Image Processing on Diagnostic Workstations 9UNIX! operating system!. Today, most systems are...

Image Processing on Diagnostic Workstations 123

B. M. ter Haar Romeny, PhDProfessor, Eindhoven University of Technology, Dept. of Biomedical Engineering, Image Analysis and Interpre-tation, PO Box 513, WH 2.106, 5600 MB Eindhoven, The Netherlands

Image Processing on Diagnostic Workstations 9Bart M. ter Haar Romeny

9.1 Introduction

Medical workstations have developed into the super-

assistants of radiologists. The overwhelming pro-

duction of images, hardware that rapidly became

cheaper and powerful 3D visualization and quanti-

tative analysis software have all pushed the devel-

opments from simple PACS viewing into a really

Scientifi c terms marked with ! are explained in Wikipedia: www.wikipedia.org

versatile viewing environment. This chapter gives

an overview of these developments, aimed at radi-

ologists’ readership. Many references and internet

links! are given which discuss the topics in more

depth than is possible in this short paper. This paper

is necessarily incomplete.

Viewing stations are core business in a radiolo-

gist’s daily work. All big medical imaging industries

supply professional and integrated environments

(such as Philips ViewForum, Siemens Syngo X, GE

Advantage, etc.). There are dedicated companies for

viewing software (a.o. Merge eFilm) or OEM solu-

tions (a.o. Mercury Visage, Barco). The application

domain of workstations is increasing. We now see

them regularly employed in PACS and teleradiol-

ogy diagnostic review, 3D/3D-time (4D) visualiza-

tion, computer-aided detection (CAD), quantitative

image analysis, computer-assisted surgery (CAS),

radiotherapy treatment planning, and pathology.

Also the applications for medical image analysis in

the life-sciences research are increasing, due to the

increased attention to small-animal scanning sys-

tems for molecular imaging!, and the many types

of advanced microscopes (such as confocal micros-

copy! and two-photon laser scanning micro-

scopes!), all giving huge 3D datasets. The focus of

this chapter is on image processing (also termed

image analysis or computer vision) applications.

9.2 Hardware

Early systems were based on expensive hardware

platforms, called workstations, often based on the

UNIX! operating system!. Today, most systems are

C O N T E N T S

9.1 Introduction 123

9.2 Hardware 123

9.3 Software 125

9.4 3D Visualization 125

9.5 Computer Aided Detection (CAD) 127

9.6 Atlases 128

9.7 CAD/CAM Design 128

9.8 Diffusion Tensor Imaging! (DTI!) – Tractography! 129

9.9 Registration 129

9.10 RT Dose Planning 129

9.11 Quantitative Image Analysis 130

9.12 Workstations for Life Sciences 131

9.13 Computer-Aided Surgery (CAS) 132

9.14 New Developments 133

9.15 Outlook 133

References 134

NERI_09-ter Haar Romeny.indd 123NERI_09-ter Haar Romeny.indd 123 28.08.2007 08:34:2128.08.2007 08:34:21

124 B. M. ter Haar Romeny

based on readily available and affordable PC and

Mac hardware platforms (running MS-Windows

or Mac-OS respectively), which are still following

Moore’s law! of increasing performance (a doubling

every 24 months) at a stable price level.

The central processor unit (CPU!) is the core of

the system, running today at several Gigahertz, and

performance is expressed in Giga-FLOPS! (109 fl oat-

ing point operations! per second). Famous CPUs are

the Intel Pentium chip, and the AMD Athlon proces-

sor. Today, we see the current 32 bit processors being

replaced by 64 bit processors, which are capable of

processing more instructions simultaneously and

addressing a larger number of memory elements

(232 = 4.2 × 109, so a 32 bit system cannot have more

than 4.2 GB of memory (109 = Giga)). There is also a

trend to have more CPUs (‘dualcore’) on the moth-

erboard!, paving the way to parallel processing,

which is currently still in its infancy.

