Table of Contents 1. INTRODUCTION ................................................................................................................ 2

2. OVERVIEW OF FLOW VISUALIZATION ....................................................................... 3

3. VISUALIZATION PIPELINE ............................................................................................. 4

3.1. Data Acquisition ........................................................................................................... 4

3.2. Data Enrichment/Enhancement .................................................................................... 5

3.2.1. Filtering ..................................................................................................................... 5

3.2.2. Data Selection ........................................................................................................... 5

3.2.3. Interpolation .............................................................................................................. 5

3.3. Visualization Mapping .................................................................................................. 5

3.4. Rendering and Display .................................................................................................. 7

3.5. Visualization Pipeline Summary ................................................................................... 7

4. FLOW VISUALISATION CLASSIFICATION .................................................................. 8

4.1. Research in Flow Visualization .................................................................................... 9

4.1.1. Integration-based and Geometric flow visualization technique ................................ 9

4.1.2. Dense and Texture Based technique ....................................................................... 13

5. ANALYSIS AND DISCUSSION....................................................................................... 18

6. APPLICATIONS AND AVAILABLE SYSTEMS ............................................................ 22

7. CONCLUSION ................................................................................................................... 22

REFERENCES ........................................................................................................................... 23

STATE OF THE ART IN THE 3D CARDIOVASCULAR VISUALIZATION

Yusman Azimi Yusoff 1, Farhan Mohamed 1, Mohd Shahrizal Sunar 1, Sanjiv Joshi Hari

Chand 2

1 UTM-IRDA Digital Media Centre, Media and Game Innovation Centre of Excellence,

Universiti Teknologi Malaysia, Skudai, Malaysia

2 National Heart Institute (IJN), Kuala Lumpur, Malaysia

yusman@magicx.my, farhan@utm.my, shahrizal@magicx.my, sanjiv@ijn.com.my

1. INTRODUCTION

The area of knowledge in scientific visualization applicable in many research field.

Scientific visualization is known with the ability to graphically illustrate the data and enable the

researchers to understand the information hidden in the datasets. This knowledge is actively

studied and applied in most of the research area including automotive industry, meteorological,

medical and engineering. Methods of visualizing datasets are dependent on the researchers’

interest. Scientific visualization can be divided into two more topics which are surface or

volume visualization and flow visualization. The taxonomy of flow visualization is shown in

Error! Reference source not found.. Surface volume visualization focuses on visualizing

scalar information inside the datasets while flow visualization is focusing on visualizing vector

data.

Both the surface volume visualization and flow visualization are widely used in biomedical

engineering especially in research related to cardiovascular. Currently, bio-medical researchers

are using engineering methods to find and solve problems related to the cardiovascular system.

In this case, researchers apply the knowledge in computer graphic as well as computational

fluid dynamic to solve the problem related to the blood flow analysis, myocardium and the

arteries network in the cardiovascular system. Scientific visualization becomes handy in these

areas because the traditional methods of finding region of interest in cardiovascular is by

studying and interpreting the medical images with bare eyes. Scientific visualization is able to

extend the medical data by constructing a virtual heart and arteries in a virtual environment.

Surface and volume visualization are used to visualize the wall and the tissue of the heart, while

flow visualization is used to visualize the blood flow. Along the pipeline, there are several

processes that are required to be followed before the final visualization result can be produced.

This chapter describes the process and method of flow visualization used in 3D

cardiovascular study. Some of the terms used in the explanation are derived from the computer

science and medical body of knowledge. The first section will detail out the taxonomy of the

flow visualization from computer science perspective. The second section will cover the

overview of flow visualization. The third section will explain the concept and framework of

data visualization along with the component inside the framework. The fourth section will

classify flow visualization method based on the spatial resolution. The fifth section will discuss

the impact and future direction of flow visualization in the cardiovascular research. The sixth

section will describe some of the readily available software on the market that applied flow

visualization knowledge to assist the cardiovascular studies. The last section will conclude the

content as well as the future work related with the cardiovascular visualisation.

Computing Methodologies

...Simulation,

Modeling, and Visualization

... Visualization

Flow visualization

Information visualization

Volume visualization

Figure 1. Domain Hierarchy/Structured Taxonomy of flow visualization [1]

2. OVERVIEW OF FLOW VISUALIZATION

Before going further into the domain of flow visualization, it is good to have a short

explanation about some of the important information about the flow visualization itself.

Traditionally, flow visualization was important in the physical experimental approach to study

the fluid behaviour due to several reasons:

• to imitate real cases without any calculation

• to test and new model or theory

Although experimental approach is able to simulate the situation in real time, there are

some problems raised in this approach. The result of the experimental approach usually affected

by the probe used to show the flow phenomena. Secondly, there are also flow phenomena such

as dynamic turbulent that are not possible to be visualised by using the experimental approach.

Conducting this approach consumes which is unaffected by the size of the experiments either in

small or large scale of experiments.

The research in flow visualization has becoming rapidly active after computer

simulation was introduced to simulate the flow phenomena. The increasing computational

capability allows researchers to use computer to execute complex numerical calculation and

simulation in flow visualisation. This technique is known as computer-aided visualization and is

used widely until today in various research field. There are several benefits of using computer-

aided visualisation compared to experimental visualization. The simulation process does not

require any physical resources since it is done virtually. The calculation time is dependent on

the complexity of the flow problem it’s solving. The model and setup for the simulation is done

virtually thus no inherent problem in finding and preparing the experiment materials.

