlusion: a screen-space technique based on using depth information to estimate the amount of light occluded, and a view-independent method that uses the density values of the data set to estimate the occlusion. Additionally, depth perception is also enhanced by adding halos around the structures of interest. Maximum Intensity Projection images provide a good understanding of the high intensity features of the data, but lack any contextual information. In order to enhance the depth perception in such a case, we present a novel technique based on changing how intensity is accumulated. Furthermore, the perception of the spatial arrangement of the displayed structures is also enhanced by adding certain colour cues. The last contribution is a new manipulation tool designed for adding contextual information when cutting the volume. Based on traditional illustrative effects, this method allows the user to directly extrude structures from the cross-section of the cut. As a result, the clipped structures are displayed at different heights, preserving the information needed to correctly perceive them.Debido a la naturaleza de los datos científicos, visualizarlos correctamente puede ser una tarea complicada, pero crucial para interpretarlos de forma adecuada. Cuando se trabaja con modelos de volumen complejos, como es el caso de los modelos anatómicos, es importante generar imágenes precisas, ya que una mala interpretación de las mismas puede producir errores graves en el diagnóstico de enfermedades o en la planificación de operaciones quirúrgicas. En estos casos, mejorar la percepción de las zonas de interés, facilita la comprensión de la información inherente a los datos. Durante décadas, los investigadores se han centrado en el desarrollo de técnicas para mejorar la visualización de datos volumétricos. Por ejemplo, los métodos que permiten definir buenas funciones de transferencia son clave, ya que éstas determinan cómo se clasifican los materiales. Otros ejemplos son las técnicas que simulan modelos de iluminación realista, que permiten percibir mejor la distribución espacial de los elementos del volumen, o bien los que imitan efectos ilustrativos, que proporcionan el nivel de abstracción necesario para interpretar correctamente los datos. El trabajo presentado en esta tesis se centra en mejorar la percepción de los elementos del volumen, ya sea modificando el modelo de iluminación aplicado en la visualización, o simulando efectos ilustrativos. Aprovechando la capacidad de cálculo de los nuevos procesadores gráficos, se describen un conjunto de algoritmos que permiten obtener los resultados en tiempo real. Para mejorar la percepción de detalles locales, proponemos modificar el modelo de iluminación utilizando una conocida herramienta de procesado de imágenes (unsharp masking). Iluminando aquellos detalles que sobresalen de las superficies y oscureciendo las zonas profundas, se mejora el contraste local de la imagen, con lo que se consigue realzar los detalles de superficie. También se presentan diferentes técnicas para mejorar la percepción de la profundidad en Direct Volume Rendering. Concretamente, se propone modificar la iluminación teniendo en cuenta la oclusión ambiente de dos maneras diferentes: la primera utiliza los valores de profundidad en espacio imagen para calcular el factor de oclusión del entorno de cada pixel, mientras que la segunda utiliza los valores de densidad del volumen para aproximar dicha oclusión en cada vóxel. Además de estas dos técnicas, también se propone mejorar la percepción espacial y de la profundidad de ciertas estructuras mediante la generación de halos. La técnica conocida como Maximum Intensity Projection (MIP) permite visualizar los elementos de mayor intensidad del volumen, pero no aporta ningún tipo de información contextual. Para mejorar la percepción de la profundidad, proponemos una nueva técnica basada en cambiar la forma en la que se acumula la intensidad en MIP. También se describe un esquema de color para mejorar la percepción espacial de los elementos visualizados. La última contribución de la tesis es una herramienta de manipulación directa de los datos, que permite preservar la información contextual cuando se realizan cortes en el modelo de volumen. Basada en técnicas ilustrativas tradicionales, esta técnica permite al usuario estirar las estructuras visibles en las secciones de los cortes. Como resultado, las estructuras de interés se visualizan a diferentes alturas sobre la sección, lo que permite al observador percibirlas correctamente

Volume ray casting techniques and applications using general purpose computations on graphics processing units