The memory in the diagnostic workstation is

organized in a hierarchical fashion. From small to

large: the CPU has a so-called cache! on its chip,

as a local memory scratchpad for super-fast access,

and communicates with the main RAM (random

access memory!, today typically 1–4 GB) through

the data bus!, a data highway in the computer. As

the RAM is fully electronic, access is fast (nanosec-

onds), much faster than access to a local hard disk!

(milliseconds). When the RAM is fully occupied, the

CPU starts communicating with the hard disk. This

explains why increasing the RAM of a slow com-

puter can markedly upgrade its performance. In a

PACS system, the disk storage is typically done on

a ‘redundant array of inexpensive disks’ (RAID!),

where many disks in parallel prevent loss of data in

case of failure of a component.

The speed of the network should be able to accom-

modate the network traffi c. Typically the workstation

is part of a local area network (LAN!). Today giga-

bit/second speeds are attained over wired networks,

wireless is slower (30–100 Mbit/s) but convenient for

laptops and ‘person digital assistants’ (PDAs). Many

PACS installations can be serviced remotely through

LAN connections to the supplier, anywhere.

Networks are so fast nowadays that 3D volume

rendering can be distributed from a central pow-

erful computer to simple (and thus low cost) view-

ing stations, called ‘thin clients’! (a.o. Terarecon

Aquarius). A powerful dedicated graphics board (in

this case the VolumePro 1000) with dedicated hard-

ware runs several 3D viewing applications simul-

taneously, and is remotely controlled by the users

of the thin clients. Advantage is the capability to

handle huge datasets (e.g. > 3000 slices) easily, but

scalability (to e.g. dozens of users) is limited.

Interestingly, the power of ‘graphical processing

units’! (GPU!, the processor on the video card!

(or graphics accelerator card!) in the system) has

increased even faster than CPU power, mainly due

to the fact that GPUs form the core of the computer

game industry. The millions of systems needed

for this lucrative market and the high competition

between the market leaders NVIDIA and ATI have

Fig. 9.1a–c. Brain aneurysm (a) and carotids (b): examples

of volume renderings with a computer game graphics card

(3Mensio Inc) (c)

a

c

b



created a huge performance/price ratio. A GPU has

a 50 times faster communication speed of the data

internally between memory and processor, and has

dedicated hardware for rendering artifi cial environ-

ments, such as texture mapping!, pixel shaders!

and an intrinsic parallel design with pixel pipelines!.

They have fi nally become fully programmable (and

can be instructed by languages as DirectX! and

OpenGL!) and are equipped with 1–1.5 gigabytes of

local memory. These ‘games’ hardware boards are

now increasingly used in 3D medical visualization

applications (a.o. 3Mensio Medical Systems). There

is also a community exploring the use of GPUs for

general processing (DICOM undated a).

The viewing screens of diagnostic workstations

have to be of special diagnostic quality. Excellent

reviews of the important parameters (resolution,

contrast, brightness, 8, 10 or 12 bit intensity range,

homogeneity, stability, viewing angle, speed, etc. are

available in the so-called white papers by a variety

of vendors (a.o. Barco – Barco undated, Eizo – Eizo

undated).

9.3 Software

The revolution in PACS (and teleradiology) viewing

stations was fi red by the standard “Digital Imag-

ing and Communications in Medicine” ! (DICOM)

standard (DICOM undated a), 4000 pages). In the

1990s the ACR (American College of Radiology) and

NEMA (National Electrical Manufacturers Associa-

tion) formed a joint committee to develop this stan-

dard. The standard is developed in liaison with other

standardization organizations including CEN TC251

in Europe and JIRA in Japan, with review also by

other organizations including IEEE, HL7 and ANSI

in the USA. It is now widely accepted. Convenient

short tutorials are available (Barco undated). As the

scanners and viewing software continue to develop,

new features have to be added to the standard con-

tinuously. Vendors are required to make available

their so-called conformity statements (see for exam-

ple Burroni et al. 2004), i.e. a specifi ed list of what

conforms to the current version of the standard.

The second revolution was the standardization

of the internal procedural organization of medi-

cal data handling in the ‘Health Level 7’! standard

(HL7) (DICOM undated a).