Computer-aided visualization also allows in-depth exploration within the simulation

result. This feature allows researchers to focus clearly on the critical area without disturbing the

flow structure. Different techniques in flow visualization also allow visualising different type

flow patterns. Flow can be represented with visual representation such as hedgehog, glyphs,

streamlines, time lines, and stream surface. Each of the techniques has its own advantage in

highlighting the important feature of the fluid structure. Flow visualization technique will be

discussed extensively in the following section.

Practically, both experimental and computer-aided visualization are used to support

each other. In most of the industry that study the fluid dynamics, computer-aided visualization

is used to assist the creation of a suitable engineering prototype. The prototype is then produced

physically and tested with the experimental visualization. The result of experimental

visualization is then compared with computer-aided visualization result for verification. There

are also conditions where researchers used experimental visualization to verify their numerical

formula to solve fluid simulation problems.

3. VISUALIZATION PIPELINE

Flow visualization pipeline consist of series of phases. Haber and McNabb [2] present

three main transformation of a process in conceptual visualization. The pipeline converts raw

data acquired from the simulation result into an informative image. Those three phases start

with Data Enrichment or Enhancement, following with Visualization Mapping and end with

Rendering process. The pipeline has been improved in [3] by adding one more stage of Data

Acquisition phase. Figure 2 shows the complete visualization pipeline commonly used in

visualising scientific data.

Figure 2. Flow visualisation framework

3.1. Data Acquisition

The first phase in the visualization pipeline is called data acquisition. Flow data can be

generated by instrument measurement, numerical simulation or roughly direct observation.

Instrument measurement is used to acquire flow data in experimental visualization. The result of

flow data in the experimental visualization can be derived either by measuring the flow or

Data Acquisition

Data Enrichment/Enhancement

Visualization Mapping

Rendering and Display

analysing series of images through image processing. Numerical flow simulation is able to

produce vector and scalar data. Vector data in this case is the velocity field itself and scalar data

is referring to the density, pressure or temperature. On the other hand, volume visualization

used different technique in extracting the desired data from the raw. Further explanation about

volume visualization is discussed in the following section.

3.2. Data Enrichment/Enhancement

The data acquired will be enriched or enhanced in the second phase. The aim of this phase is to

improve the content of information. Not all data acquired can be directly visualized since it is

usually not possible to be used in the next phase. Methods such as sampling, filtering,

interpolation, and domain transformation are used to enrich or enhance the data before proceed

to the next phase.

3.2.1. Filtering

Data measured in the first phase often comes with the undesired information and noises. These

elements will disturb the process of visualization mapping and leads to visible error and poor

interpretation of the data. Thus, filtering operation is required to remove these disturbances in

the data acquired. Furthermore, certain filters are useful in highlighting important features in the

data, while other filters are used to dampened unrelated information.

3.2.2. Data Selection

Data selection operations are done to improve the upcoming result by selecting region of

interest portion of the data. In most cases, data acquired from the simulation result is in large

spaces where some of the area in the simulation are not related. Data selection is carried out by

removing the area of non-important parts of the data. There are also advance methods in data

selection in order to achieve a better visualization. For example, the important feature of the

flow field can be highlighted by choosing the area which the velocity change rapidly. This kind

of task requires further calculation in order to select a part of the data.

3.2.3. Interpolation

Simulation positioned the flow data in a grid arrangement so each grid points has the velocity

information. An interpolation operation is required in finding and generating new velocity data

to fill in the points between the grid points. The order of the interpolation depends on the

accuracy desired before executing the operation.

3.3. Visualization Mapping

The third phase in the pipeline focuses on converting the flow data into a visual representation.

Visual Mapping can be consider as the core in the visualization pipeline. This phase cover the

process of preparing the data into the desired visual presentation including other features such

as lights and colours. One of the ways to choose the visualization method is by knowing the

formula that compute the numerical simulation. In flow visualization, there are two types of

well-known formula used to calculate the flow features which are the Eulerian and the

Lagrangian functions. Eulerian can be described as observing the motion of fluid at a specific

position in an area as the fluid flows throughout the time. This type of flow can be represent

using arrow plot which able to show the flow velocity at each grid point. On the other hand,

Lagrangian can be describe as observing the motion of the fluid where a parcel of fluid is track

as it flow along the space and time. The visualization results by using Lagrangian is usually

presented as an animation of particular particle which shows the particle flow path through

time.

The enhanced data be will represented by the standard visualization mapping. In the

experimental flow visualization, flow is commonly represented with velocity arrow,

streamlines, streak lines, path lines and contour. These methods are also known as direct

method since it can be shown during the experimental process is carried out. Other primitive

method of visualizing flow also can be used such as points, lines and surfaces. There are pros

and cons for every visualization mapping method. Therefore, it is prefer to have a clear

understanding for each technique ability before using the method to visualize the flow.

This chapter focuses on the integration-based and dense/texture-based flow

visualizations. Primarily all vector visualizations are visualised by using the simple direct

visualization approach. Direct visualization uses glyphs to represent vector information. Glyphs

are usually in the form of arrow, icon or shape that able to show the direction and magnitude of

the vector. Each grid point will be placed by a glyph and the direction and size is dependent on

the vector and magnitude. This is one of the example of Eulerian formulation result. Glyph able

to show the global representation of flow pattern and is suitable to be used in 2 dimensional

(2D) flow data. Direct method is not suitable to be used with 3 dimensional (3D) flow data

because of several visual perception problems such as occlusion and confusion. More

explanation will be discuss in the rendering section.