Traditional 3D computer graphics focus on rendering the exterior of objects. Volume rendering is a technique used to visualize information corresponding to the interior of an object, commonly used in medical imaging and other fields. Visualization of such data may be accomplished by ray casting; an embarrassingly parallel algorithm also commonly used in ray tracing. There has been growing interest in performing general purpose computations on graphics processing units (GPGPU), which are capable exploiting parallel applications and yielding far greater performance than sequential implementations on CPUs. Modern GPUs allow for rapid acceleration of volume rendering applications, offering affordable high performance visualization systems. This thesis explores volume ray casting performance and visual quality enhancements using the NVIDIA CUDA platform, and demonstrates how high quality volume renderings can be produced with interactive and real time frame rates on modern commodity graphics hardware. A number of techniques are employed in this effort, including early ray termination, super sampling and texture filtering. In a performance comparison of a sequential versus CUDA implementation on high-end hardware, the latter is capable of rendering 60 frames per second with an impressive price-performance ratio heavily favoring GPUs. A number of unique volume rendering applications are explored including multiple volume rendering capable of arbitrary placement and rigid volume registration, hypertexturing and stereoscopic anaglyphs, each greatly enhanced by the real time interaction of volume data. The techniques and applications discussed in this thesis may prove to be invaluable tools in fields such as medical and molecular imaging, flow and scientific visualization, engineering drawing and many others

Occlusion and Slice-Based Volume Rendering Augmentation for PET-CT

Dual-modality positron emission tomography and computed tomography (PET-CT) depicts pathophysiological function with PET in an anatomical context provided by CT. Three-dimensional volume rendering approaches enable visualization of a two-dimensional slice of interest (SOI) from PET combined with direct volume rendering (DVR) from CT. However, because DVR depicts the whole volume, it may occlude a region of interest, such as a tumor in the SOI. Volume clipping can eliminate this occlusion by cutting away parts of the volume, but it requires intensive user involvement in deciding on the appropriate depth to clip. Transfer functions that are currently available can make the regions of interest visible, but this often requires complex parameter tuning and coupled pre-processing of the data to define the regions. Hence, we propose a new visualization algorithm where a SOI from PET is augmented by volumetric contextual information from a DVR of the counterpart CT so that the obtrusiveness from the CT in the SOI is minimized. Our approach automatically calculates an augmentation depth parameter by considering the occlusion information derived from the voxels of the CT in front of the PET SOI. The depth parameter is then used to generate an opacity weight function that controls the amount of contextual information visible from the DVR. We outline the improvements with our visualization approach compared to other slice-based and our previous approaches. We present the preliminary clinical evaluation of our visualization in a series of PET-CT studies from patients with non-small cell lung cancer

Feature-driven Volume Visualization of Medical Imaging Data

Direct volume rendering (DVR) is a volume visualization technique that has been proved to be a very powerful tool in many scientific visualization domains. Diagnostic medical imaging is one such domain in which DVR provides new capabilities for the analysis of complex cases and improves the efficiency of image interpretation workflows. However, the full potential of DVR in the medical domain has not yet been realized. A major obstacle for a better integration of DVR in the medical domain is the time-consuming process to optimize the rendering parameters that are needed to generate diagnostically relevant visualizations in which the important features that are hidden in image volumes are clearly displayed, such as shape and spatial localization of tumors, its relationship with adjacent structures, and temporal changes in the tumors. In current workflows, clinicians must manually specify the transfer function (TF), view-point (camera), clipping planes, and other visual parameters. Another obstacle for the adoption of DVR to the medical domain is the ever increasing volume of imaging data. The advancement of imaging acquisition techniques has led to a rapid expansion in the size of the data, in the forms of higher resolutions, temporal imaging acquisition to track treatment responses over time, and an increase in the number of imaging modalities that are used for a single procedure. The manual specification of the rendering parameters under these circumstances is very challenging. This thesis proposes a set of innovative methods that visualize important features in multi-dimensional and multi-modality medical images by automatically or semi-automatically optimizing the rendering parameters. Our methods enable visualizations necessary for the diagnostic procedure in which 2D slice of interest (SOI) can be augmented with 3D anatomical contextual information to provide accurate spatial localization of 2D features in the SOI; the rendering parameters are automatically computed to guarantee the visibility of 3D features; and changes in 3D features can be tracked in temporal data under the constraint of consistent contextual information. We also present a method for the efficient computation of visibility histograms (VHs) using adaptive binning, which allows our optimal DVR to be automated and visualized in real-time. We evaluated our methods by producing visualizations for a variety of clinically relevant scenarios and imaging data sets. We also examined the computational performance of our methods for these scenarios