The basic function of a viewing station is the con-

venient viewing of the data, with a patient selection

section. The functions are grouped in a so-called

‘graphical user interface’! (GUI!). Versatile PC

based viewing packages are now widely available

(see RSNA 2006 for an extensive list), many also

offering ‘extended ASCI’! character sets for the

Chinese, Japanese and Korean markets.

Basic functions of the GUI include administra-

tive functions as patient and study selection, report

viewing and generation, and visualization func-

tions as cine loop, ‘maximum intensity projec-

tion’! (MIP), ‘multi-planar reformatting’! (MPR!)

including oblique and curved reconstructions, cut

planes, measurement tools for distances and angles,

magnifying glass, annotations, etc.

The development of computer vision algorithms

often follows a hierarchical pathway. The design

process (rapid prototyping) is done in high-level

software (examples are Mathematica!, Maple!,

Matlab!), where very powerful statements and

algebraic functionality make up for very short

code, but his is diffi cult to extent to the huge multi-

dimensional medical images. When the formulas

are understood and stable, the implementation is

made into lower languages, like C, C++, Java. When

ultimate speed (and limited variability) is required,

the code can be implemented in hardware (GPU!,

fi eld programmable gate array’s (FPGA!), dedicated

chips, etc.). Many packages offer software develop-

ment kits for joint development (e.g. MevisLab!

by MEVIS, ‘Insight Segmentation and Registration

Toolkit’ (ITK!) by NLM, etc.).

9.4 3D Visualization

The fi rst breakthrough in the use of workstations

has been by the invention of generating realistic 3D

views from tomographic volume data in the 1980s.

Now 3D volume rendering is fully interactive, at high

resolution and real-time speed, and with a wide vari-

ety of options, making it a non-trivial matter to

use it.

Many dedicated companies are now established

(such as Vital Images with Vitrea, Mercury Computer

Systems with Amira, Barco with Voxar, 3Mensio

with 3Vision, Terarecon with Aquarius, etc.). Often

a third party 3D viewing application is integrated



in the PACS viewing application, and supplied as

a complete system by such an ‘original equipment

manufacturer’! (OEM!).

The principle of ray tracing (‘rendering’!)

(Nowinski et al. 2005) is actually based on mimick-

ing the physics of light refl ection with the computer:

the value of a pixel in a 2D image of a 3D view (also

called a 2.5D view) is calculated from the refl ected

amount of light from a virtual light source, either

bouncing on the surface of the 3D data (this process

is called ‘surface rendering’!), or as the summation

of all contributions from the inside of the 3D data-

set along the line of the ray in question, composed

with a formula that takes into account the transpar-

ency (or the inverse: the opacity) of the voxels (this

process is called ‘volume rendering’!). The use can

change the opacity settings by manipulating the

so-called ‘transfer function’!, this function giving

the relation between the measured pixel value from

the scanner and the opacity. As there is an infi nite

number of settings possible, users often get con-

fused, and a standard set of settings is supplied, e.g.

for lung vessels, skull, abdominal vascular, etc., or

a set of thumbnails is given with examples of pre-

sets, from which the user can choose. Attempts are

underway to extract the optimal settings from the

statistics of the data itself (Nowinski et al. 2005).

In virtual endoscopy (e.g. colonoscopy) the

camera is positioned inside the 3D dataset. Chal-

lenges for the computer vision application are the

automatic calculation of the optimal path for the

fl y-through through the center of the winding colon,

bronchus or vessel. Clever new visualizations have

been designed to screen the foldings in the colon for

polyps at both the forward as backward pass simul-

taneously: unfolding (ter Haar Romeny 2004) (see

Fig. 9.1) and viewing an unfolded cube (Vos et al.

2003) (see Fig. 9.2).

Segmentation is the process of dividing the 3D

dataset in meaningful entities, which are then visu-

alized separately. It is essential for 3D viewing by,

e.g. cut-away views, and also, unfortunately, one

of the most diffi cult issues in computer vision. It is

discussed in more detail in Section 9.5. When clear

contrasts are available, such as in contrast enhanced

CT or MR angiography and bone structures in CT,

the simple techniques of thresholding and region

growing can be employed, up to now the most often

used segmentation technique for 3D volume visual-

ization.