Integration-based flow visualization is introduced to convey richer information from the

visualization. First, flow data is integrated using integrated-based approach and geometric

object is used as the foundation to visualize the flow. Examples of geometric-based

visualization are streamlets, streamlines, streak lines, and path lines. Streamlets is the result of

flow vector integration in a short period of time. Streamlets able to show the flow pattern

progression along the flow data. Streamlines is the extensions of streamlets through integration

of multiple streamlets features. In a 3D incompressible flow, streamlines are curve that satisfy

the equation , (1):

𝑑𝑥

𝑢=

𝑑𝑦

𝑣=

𝑑𝑧

𝑤, (1)

Where the velocity component of 𝑥- , 𝑦- and 𝑧- directions are represented by 𝑢, 𝑣, and 𝑤. This

method cover longer flow pattern and convey more information. These two methods are usually

used with steady flow data.

Streaklines, timelines and pathlines are much better method in studying unsteady flow

data. Unlike streamlets and streamlines, these three methods are able to show the flow pattern

along the time. Streaklines is the method of tracing a sets of particles that passes through a

position in the space. Another ways of understanding streaklines is by releasing particles into a

fluid flow continuously. The pattern shown by the particles along the flow space is the example

of streaklines. Timelines approach involves integrated lines from a set of particles that are

released at different position within the same time step. The generated line is perpendicular to

the direction of the particle movement but move with the particles. Lastly, pathlines or also

known as particle trace is the temporal path of the particle in the fluid flow. The generated line

is the trajectory of particle through time.

Another approach of visualizing the flow is known as dense or texture-based flow

visualization. This approach is different from integration-based where it provide global flow

pattern by blurring noise texture along the vector field. One of the earliest work related with this

approach is the Line Integral Convolution (LIC) which will be covered in Section 4. Figure 3

shows the input required in order to produce the visualization result using LIC. The selection of

input noise also plays an important role in constructing the visualization result. Improvements

made for this approach, which improved the texture-based result with additional features are

discussed in Section 4.

Figure 3. Input required in Line Integral Convolution [4].

3.4. Rendering and Display

After the data is successfully mapped, it will be transformed images that will be display

to the viewers. This process require computer graphic knowledge in order to carry out numbers

of operation such as setting up the viewing angle and backface culling. The scene of the mapped

visualization will be rendered to become a displayable image. The viewing frustum controls the

viewing angle and the region that will be showed as the visualization images. The hidden or

obstructing object in the scene will also be removed in the process. Other additional operation

such as anti-aliasing, shading, ray tracing are also implemented in this process. After the

mapped visualization is rendered, it will be displayed on the display device. Pixels of the

rendered images will be loaded into the frame buffer. Further operations are required to

generate animation for interactive and animated visualization display. The visualization is pre-

rendered and the images are organised accordingly to tailor it with the display properties.

3.5. Visualization Pipeline Summary

Figure 4 shows the processes involved for different visualization approaches.. Not

discussed in this chapter are the Feature- and topology-based approaches. These two approaches

have similar processes and results with integration-based visualization. The key difference is

that these two approaches highlight the critical point of flow pattern. In conclusion, each flow

visualization approaches has each own benefits and drawbacks. The choice of visualization

approach depend on the flow data properties and perspective required by the viewer.

Figure 4. Approach available flow from data acquisition process to display.

4. FLOW VISUALISATION CLASSIFICATION

This section is intended to share with readers on important flow visualization works.

Some of key paper related to 3D cardiovascular visualization are detailed out before going deep

into classifying flow visualization work. This classification allows the reader to focus and

understand the related works which introduced different flow visualization methods. The

classification is based on the data dimension involved in flow visualization approaches.

Generally, flow visualization works can be classified into 2D and 3D flow data. This

categorisation will allow readers to focus the works streamed to the input data domain.

The source most of scientific visualization data is based on the spatial resolution. For

example, wind tunnel produced a 3D vector dataset while cloud movement can be transformed

into a 2D vector dataset. Figure 5 shows the example of 2D air stremlets from [5] and 3D air

flows [6] visualizations. Researchers implement different algorithms and optimize it based on

the data before producing the flow visualization. The algorithm used to generate the

visualization is the main focus of this paper. These algorithms may change the way of

positioning the seed point, flow presentation and visualization focus area.

Display

Data Acquisition

Direct

Visualization

Geometry

Extraction

Visualization

Dense texture-

based

visualization

Feature

Extraction

Visualization

Geometry

Extraction

Figure 5 (a) Flow visualization using Streamlet on a 2D plane [5]. (b) Interactive flow

visualization with different visualization technique with locally define Cartesian grid [6].

In medical domain, most of the patient data are acquired from CT-Scan or MRI

machine during screen process to identify the patient health problem. These machines are able

to produce patient data from three different views. Each view consists of layer of images that

made up the data. Volume visualization can be used to extract and generate graphical

representation of the medical data based on the specific parameters. In general, there are two

types of flow visualization, which are the indirect and direct volume rendering. Indirect volume

visualization approach used algorithm such as contour tracing, cuberille, and marching cube [7]

to process and reconstruct the surface on an object from the volume data. Meanwhile, direct

volume visualization used techniques such as law of physics approach (e.g., emission,

absorption, and scattering).

4.1. Research in Flow Visualization

Most of the work done in flow visualization fall into visualization mapping phase

because this stage is crucial in transforming raw data to informative, graphical flow

representations. In the early days, flow visualization work were focused on improving 2D flow

visualization. Apparently, 3D flow data availablily through simulations and research in fluid

mechanics has made 3D visualization an important research field. These complex multi-

dimensional data have encouraged the development of new algorithms in visualizing flow data.