Feature-driven Volume Visualization of Medical Imaging Data

Direct volume rendering (DVR) is a volume visualization technique that has been proved to be a very powerful tool in many scientific visualization domains. Diagnostic medical imaging is one such domain in which DVR provides new capabilities for the analysis of complex cases and improves the efficiency of image interpretation workflows. However, the full potential of DVR in the medical domain has not yet been realized. A major obstacle for a better integration of DVR in the medical domain is the time-consuming process to optimize the rendering parameters that are needed to generate diagnostically relevant visualizations in which the important features that are hidden in image volumes are clearly displayed, such as shape and spatial localization of tumors, its relationship with adjacent structures, and temporal changes in the tumors. In current workflows, clinicians must manually specify the transfer function (TF), view-point (camera), clipping planes, and other visual parameters. Another obstacle for the adoption of DVR to the medical domain is the ever increasing volume of imaging data. The advancement of imaging acquisition techniques has led to a rapid expansion in the size of the data, in the forms of higher resolutions, temporal imaging acquisition to track treatment responses over time, and an increase in the number of imaging modalities that are used for a single procedure. The manual specification of the rendering parameters under these circumstances is very challenging. This thesis proposes a set of innovative methods that visualize important features in multi-dimensional and multi-modality medical images by automatically or semi-automatically optimizing the rendering parameters. Our methods enable visualizations necessary for the diagnostic procedure in which 2D slice of interest (SOI) can be augmented with 3D anatomical contextual information to provide accurate spatial localization of 2D features in the SOI; the rendering parameters are automatically computed to guarantee the visibility of 3D features; and changes in 3D features can be tracked in temporal data under the constraint of consistent contextual information. We also present a method for the efficient computation of visibility histograms (VHs) using adaptive binning, which allows our optimal DVR to be automated and visualized in real-time. We evaluated our methods by producing visualizations for a variety of clinically relevant scenarios and imaging data sets. We also examined the computational performance of our methods for these scenarios

A volume filtering and rendering system for an improved visual balance of feature preservation and noise suppression in medical imaging

Preserving or enhancing salient features whilst effectively suppressing noise-derived artifacts and extraneous detail have been two consistent yet competing objectives in volumetric medical image processing. Illustrative techniques (and methods inspired by them) can help to enhance and, if desired, isolate the depiction of specific regions of interest whilst retaining overall context. However, highlighting or enhancing specific features can have the undesirable side-effect of highlighting noise. Second-derivative based methods can be employed effectively in both the rendering and volume filtering stages of a visualisation pipeline to enhance the depiction of feature detail whilst minimising noise-based artifacts. We develop a new 3D anisotropic-diffusion PDE for an improved balance of feature-retention and noise reduction; furthermore, we present a feature-enhancing visualisation pipeline that can be applied to multiple modalities and has been shown to be particularly effective in the context of 3D ultrasound