This also explains the popularity of maximum

intensity projection!, where pixels in the 2.5D view

are determined from the maximum along each ray

from the viewing eye through the dataset (such a

diverging set of rays leads to a so-called ‘perspec-

tive rendering’!). As this may easily lead to depth

ambiguities, often the more appealing ‘closest vessel

projection’! (CVP) is applied, where the local maxi-

mum values closest to the viewer is taken. The sam-

pled points of the (oblique) rays through the dataset

are mostly located in between the regular pixels, and

are calculated by means of interpolation!.Fig. 9.2. Volume rendering of the heart and coronaries

( Terarecon Inc)

Fig. 9.3. Virtual colonoscopy with unfolding enables inspection of folds from all sides. From ter Haar Romeny (2004)

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9.5 Computer Aided Detection (CAD)

One of the primary challenges of intelligent soft-

ware in modern workstations is to assist the human

expert in recognition and classifi cation of disease

processes by clever computer vision algorithms.

The often used term ‘computer-aided diagnosis’

may be an overstatement (better: ‘computer-aided

detection’), as the fi nal judgement will remain with

the radiologist. Typically, the computer program

marks a region on a medical image with an anno-

tation, as an attention sign to inspect the location

or area in further detail. The task for the software

developer is to translate the detection strategy of

the expert into an effi cient, effective and robust

computer vision algorithm. Modern techniques are

also based on (supervised and unsupervised) ‘data

mining’! of huge imaging databases, to collect sta-

tistical appearances. E.g. learning the shape and tex-

ture properties of a lung nodule from 1500 or more

patients in a PACS database is now within reach.

Excellent reviews exist on current CAD techniques

and the perspectives for CAD (Doi 2006; Gilbert

and Lemke 2005). The fi eld has just begun, and some

fi rst successes have been achieved. However, there

is a huge amount of development still to be done in

years to come.

Some advances in CAD techniques that have

brought good progress are in the following applica-

tion areas.

Mammography: this has been the fi rst fi eld where

commercial applications found ground, in par-

ticular due to the volume production of the associ-

ated screening, the high resolution of the modality

and the specifi c search tasks. Typical search tasks

involve the automated detection of masses, micro-

calcifi cations, stellate or spiculated tumors, and the

location of the nipple.

How do such algorithms work? Let us look in

some detail to one example: stellate tumor detec-

tion (Hofman et al. 2006). As breast tissue con-

sists of tubular structures from the milk-glands to

the nipple, tumor extensions may preferably follow

these tubular pathways. In a projection radiograph

this leads to a spiculated or star-shaped pattern.

The computer will inspect the contextual environ-

ment of each pixel (say 50 × 50 pixels) on the pres-

ence of lines with an orientation pointing towards

the relevant pixel. In this way a total of 2500 ‘votes’

are collected for each pixel. The pixels with a voting

probability exceeding some threshold are possible

candidates for further inspection.

The location of the nipple is important as a gen-

eral coordinate origin for localization references

with, e.g. previous recordings. The general statis-

tical ‘fl ow’ of line structures points towards the

nipple; the location can reasonably well be found by

modeling the apparent statistical line structure with

physical fl ow models.

The role of MRI in breast screening is rising. As

in regular anatomical scans, too many false nega-

tive detections are found, and current attention now

focuses on dynamic contrast enhanced MRI. The

rationale is the high vascularity of the neoplasm,

leading to a faster uptake and outwash over time of

the contrast medium compared to normal tissue.

Current research focuses on the understanding of

this vascular fl ow pattern (e.g. by two-compartment

modeling) and the optimal timing of the image

sequence.

Polyp detection in virtual colonography: polyps

are characterized by a mushroom-like extrusion of

the colon wall, and can be detected by their shape:

they exhibit higher local 3D curvature! (‘Gauss-

ian curvature’!) properties. These can be detected

with methods from ‘differential geometry’! (the

theory of shapes and how to measure and character-

ize them), and highlighted as, e.g. colored areas as

attention foci for further inspection.