4.1.1. Integration-based and Geometric flow visualization technique

Seed Placement Strategy

Most of the research in integration based visualization focus of the seed placement

strategy and curve or geometric object generation process. These two areas highlight different

important properties from the flow features. The sub-sections will focus on seed placement

strategy and the followed by the geometric object generation process.

An improved method for streamlines placement is proposed in [8]. They used image-

guided technique to place streamlines in a proper place and gap by using low-pass filter in the

energy function. This will show the visual density difference between the current and the target

images. The approach reduces the density, thus improving the placement of streamlines by

altering the position and length of streamlines, combining the near adjacent lines and creating

a b

new streamlines to fill the large gap. The result manifests a better hand-placed appearance than

a regular or randomly–placed streamlines.

In order to generate an evenly spaced streamlines, an algorithm developed in [9] to

visualize 2D steady flow. The contribution of the work was to compute a wide range of flow

field sources (texture based up to hand-drawing style). The result produced clean, long, and

evenly-spaced streamlines with accurate control of visual density. The visualization lines are

less cluttered, making it easier to convey the flow information. However, this technique has

difficulties in visualizing small turbulence in large flow field due to the property of the method.

Another seeding strategy was presented in [10] to improve the seed placement by using

the information from the flow features in the data sets. The aim of the approach is to identify the

flow pattern in the critical points even when the density of the streamlines is reduced. They also

emphasised streamlines placements at the non-critical regions with varies streamlines length to

make a clear visualization through out the dataset. The advantages of this method is that the

flow pattern close to the critical point can be visualized. The drawback of this method is it does

not handle higher order critical points but it can be extend to place seed correctly in the area of

higher order critical points.

A novel algorithm focusing on high quality placement to generate long streamlines is

proposed. The algorithm focuses on placement of streamlines based on the 2D steady flow

vector or direction field [11]. The goal of the algorithm is to produce a high quality placement

by favouring long streamlines, while retaining the uniform pattern with increasing density. This

can be achieve by placing one streamlines at a time with the help of numerical integration

starting at the furthest away from all previous placed streamlines. This technique manage to

achieve the simplicity, robustness and efficiency by applying the Delaunay triangulation to

generate the streamlines.

An advance evenly-spaced streamlines placement algorithm is presented in [12] was

introduced as improvement of work done in [9]. They employed a fourth-order Runge-Kutta

integrator and adaptive step size error control to acquire rapid accurate streamlines advection. In

order to reduce the amount of distant checking, they adopt Cubic Hermite polynomial

interpolation with large sample spacing to create fewer evenly-spaced sample along the

streamlines. They also propose an ideal loop detection strategy to handle spiralling and closed

streamlines. The results shows the algorithm perform faster than algorithm in [9] based on the

order-of-magnitude.

Encouraged by the idea of abstract drawing which focuses on the explanatory quality in

art, a new seeding strategy is proposed in [13] to generate illustrative 2D streamlines that

focuses on visual clarity and evidence. The basic idea of the algorithm is to highlight the flow

field effectively with a minimum set of streamlines. In order to produce the result, 2D distance

filed is generated to identify the distance between each grid point to the nearby streamlines.

Local metric is derived to calculate the dissimilarity between the original fields to the

approximate field based on the distance field. Global metric assist the process of identifying the

dissimilarity of the streamlines pattern based on the local metric result and decide whether to

generate new seed point at the local point. The process is repeated until there is no more

dissimilarity found in the flow field. There are 3 benefits of this technique firstly, the density of

streamlines is related to the natural flow features of the vector field. Secondly, the technique do

not rely on the detecting the critical point. Lastly, the most important point is the result reduce

the visual cluttering problem. The drawback of this technique is that the performance is lesser

that the technique proposed in [12] which is faster and do not depend on the flow field feature.

Animation

Animating the streamlines can improve the viewers’ understanding about the flow

visualization. This concept is implemented in [14] where they developed a unique technique to

animate streamlines based on the data structure called Motion Map. This data structure valuable

information such as flow field dense representation and motion information required to animate

the flow. The advantage of using method is that computing the Motion Map does not consume

more time than computing a single flow image and this step only required to execute once. The

result is much better if compare to LIC-based technique. The main drawback of the technique is

that it only able to be used with 2D steady flow. Future work will address the issues of

animating the unsteady flow and add another dimension to become 3D flow.

Method of animating streamlines is improved in [15] by using a new approach to

produce a complete cyclic with variable-speed animation for 2D steady vector fields based on

work in [8], [9]. The animation frames are encoded in a single image and played using colour

table animation technique. The cyclic effect can be produced as well and then encoded in a

common animation format or used it for texture mapping on 3D objects. The advantages of this

technique is that the animation produce is smoother and optimize in term of memory

requirement and the computation duration.

Another work related to the animation in flow visualization is presented in [16] which

also focusing in unsteady flow. Streamlines generation method is modified in order to visualize

unsteady flow by using specialized Navier-Stoke equation. The unsteady flow input is integrate

in the time domain as the flow field temporal resolution increased. A grid hierarchy is

implement to the seed point in order to produce evenly spaced streamlines and control the

densities of the streamlines. Figure 6 shows the pseudo code on implementation of grid

hierarchy in streamlines generation. The algorithm successfully reduce the amount of sample

calculated while maintaining the accuracy of the streamlines tracing process.

Figure 6. Pseudo code for streamlines generation algorithm [16]

Optimization

Optimization is important in flow visualization because of the high computational

process in visualizing flow data. A system is develop in [17] to improve 3D flow visualization.