직접 볼륨 렌더링의 전이 함수 설계에 관한 연구

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2017. 2. 신영길.Although direct volume rendering (DVR) has become a commodity, the design of transfer functions still a challenge. Transfer functions which map data values to optical properties (i.e., colors and opacities) highlight features of interests as well as hide unimportant regions, dramatically impacting on the quality of the visualization. Therefore, for the effective rendering of interesting features, the design of transfer functions is very important and challenging task. Furthermore, manipulation of these transfer functions is tedious and time-consuming task. In this paper, we propose a 3D spatial field for accurately identifying and visually distinguishing interesting features as well as a mechanism for data exploration using multi-dimensional transfer function. First, we introduce a 3D spatial field for the effective visualization of constricted tubular structures, called as a stenosis map which stores the degree of constriction at each voxel. Constrictions within tubular structures are quantified by using newly proposed measures (i.e., line similarity measure and constriction measure) based on the localized structure analysis, and classified with a proposed transfer function mapping the degree of constriction to color and opacity. We show the application results of our method to the visualization of coronary artery stenoses. We present performance evaluations using twenty-eight clinical datasets, demonstrating high accuracy and efficacy of our proposed method. Second, we propose a new multi-dimensional transfer function which incorporates texture features calculated from statistically homogeneous regions. This approach employs parallel coordinates to provide an intuitive interface for exploring a new multi-dimensional transfer function space. Three specific ways to use a new transfer function based on parallel coordinates enables the effective exploration of large and complex datasets. We present a mechanism for data exploration with a new transfer function space, demonstrating the practical efficacy of our proposed method. Through a study on transfer function design for DVR, we propose two useful approaches. First method to saliently visualize the constrictions within tubular structures and interactively adjust the visual appearance of the constrictions delivers a substantial aid in radiologic practice. Furthermore, second method to classify objects with our intuitive interface utilizing parallel coordinates proves to be a powerful tool for complex data exploration.Chapter 1 Introduction 1 1.1 Background 1 1.1.1 Volume rendering 1 1.1.2 Computer-aided diagnosis 3 1.1.3 Parallel coordinates 5 1.2 Problem statement 8 1.3 Main contribution 12 1.4 Organization of dissertation 16 Chapter 2 Related Work 17 2.1 Transfer function 17 2.1.1 Transfer functions based on spatial characteristics 17 2.1.2 Opacity modulation techniques 20 2.1.3 Multi-dimensional transfer functions 22 2.1.4 Manipulation mechanism for transfer functions 25 2.2 Coronary artery stenosis 28 2.3 Parallel coordinates 32 Chapter 3 Volume Visualization of Constricted Tubular Structures 36 3.1 Overview 36 3.2 Localized structure analysis 37 3.3 Stenosis map 39 3.3.1 Overview 39 3.3.2 Detection of tubular structures 40 3.3.3 Stenosis map computation 49 3.4 Stenosis-based classification 52 3.4.1 Overview 52 3.4.2 Constriction-encoded volume rendering 52 3.4.3 Opacity modulation based on constriction 54 3.5 GPU implementation 57 3.6 Experimental results 59 3.6.1 Clinical data preparation 59 3.6.2 Qualitative evaluation 60 3.6.3 Quantitative evaluation 63 3.6.4 Comparison with previous methods 66 3.6.5 Parameter study 69 Chapter 4 Interactive Multi-Dimensional Transfer Function Using Adaptive Block Based Feature Analysis 73 4.1 Overview 73 4.2 Extraction of statistical features 74 4.3 Extraction of texture features 78 4.4 Multi-dimensional transfer function design using parallel coordinates 81 4.5 Experimental results 86 Chapter 5 Conclusion 90 Bibliography 92 초 록 107Docto

Perceptually optimized real-time computer graphics

Perceptual optimization, the application of human visual perception models to remove imperceptible components in a graphics system, has been proven effective in achieving significant computational speedup. Previous implementations of this technique have focused on spatial level of detail reduction, which typically results in noticeable degradation of image quality. This thesis introduces refresh rate modulation (RRM), a novel perceptual optimization technique that produces better performance enhancement while more effectively preserving image quality and resolving static scene elements in full detail. In order to demonstrate the effectiveness of this technique, a graphics framework has been developed that interfaces with eye tracking hardware to take advantage of user fixation data in real-time. Central to the framework is a high-performance GPGPU ray-tracing engine written in OpenCL. RRM reduces the frequency with which pixels outside of the foveal region are updated by the ray-tracer. A persistent pixel buffer is maintained such that peripheral data from previous frames provides context for the foveal image in the current frame. Traditional optimization techniques have also been incorporated into the ray-tracer for improved performance. Applying the RRM technique to the ray-tracing engine results in a speedup of 2.27 (252 fps vs. 111 fps at 1080p) for the classic Whitted scene with reflection and transmission enabled. A speedup of 3.41 (140 fps vs. 41 fps at 1080p) is observed for a high-polygon scene that depicts the Stanford Bunny. A small pilot study indicates that RRM achieves these results with minimal impact to perceived image quality. A secondary investigation is conducted regarding the performance benefits of increasing physics engine error tolerance for bounding volume hierarchy based collision detection when the scene elements involved are in the user\u27s periphery. The open-source Bullet Physics Library was used to add accurate collision detection to the full resolution ray-tracing engine. For a scene with a static high-polygon model and 50 moving spheres, a speedup of 1.8 was observed for physics calculations. The development and integration of this subsystem demonstrates the extensibility of the graphics framework

Constructive 3D Visualization techniques on Mobile platform- Empirical Analysis

As per the concept of 3D visualization on mobile devices it is clear that it belongs to two approaches i.e. local and remote approach. According to the technological advances in mobile devices it is possible to handle some complex data locally and visualized it. But still it is a challenging task to manage real entities on mobile devices locally. Remote visualization plays a vital role for 3D visualization on mobile platform in which data comes from server. Remote approach for 3D visualization on mobile platform consist of various techniques, critical analysis of such techniques is focus into this paper. Also the main focus is on network aspects