Methods have been developed to carry out an

electronic cleansing! of the colon wall when con-

trast medium is still present. An interesting current

target is possible to reduce strongly the radiation

dose of the CT scan, and still be able to detect the

Fig. 9.4. Unfolded cube projection in virtual colonoscopy.

From Vos et al. (2003)



polyp structures, despite the deterioration of the

detected colon wall structures. Clever shape smooth-

ing techniques and edge-preserving smoothing of

the colon surface have indeed enabled a substantial

dose reduction.

Thorax CAD: here the focus is on the automated

detection of nodules in the high resolution multi-

slice CT (MSCT) data, on the detection of pulmo-

nary emboli, and of textural analysis by classifi -

cation of pixels, e.g. for the quantifi cation of the

extent of sarcoidosis. See Sluimer et al. (2006) for

a review.

Other CAD applications include calcium scoring,

used to detect and quantify calcifi ed coronary and

aorta plaques, analysis of retinal fundus images for

leaking blood vessels as an early indicator for diabe-

tes, and the inspection of skin spots for melanoma

(of particular attention in Australia).

9.6 Atlases

The use of interactive 3D atlases on medical worksta-

tions is primarily focused on education and surgery.

As an example, K.-H. Höhne’s pioneering Voxel-

Man series of atlases (Hofman et al. 2006) was ini-

tiated by the ‘visible human project’!. Atlases for

brain surgery (e.g. the Cerefy Brain atlas family;

Nowinski et al. 2005) now become probabilistic,

based on a large number of patient studies.

9.7 CAD/CAM Design

Workstations can also assist in the creation of

implants from the 3D scans of the patients. This is

a highly active area in ENT, dental surgery, orthope-

dic surgery and cranio-maxillofacial surgery. Many

design techniques have been developed to create the

new shapes of the implants, e.g. by mirroring the

healthy parts of the patient of the opposite side of the

body, 3D region growing of triangulated ‘fi nite ele-

Fig. 9.5a–c. Virtual colonoscopy with surface smoothing. a Original dose (64 mAs); b 6.25 mAs; c 1.6 mAs. From Peeters

(2006b)

Fig. 9.6. The famous Voxel-Man atlas explored many types of

optimal educational visualization. From Höhne (2004)

ba c



ment models’! in the assigned space, etc. The ‘stan-

dard tesselation language’! (STL!) is a common

format to describe surfaces for 3D milling equipment

for rapid prototyping!, such as stereolithography!

systems, plastic droplets ditherers, fi ve-axes com-

puterized milling machines, laser powder sinter-

ing systems, etc. Many dedicated rapid prototyping

companies exist (e.g. Materialize Inc., see also www.

cc.utah.edu/~asn8200/rapid.html). In the medical

arena several large research institutes are active in

this area (Ceasar, Berlin; Co-Me, Zürich).

9.8 Diffusion Tensor Imaging! (DTI!) – Tractography!

Three-dimensional (3D) visualization of fi ber tracts

in axonal bundles in the brain and muscle fi ber bun-

dles in heart and skeletal muscles can now be done

interactively. The images are no longer composed of

scalar! (single) values in the voxels, but a complete

diffusion tensor! (a 3 × 3 symmetric matrix!) is

measured in each voxel.

The three so-called eigenvectors! can be calcu-

lated with methods from linear algebra!; they span

the ellipsoid of the Brownian motion! that the water

molecules make at the location of the voxels due to

thermal diffusion. Complex mathematical methods

are being investigated to group the fi bers in mean-

ingful bundles, to segment and register the DTI data

with anatomical data, and fi nd fi ber crossings and

endings automatically. An interesting development

is the photorealistic rendering of the tiny bundle

structures (with specularities and shadows), based

on the physics of the rendering of hair.

9.9 Registration

Registration, or matching, is a classical technique in

image analysis (Hajnal et al. 2001). It is employed

to register anatomical to anatomical, or anatomical

to functional data, in any dimension. Examples are

MRI-CT, PET-CT, etc. The construction of a PET and

a CT gantry in a single system effectively solves the

registration problem for this modality.