The aim of the work is to overcome the limitation of bandwidth between CPU and GPU as well

as the computation capability. The work presented used particle system to visualize flow

interactively by accelerating the particle integration process and avoid the rendering process for

transferring targeted particle sets. They utilize the capability of the graphic processing unit of

GPU in order to fasten the particle advection process. Data transfer between CPU and GPU is

nearly avoided caused some essential APIs call is required from CPU side to start the GPU

operation. The position of the particle is stored in the graphic memory to ease of access of

obtaining images in the frame buffer. This system allow millions of particle to be rendered and

streamed interactively with the virtual exploration features.

The computation cost used to visualize flow has increase from time to time as the

simulation capability improved in producing complex result. Since then, parallel computing is

used in flow visualization to distribute the computational cost among connected node but there

are less work focusing on partitioning the flow datasets. Motivated by this problem, a work on

optimizing the parallel performance for flow visualization is presented in [18]. They able to

repartition the flow data based in the flow direction and the features for large unstructured flow

data. The partitioned data will be stored in the distributed-memory and each processor able to

focus on specific streamlines without passing streamlines generation process back and forth to

other processors. Conventional method of partitioning the flow data does not focus on the flow

visualization process. This will lead to increasing in time and computational cost. By assigning

the partition based on the seed point and the flow feature, the process is able to obtain good load

balance among the processors.

Similar work also done in [19] to improve time and streak surfaces generation process

for large unsteady flow data sets. A novel algorithm is proposed in solving the problem of

generating surfaces for unsteady flow. Surface advection and adaptation is separated which

proc hierarchical_streamline()

{

create_multilevel_grids(numLevel,grids);

for(each level)

do {

trace_one_level_streamlines(level);

if(level<MAX) filter_short_streamlines(level);

if(level<MAX) set_next_level_flag(level+1);

if(level<MAX) extend_streamlines(level+1);

}

}

proc trace_one_level_streamlines(level)

{

for(each cell)

do {

if(unmarked(cell)=True){ seed = create_seed(cell);

if(is_valid(seed)==True) trace_streamline(seed); }

}

}

improve the surface tracking method by executing both of the process at the same time at

different CPU core. The algorithm able to produce high quality rendering result with interactive

visualization of time and streak surface. Their work also able to show the temporal evolution of

the surface. This method also able to solve some of the obstacles of seeding placement for

surface in large flow dataset by pre-compute the path of the seed curve.

Others

A work focusing on multi-resolution flow visualization is presented in [20] to enrich the

information for close-up view and long shot view. The paper present a method to compute a

series of streamlines-based images of a vector field with different densities, varying from sparse

to texture-like representation. The streamlines position is based on [9]. It allow user to view a

clearer flow visualization while zooming in and out in the vector field and adjust the density if

the streamlines. The density of streamlines also can be compute automatically based on the

velocity and vorticity.

Streamlines selection is very important in conveying the flow information. The amount

of streamlines in the scene need to be adequate to avoid visual perception problem. The

viewpoint also plays important role in viewing the scene at the correct position. A unified

framework is presented in [21] in order to enhance the visualization result. The framework is

made up by two information channel that relate a set of streamlines and a number of sample

viewpoints. The researchers solve streamlines selection problem using two methods which are

probability distribution of streamlines set and streamlines information. Probability distribution

is used to find the importance of every streamlines from the viewpoint set. Streamlines

information is the degree of dependence of a streamlines with the viewpoints. The viewpoint

selection also follow the similar process with streamlines selection. Viewpoint information is

used to choose the best viewpoint to be stored in the viewpoint set. The framework able to

select and show appropriate streamlines set with a set of suitable viewpoints.

4.1.2. Dense and Texture Based technique

Dense and Texture Based technique is another method of visualizing flow data. Instead

of focusing on certain feature in the flow data, texture based method visualize the whole spatial

resolution with dense representation of the flow. One of the advantages of using this method is

to solve the seed placement problem [4]. In order to produce a dense representation, a series of

process is required to combine texture with the flow field. A work on this method has been

presented in [22] which become the main reference in texture-based flow visualization. Line

integral convolution (LIC) technique proposed use linear and curvilinear filtering to blur the

texture according to the vector field. The technique filter the texture or images to the vector data

and generate the visualization. Flow direction can be shown using continuous motion filter

which display apparent motion in the direction of the flow data.

An advancement of LIC is presented in [23] for two dimensional flow visualization.

Image Based Flow Visualization (IBFV) is applicable to different flow visualization such as

moving particle, streamlines and feature-based method. IBFV able to visualize unsteady flow,

with efficient and easy implementation. This method also able to produce the flow animation

with the frame rates up to 50 on a conventional graphic hardware and feature. Error!

Reference source not found. visualizes the process for the image based flow visualization.

Figure 7. Image based flow visualization processes [23]

Assume the image set that usually has the similar features is ready to process. The animation

process is achieve by projecting the image onto rectangular meshes and move the mesh point to

a distance based on the local flow. The process continue by render and distort the images. There

are several problem of the process is repeated. The deformation accumulated will produce a

cells with different shape from the initial shape. This can be solve by repeating the same step on

the same rectangular shape mesh. The flow advection also will cause the initial image will be

distorted and hidden from the viewport. In order to solve this problem, the new image is blend

with the distorted image to preserve the result for the next iteration.

The work on IBFV is extended in [24]. They introduce image based flow visualization

for curved surface (IBFVS) method which based on image warping and blending based on a

single framework. The use mesh of triangular to create the geometric model for the surface.