The matching can be global (only translation, ori-

entation and zooming of the image as a whole) or

local (with local deformation, also called warping!).

Registration can be done by fi nding correspondence

between (automatically detected) landmarks, or on

the intensity landscape itself (e.g. by correlation!).

There is always an entity (a so-called functional!)

that has to be minimized for the best match: e.g.

the mean squared distance between the landmarks,

a Pierson correlation coeffi cient, or others. In par-

ticular, for multi-modality matching, the mutual

information! (MI) has been found to be an effective

minimizer. As an example, in MRI bone voxels are

black and in CT white; they show as a cluster in the

joint probability histogram of the MR vs CT inten-

sities. The MI is a measure of entropy (disorder) of

this histogram.

9.10 RT Dose Planning

The accuracy of radiotherapy dose calculations,

based on the attenuation values of the CT scan of

the patient, needs to be very high to prevent under-

exposure of the tumor and overexposure of the

healthy tissue. Typically the isodose surfaces are

calculated and viewed in 3D in the context of the

local anatomy. Increasingly the images made in the

linear accellerator with the electronic portal imag-

ing device! (EPID) are used for precise localization

of the beam and repeat positioning of the patient,

Fig. 9.7. Muscle fi bers tracked in a high-resolution DTI

MRI of a healthy mouse heart. Lighting and shadowing of

lines combined with color coding of helix angle (αh). From

Peeters et al. (2006a)



by precise registration techniques. The low contrast

images (due to the high voltage of the imaging beam)

can be enhanced by such techniques as (adaptive)

histogram equalization!.

9.11 Quantitative Image Analysis

This is the fastest growing application area of medi-

cal workstations. The number of applications is vast,

every major vendor has research activities in this

area, and many research institutes are active. To

quote from the scope of ‘Medical Image Analysis’,

one of the most infl uential scientifi c journals in the

fi eld:

“The journal is interested in approaches that uti-

lize biomedical image datasets at all spatial scales,

ranging from molecular/cellular imaging to tissue

/ organ imaging. While not limited to these alone,

the typical biomedical image datasets of interest

include those acquired from: magnetic resonance,

ultrasound, computed tomography, nuclear medi-

cine, X-ray, optical and confocal microscopy, video

and range data images.

The types of papers accepted include those that

cover the development and implementation of algo-

rithms and strategies based on the use of various

models (geometrical, statistical, physical, func-

tional, etc.) to solve the following types of problems,

using biomedical image datasets: representation of

pictorial data, visualization, feature extraction, seg-

mentation, inter-study and inter-subject registra-

tion, longitudinal / temporal studies, image-guided

surgery and intervention, texture, shape and motion

measurements, spectral analysis, digital anatomical

atlases, statistical shape analysis, computational

anatomy (modeling normal anatomy and its varia-

tions), computational physiology (modeling organs

and living systems for image analysis, simulation

and training), virtual and augmented reality for

therapy planning and guidance, telemedicine with

medical images, telepresence in medicine, telesur-

gery and image-guided medical robots, etc.”

See also the huge amount of toolkits for computer

vision: http://www.cs.cmu.edu/~cil/v-source.html.

Important conferences in the fi eld are MICCAI,

CARS, IPMI, ISBI and SPIE MI. In the following

some often-used techniques are shortly discussed.

There are excellent tutorial books (Molecular

visualizations undated; Yoo 2004) and review

papers for the fi eld.

Segmentation! is a basic necessity for many sub-

sequent viewing or analysis applications. Mostly

thresholding and 2D/3D region growing are applied,

but these often do not give the required result.

Proper segmentation is notoriously diffi cult. There

are dozens of techniques, such as model-based seg-

mentation, methods based on statistical shape vari-

ations (‘active shape models’!), clustering methods

in a high-dimensional feature space (e.g. for tex-

tures), histogram-based methods, physical models

of contours (‘snakes’, level sets!), region-growing!

methods, graph partitioning! methods, and multi-

scale segmentation!.

The current feeling is that fully automated seg-

mentation is a long way off, and a mix should be

made between some (limited, initial) user-interac-

tion (semi-automatic segmentation).

Feature detection! is the fi nding of specifi c land-

marks in the image, such as edges, corners, T-junc-

tions, highest curvature points, etc. The most often

used mathematical technique is multi-scale differ-

ential geometry! (Ter Haar Romeny 2004). It is

interesting that the early stages of the human visual

perception system seem to employ this strategy.

Image enhancement! is done by calculating spe-

cifi c properties which then stand out relative to the

(often noisy) background. Examples are the likeness

of voxels to a cylindrical structure by curvature rela-

tions (‘vesselness’!), edge preserving smoothing!,

coherence enhancing!, tensor voting!, etc. Com-

mercial applications are, e.g. MUSICA (‘Multi-Scale

Image Contrast Amplifi cation’, by Agfa), and the

Swedish ContextVision (http://www.contextvision.

se/). Enhancement is often used to cancel the noise-

increasing effects of substantially lowering the X-ray

dose, such as in fl uoroscopy and CT screening for

virtual colonoscopy.

Quantitative MRI is possible by calculating the

real T1 and T2 fi gures from the T1 and T2 weighted

acquisitions, using the Bloch equation! of MRI

physics. Multi-modal MRI scans can be exploited

for tissue classifi cation: when different MRI tech-

niques are applied to the same volume, each voxel

is measured with a different physical property, and

a feature space can be constructed with the physical

units along the dimensional axes: e.g. in the charac-

terization of tissue types in atherosclerotic lesions

with T1, T2 and proton density weighted acquisi-

tions, fat pixels tend to cluster, as do blood voxels,

muscle voxels, calcifi ed voxels, etc. Pattern recogni-



tion techniques like neural networks! and Bayesian

statistics! may fi nd the proper cluster boundaries.

Shape can be measured with differential geomet-

ric properties, such as curvature!, saddle points!,

etc. It is often applied when, e.g. in the automated

search for (almost) occluded lung vessels in pul-

monary emboli, polyps on the colon vessel wall,

measuring the stenotic index, spiculated lesions in

mammography, etc. A popular method is based on

‘active shape models’!, where the shape variation is

established as so-called shape eigenmodes! by ana-

lyzing a large set of variable shapes and perform-

ing a ‘principal component analysis’!, a well known

mathematical technique. The fi rst eigenmode gives

the main variation, the second the one but larg-

est, etc. Fitting an atlas or model-based shape on a

patient’s organ or segmented structure becomes by

this means much more computationally effi cient.

Temporal analysis is used for bolus tracking

(time-density quantifi cation), functional maps of

local perfusion parameters (of heart and brain),

contrast-enhanced MRI of the breast, cardiac output

calculations by measuring the volume of the left ven-

tricle over time, multiple sclerosis lesion growth /

shrinkage over time, regional cardiac wall thickness

variations and local stress/strain calculations, and

in fl uoroscopy, e.g. the freezing of the stent in the

video by cancellation of the motion of the coronary

vessel.

9.12 Workstations for Life Sciences

In life sciences research a huge variety of (high

dimensional) images is generated, with many new

types of microscopy! (confocal!, two-photon!,

cryogenic transmission electron microscopy!,

etc.) and dedicated (bio-) medical small animal

scanners (micro-CT, mini PET, mouse-MRI, etc.).

The research on molecular imaging and molecu-

lar medicine is still primarily done in small animal

models.

There is great need for quantitative image analy-

sis. An example is, e.g. the measurement of quan-

titative parameters that determine the strength of

newly engineered heart valve tissue of the patient’s

own cell line, such as collagen fi ber thickness, local

orientation variation and tortuosity!. The source

images are from two-photon microscopy, where the

collagen is specifi cally colored with a collagen spe-

cifi c molecular imaging marker.

Another example is the detailed study of the micro-

vascular structure in the goat heart from ultra-thin

slices of a cryogenic microtome! (degree of branch-

ing, vessel diameter, diffusion and perfusion dis-

tances, etc.). Typical resolution is 25–50 micron in

all directions, with datasets of 20003.