This allow the model to be cover mostly by the texture because of property of triangle mesh

which is a generic format. Increasing the number of the triangle also will increase the level of

detail of the result. The algorithm also perform faster by exploiting the ability of graphic

processor. Figure 8 show the pipeline for IBFVS. The method start by initializing the texture to

the model with specific background colour. Texture coordinate is calculated for all vertices

before it is render and mapped without shading. Then, noise is blend on the surface. The blend

result is stored to be used for the next cycle before the shaded is rendered and blend together for

the final result. IBFVS inherit the advantages of its predecessor which can be implemented and

able to produce different kind of effect.

Figure 8. Image based flow visualization for curved surface [24].

Another similar work presented in [25] which also focus on dense representation of

flow field on the surface. They come out with new algorithm that able to generate dense

representation of fluid flow on complex surface from the computational fluid dynamics named

Image Space Advection (ISA). The algorithm also can be used to visualize other flow data

related with surface. The conventional method of texture-based is start by mapping the 2D

texture to the surface geometry in the 3D space. The mapped textured is then rendered to the

image space. The algorithm operate differently than the conventional method by projecting the

surface geometry first before applying the texture. This method allow texture advection to be

done in the image space, resulting a well flow visualization using dense representation. It can

visualize flow on a sophisticated surface which consist more than 200,000 polygons. User

interaction also is enable for operation such as translation, zooming and rotation. This method

also able to animate the visualization up to 20 frame per second on robust meshes with time-

dependent geometry. A detail comparison of ISA and IBFVS is discuss in [26] which highlight

the strength and weakness of both methods. The paper also suggest the appropriate situation to

use each methods.

Transfer function is usually used in volume visualization in arranging the colour and the

opacity of the volume in the scalar field. One of the effective ways of assigning these features is

by using Multi-Dimensional Transfer Function (MDTF). As the texture-based visualization

method for 3D flow data is capturing researchers attention, it is important to introduce a similar

MDTF that can be implement to steady and unsteady flow data. driven by this cause, a work is

presented in [27] that develop MDTFs for 3D flow visualization. Vector field properties derived

from vector field calculus is used to map with the transfer function. Vector calculus can provide

transfer function with properties such as velocity magnitude, velocity, curl magnitude, gradient

tensor determinant, divergence and helicity based on the 3D flow field. These properties is

combined to form the new MDTF. It allow viewers to explore and highlight flow features

interactively in 3D flow field. they implement MDTF with 3D IBFV method in [28] in order to

produce image based flow visualization for 3D flow dataset. Figure 9 show the interface to

control the MDFT and apply it on tornado dataset. Each component can be adjust to highlight

certain feature of the flow visualization. For example, Figure 9(a) show the option to show flow

features with high curl magnitude with orange to red colour while separate the region of high

positive and high negative of divergence value with blue and white colour. One of the major

advantages of applying MDTF into the 3D flow dataset is it able to overcome the occlusion

problem which has been the major drawback of flow visualization in 3D.

Figure 9(a). Five component which are vector magnitude, velocity gradient tensor determinant,

curl magnitude, helicity and divergence that used to control MDTF. Figure 9(b). The flow

visualization result of 3D IBFV with MDTF [27].

One of the problems that is left aside in flow visualization research is the uncertainties

of data that caused by various factors, such as noises from data acquisition process and

properties in display process [29]. A work related to with uncertainty in flow visualization is

presented in [30]. They present two new methods in order to visualize the uncertainty in 2D

unsteady flow dataset. The first error filtering method used cross advection method which

applies perpendicularly to the flow direction. The basic concept of this method is to move the

particle towards the flow direction literally and then generate streaklines before smearing the

particle. This will produce a one dimensional convolution which looks like the conventional

texture-based flow visualization. The second method is error diffusion which implement

Gaussian filtering that employs 2D isotropic filter kernel. Unlike the first method, it does not

depend on the flow direction and the effect of the diffusion filter is blurs in all direction. Linear

interpolation between a complete Gaussian kernel and an identity mapping is used to construct

the filter. The uncertainty value is used as a weightage for the constructed filter. This will

produce a variety of result from an identity mapping in region with no uncertainty value up to

the standard Gauss filter with highest uncertainty value. Error! Reference source not found.

shows the flow visualization result of uncertainty for both methods. The difference between

both methods is show in Figure 1, notice that the smear and blur effect is obvious when the

streaklines have high uncertainty value.

(a) (b)

Figure 90. (a) Result of the cross advection method. (b) Result using Gaussian error

diffusion method [30].

As the hardware performance improved, the rendering performance for texture-based

flow visualization also increased. But some of the methods are not high spatial-correlated cause

by messy flow direction in the visualization and low intensity contrast between streamlines. A

work in [31] has presented an algorithm to enhance unsteady flow visualization using texture-

based method with the ability to produce high spatial-temporal coherence animation at

interactive rates. The input texture for the initial LIC convolution used random noise that

represent the massless particle. 1D high-pass filtering flow texture is used to enhance

streamlines along the orthogonal flow direction. This process allow spatial coherence for every

image frames to reveal the pattern that represent the flow structure of the vector data. User able

to identify the motion consistency in the flow pattern due to high quality of temporal coherence

which is achieved by convoluting the texture property iteratively. Error! Reference source not

found.Error! Reference source not found. show the comparison of result between FastLIC

method and the presented method. The streamlines produced much clearer thus provide better

information about the flow pattern. The algorithm also implement on the surface with several

improvement in vector projection on surface, texture convolution edge detection and

supplement [32]. The result of the algorithm is a well rendered texture with the presence of

streamlines showing the flow pattern.