Fig. 9.8. a Multimodality MRI of atherosclerotic plaque in the human carotid artery: (w1) T1-weighted 2D TSE, (w2) ECG-

gated proton density-weighted TSE, (w3) T1-weighted 3D TFE, (w4) ECG-gated partial T2-weighted TSE, (w5) ECG-gated

T2-weighted TSE. b Feature space for cluster analysis. c Classifi cation result. From Hofman et al. (2006)

a

cb



This research arena will benefi t greatly in the

near future from the spectacular developments in

the diagnostic image analysis and visualization

workstations.

9.13 Computer-Aided Surgery (CAS)

In the world of CAS some very advanced simula-

tion and training systems (KISMET, Voxel-Man)

have been created. Especially in dental implants,

craniofacial surgery and laparoscopic surgery there

are many and highly advanced systems today. Surgi-

cal navigation workstations are routinely displaying

the combination of the anatomy and the position

and orientation of the instruments in the operating

theatre.

An interesting development is the use of complex

fl uid dynamics modeling, which enables the predic-

tion of rupture chances in abdominal aorta surgery,

and selecting optimal therapeutic procedures with

bypass surgery in the lower aorta.

In neurosurgery workstations can be employed in

the calculation of an optimal (safest) insert path for

electrodes for deep brain stimulation (DBS), based

on a minimal costs path avoiding blood vessels and

ventricles, and starting in a gyrus. Workstations

assist in inter-operative visualization by warping

the pre-operative imagery to the real situation in

the patient during the operation, by intra-operative

MRI, or ultrasound.

Fig. 9.10. A 3D visualization of a microtome stack

(40 × 40 × 40 μm) of the micro-vasculature of a goat heart

(van Bavel et al. 2006) [Bennink 2006]

Fig. 9.11. Virtual laparoscopy trainer (Origin: Forschungs-

zentrum Karlsruhe KISMET)

Fig. 9.9a,b. Two-photon fl orescence microscopy of collagen fi bers of tissue-engineered heart-

valve tissue. a Result of structure preserving denoising. From Daniels et al. 2006

a b



9.14 New Developments

The visual perception of depth (when viewing 3D)

data is helped enormously if the viewer can move

the data himself. There are many depth cues (stereo,

depth from motion, depth from perspective), but

depth from motion is the strongest. That is why

maximum intensity projections (MIP) are prefer-

ably viewed dynamically. By self-tracking also the

muscle’s proprioceptors are giving feedback to the

brain, adding to the visual sensation. The combi-

nation with human’s superb eye-hand coordination

has led to the concept of the Dextroscope (www.dex-

troscope.com), where a (computer generated) view

or object can be manipulated under a half-trans-

parent mirror, through which the viewer sees the

display. Displays can also be equipped with haptic

(tactile) feedback systems, which are now commer-

cially available.

Super-large screens, and touch screens are becom-

ing popular; a new trend is the multi-touch screen

(http://cs.nyu.edu/~jhan/ftirtouch/ with movie),

where multiple positions to interact simultaneously

make more complex transformations possible, such

as zooming, multiple simultaneous objects interac-

tions, etc.

Fig. 9.12a–c. Abdominal aorta aneurysm: a color coding of displacement (mm); b Von Mises strain; c Von Mises stress (kPa).

From de Putter et al. (2005)

ba c

Fig. 9.13. Stereo viewing and manipulation with haptic feed-

back

9.15 Outlook

We have actually just started with exploiting the

huge power these super assistants can add, in any

of the fi elds discussed above – hardware, software

and integration. Image processing plays an essential

role, be it for visualization, segmentation, computer-

aided detection, navigation, registration, or quanti-

tative analysis. There will be an ever greater need for

clever and robust algorithms: it is the conviction of

the author that the study of human brain mechanism

for the inspiration for such algorithms has a bright

future to come (ter Haar Romeny 2004). The radi-

ologists will benefi t from these supper-assistants,

and fi nally: the patient has the best benefi t of all.



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