(a) (b)

Figure 81. Comparison of cross advection (left) and error diffusion (right) effect on the

streaklines [30].

Figure 12. Comparison result of FastLIC (left) and result of proposed algorithm (right) [31].

Another similar work that produce streamlines-like texture-based flow visualization is

presented in [33]. They develop a thickness function of streamline and wave source. These

function are used to process and generate an image with a coherent wave interference pattern.

This works is motivated by the advantage of texture-based method which able to show global

flow structure and streamlines able to produce high contrast result with simple (computation for

integral and rendering of lines) and low computational cost. They come out with an ideal

framework based on fully-automated eigenvalue computation scheme. This framework only

require two parameter which are base streamlines thickness and thickness variation. The method

start by using affinity function between image pixels corresponding to the streamlines thickness.

The intensity of each pixels is presented in the visualization image as a total of coherent

sinusoidal wave. The wave is propagated from its neighbourhood in the space induced by the

function. The result of the texture-based visualization is neat, free from fuzzy and able to be

converted to black and white vector graphic if required for further processing.

Image-space method able to generate dense representation of flow pattern with better

performance compared to surface parameterization method. This is because it only focus on the

visible surface from a given viewpoint. Technically, the method project the vector field and

geometry of the surface to the viewing screen and then apply texture advection in the image

space. But it face a problem when the user change the viewpoint. This method cannot maintain

the texture consistency when user rotate or change the viewpoint distance. This is because the

noise textured generated independently for every frames on the image space during viewpoint is

moved. In order to solve this problem, a novel image-space visualization technique is presented

in [34] to handle texture inconsistency by proposing a fix texture position to each vertex with a

triangular-texture matching method. This technique will hold the texture in place at different

viewpoint position and angle thus avoiding the texture inconsistency.

5. ANALYSIS AND DISCUSSION

We have classified the approaches in flow visualization into two categories of 2D and

3D data which include integration-based and dense or texture-based methods. There are other

approaches which are not included in the classification such as feature and topology based

methods. Although both approaches show different ways of visualizing the flow pattern, the

focus of developing new visualizing method share the same goal which is to solve issues such

as visual artefacts, occlusion and perception problems, computing performance, visualization

quality, as well as optimization of the rendering process to achieve interactive frame rates. Each

approach has each own advantages and disadvantages. So, knowing the approach capabilities

will help in choosing the proper method to visualize the flow.

Integration-based approach in known with the ability to provide local flow pattern

information. The seed placement process is important in highlighting the crucial information in

the vector dataset. There are many well-developed algorithms for seed placement including the

reviewed algorithm in the section 4. Result produced by integration-based is also high in

contrast which clearly show the flow pattern. The core operation from computing the integral

curve up to rendering lines is basically low. The wide range of method available in this

approach give the choice to the researcher to implement it based on the dataset and visualization

requirement. Most of the integration-based approach do not has problem in visualizing 2D and

3D flow dataset as the approach is straightforward and only require vector data and seed point

positon to visualize the flow.

While integration-based approach is focusing on local flow pattern, texture-based able

show the global flow pattern by applying and convoluting the noise texture on the whole area of

the vector data. The process of implementing this approach is quite straightforward and has high

flexibility in generating different visualization output. It can produce a native texture-based

visualization up to resemble geometric lines for flow visualization. The output result can easily

be changed by adjusting the kernel and the input noise texture. This method is also GPU

friendly since most of the operations developed can be implemented on the GPU. Executing the

algorithm to the parallel processing on GPU with correct setting will boost the performance of

the algorithm.

There are different research of interest in flow visualization body of knowledge.

Researchers can contribute to various part of flow visualization pipeline in order to improve the

quality and performance of the visualization. Research focus that related with flow visualization

also depend on the approach discussed in section 4 of this chapter. Some of the focus area such

as optimization and animation are sometimes overlapping each others. Understanding the core

process of implementing these approaches will help to identify the domain of interest and the

possible contribution to flow visualization knowledge. Figure 1 shows the process in blue

colour border where researchers can improve in flow visualization. There are also shared

interests for the approaches that may or may not have the same implementation strategies.

Seed placement is the starting point of integration-based approach. It will determine the

characteristic of the flow visualization. The streamlines density is directly correlated to the seed

placement method. A dense streamlines does not mean the visualization is better because the

visual complexity also increase thus limiting the information that can be obtain by the viewer.

Same goes to texture-based flow visualization. The control parameters that affect the

visualization result are the input noise and the kernel. Any input noise texture can be used in

this approach but different kind of noises may highlight the flow pattern differently. The choice

of kernel gives a major visual effect in texture-based flow visualization. The length of the kernel

controls the streamlines length. The shape of the kernel decides the feature of the streamlines

and the characteristic of the filter. Usually, the kernel is preferable to have the features of a low-

pass filter that allow the viewer to recognize the line in the texture. Researchers can develop

new or improve current kernel in order to obtain clearer flow visualization by using texture-

based approach.

Research in solving the problem related visual artefacts are also becoming one of the

focuses in flow visualization domain. Occlusion and visual perception become major challenge

in visualizing the flow especially in 3D flow dataset. Occlusion often occur when the

visualization dense with visualization object or texture. Identifying the method to control the

density of the visualization as well as highlighting only the important part of the flow feature

will reduce the occlusion problem. Visual perception causes viewer to confuse in understanding

the visualization result. Error! Reference source not found. show an example of visual

perception. An arrow in the 3D space can be misinterpreted as pointing backward or forward.

The problem also can be found in flow visualization using streamlines where curve lines are

twirling at the same area. This open another research interest in flow visualization as there are

number of problems in this area available to be solved.

Flow Visualization

Integration-Based Texture-Based

Seed Placement

Share research interest in optimization

Animation Algorithm Resource

Management Rendering

Kernel Visual Artifact

Figure 103. Field of research interest in flow visualization

Figure 114. Visual perception problem occur in 3D space [3].

Flow visualization plays around with large datasets before come out with beautiful flow

visualization. The complexity of flow visualization increases if the dataset involved time-

dependent flow field. In many unsteady flow cases, finding a proper seed placement for

integration-based visualization can be a tedious task and costly to compute. Similar problem in

texture-based where series of post-processing is required to achieve good contrast and with

distinct flow feature. The increasing spatial and temporal resolution of the dataset also consume

a lot of machine memory. All of these problems require optimization algorithms that can solve

and improve the visualization process. Optimization also can be done to improve the usage of

hardware resources such as memory, CPU and GPU. For example, parallel processing able to

increase the performance of the process but it need to be implement in the correct way.

Rendering process and animation process also shared by both flow visualization

approach. Rendering cover a number of process before displaying the result. One of the

processes that can be improved is the view transformation process. Viewpoint help to enhance

the result by displaying the flow pattern at a position and angle which capture the important

information. Finding a proper viewpoint will leads to a better understanding about the flow

information. Other processes that can be improved such as hidden surface removal, filtering

(motion blur and anti-aliasing), and lighting also fall into rendering process.

Researchers can also look into problems in animating flow visualization. Animation can

reveal more information about the flow pattern by pre-computing series of frames and play it

back together. Real time animation can be achieve by rendering the frames during display. Both

methods have its own advantages and disadvantages. Pre-computed frame can produce

smoother animation but require longer time to process. Real-time and fast animated

visualization display is achievable but highly dependent on the data and visualization

complexity. The viewpoint is required to be properly placed in getting the best view of the flow

pattern before producing the animation. Animating unsteady flow also a tedious task because

the content of each frame is different. The velocity field needs to be computed for each frame in

tracing the particle position. For example, a total of 300 frames is required to be computed in

order to produce an animation with duration of 10 second with 30 frame per second. Optimizing

the velocity field computation will assist by shortening the time taken to produce the animation.

Algorithms for identifying proper viewpoints and paths between them are also useful in

increasing the information comprehension from the animated visualization.

6. APPLICATIONS AND AVAILABLE SYSTEMS

Flow visualization has been used widely in scientific visualization especially in the

study of aerodynamics, meteorology, oceanography, fluid mechanics as well as medical. In this

section, two applications related to flow visualization in cardiovascular will be described. In

general, most of the clinician or medical practitioners do not prefer flow visualization as a

method in supporting the decision making process related to cardiovascular diseases. Flow

visualization in cardiovascular only provides the flow of blood inside the heart and related

vessel. Doppler ultrasound is being used as one of the convenient ways to study the blood flow

inside the heart. The result is much more reliable and accepted by medical practitioner. Recent

research on flow visualization in cardiovascular has improved a lot in term of quality and

performance. They also come out with software that available for research and commercial use.

One of the software that available for researchers is FourFlow. FourFlow is an open

source, clinically applicable and user friendly software developed by researchers from Cardiac

MR Group in Department of Clinical Physiology / Clinical Sciences, Lund University. The

software is able to do quantitative visualization of 3D time dependent including three

directional blood flow inside the heart. The software is based on ParaView which is a well-

developed application for scientific visualization. Some of the scientific visualization operation

that can be done using this software are particle trace, streamlines, animation and isosuface

visualization. One of the work that related with the software has been publish in [35] which

study a volume tracking method for qualitative assessment and visualization of blood flow

inside the heart.

Morpheus Imaging, Inc., one of member of StartX network, a Stanford acceleration

program for entrepreneur has developed a 4D flow MRI imaging technology to assess and

visualize the blood flow within the heart. The process is done by using real time cloud

technology to achieve an accurate 4D intracardiac flow data from conventional MRI scanner.

The technology connects several machines in the cloud to compute and produce the result using

an advanced processing algorithm. The product has received market clearance from the

European Union and US FDA which means that the result of the system is trusted and reliable.

7. CONCLUSION

Flow visualization has become one of the most active research fields within the

scientific visualization community. General knowledge about flow visualization and related

researches has been reviewed. One of the research fields that benefited from flow visualization

is the cardiovascular blood flow study. The use of flow visualization in cardiovascular study is

increasing as there is potential to use flow visualization to solve problem related to blood flow

circulations. There are few research conducted on blood flow analysis which apply the flow

visualization knowledge to study and identify the flow pattern behaviour for patients with heart

problem. The study has the potential in helping the clinicians by showing the position of

irregular blood flow. The study also can be used to check the blood flow of a patient before and

after cardiovascular related operation.

The development of future Ventricular Assist Device (VAD) requires flow analysis in

identifying the best design in assisting the heart functions. Research related to VAD is

increasing. VAD is known to be one of the methods in treating patients with heart problem.

VAD design is very important because the device will be attached directly to the human heart.

Optimizing the flow inlet and outlet will ensure the device works as a healthy heart. There are

many other scientific applications of flow visualization where researchers are encouraged to

explore where flow visualization extends the common visual representations. Flow visualization

studies cover the importance of conveying vector information in a proper way, assisting viewers

to identify region of interests and assisting in the information comprehension from the

visualization.

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