chapter 3 automatic affine registration of...
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CHAPTER 3
AUTOMATIC AFFINE
REGISTRATION OF BRAIN
TOMOGRAPHS
Papers Published out of this work
1. Ms.N.Usha Rani,Dr.P.V.Subbaiahand Dr.D.VenkataRao,
“Automatic Image Registration of CT-MRI Images of Brain”,1st
International Conference on Emerging Trends in Signal Processing
& VLSI design, Guru Nanak Engineering College,Hyderabad,pp-
46.
2. Ms.Usha Rani.Nelakuditi,Dr.P.V.Subbaiah, and M.Sarada,
“Optimization of brain modalities using Hough Transform”,1st
International Conference on Emerging Trends in Signal Processing
& VLSI design, Guru Nanak Engineering College,Hyderabad,pp-
26.
3. UshaRani.N, SaradaMusala, andDr.K.SoundaraRajan, “A Novel
Optimized Rigid Image Registration Of Brain using ACMI”, 2010
ICCIC-IEEE, Tamilanadu College of Engg., Coimbatore,
India,Dec. 28-30, 2010. (Available on IEEE explore).
53
CHAPTER-3
AUTOMATICAFFINE REGISTRATION OF
BRAIN TOMOGRAPHS
3.1. INTRODUCTION
Image analysis plays a key role in middle level image processing.
Image registration is one of today‟s challenging problems in image
analysis tasks. Image registration plays an important role in remote
sensing, medicine and computer vision. Typically, registration is
required in remote sensing for multispectral classification,
environmental monitoring, change detection, image mosaicking, weather
forecasting, creating super-resolution images, and for integrating
information into geographic information systems (GIS).In medicine,
computer tomography (CT) is combined with NMR data to obtain more
complete information about the patient, like monitoring tumor growth,
treatment verification, comparison of the patient‟s data with anatomical
atlases etc. Image Analysis also provides critical applications in
cartography for map updating, and in computer vision target
localization, automatic quality control etc. In recent timesespecially in
medicine,image acquisition devices have undergone rapid development
and thediversity ofobtained imagesis invoking furtherinterest and
research on automatic image registration.
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When given two images, registration involves finding a
“reasonable” transformation [1, 3, 5, and 6] such that a transformed
version of the so-called template image becomes “similar” to the so-
called reference image. Image registration is applied whenever images
resulting from different times, modalities, and/or different views are to
be compared and integrated. Image registration, mentioned above, is
widely used in remote sensing, medical imaging, computer vision etc.
Due to the diversity in images to be registered and due to various
types of degradations [13, 15] it is impossible to design a universal
registration method applicable to all registration tasks. Every method
should take into account not only the assumed type of geometric
deformation between the images but also radiometric deformations and
noise corruption, required registration accuracy and application-
dependent data characteristics.
3.2. IMAGE REGISTRATION
In general, many differences will exist between images due to
different imaging conditions [2, 3]. Image registration is considered as
the process of overlaying two or more images of the same scene taken at
different times, from different viewpoints, and/or by different sensors.
This process geometrically aligns the two images the reference and the
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sensed images. Hence it includes geometrical alignment and fusion for
integration of information of two images. The registration process is
explained in Fig.3.1.
In this figure the two images the reference and the sensed images
displaying different views of the same scene are considered and
transformation is applied on the sensed image so that they are aligned
geometrically and finally information from both the images is combined
by fusion. The fused image obtained contains the complementary
information of two images.
Fig.3.1. Registration Process
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3.3. NEED OF MEDICAL IMAGE REGISTRATION
Image registration plays an important key role in medical domain.
In this thesis registration of brain scans is clearly discussed.
Registration is central to many challenges in medical imaging today. It
has a vast range of applications.
Within the current clinical setting, medical imaging is a vital
component of a large number of applications. Such applications occur
throughout the clinical track of events not only within clinical
diagnostic settings, but also prominently in the area of planning and
evaluation of surgical and radio-therapeutical procedures. At present
patient registration for computer assisted surgery is a challenging
problem requiring short registration times and high accuracies.
Registration algorithms typically involve trade-offs between speed of
execution, accuracy and ease of application. Image-based registration
algorithms, which gather data from large portions of the image in order
to increase accuracy are computationally intensive, and typically suffer
performance degradation when the input images contain clutter.
Diagnosis and treatment of brain diseases can be done using two
kinds of medical images [2]; functional images like SPECT and PET
provide physiological information i.e. malignancy and growth.
Anatomical images, X-ray, ultrasound, CT and MRI provide anatomic
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structure i.e. location of tumors. In the process of registration doctors
integrate the data from both kinds of scans to diagnose and treat the
problem with more precision. Based on the registered images doctors
are able to make a more exact diagnosis than in cases where traditional
imaging modalities are adopted. The functional modalities form the
basis of the rapidly advancing field of molecular imaging defined as the
direct and indirect non-invasive monitoring and recording of the spatial
and temporal distribution of the molecular, genetic, cellular processes
for biochemical, biological, diagnostic or therapeutic applications.
Information obtained from structural and functional images is often
complementary. Medical image registration is required to monitor the
changes in anatomical structure over time, combining the information of
the same patient (intra-subject) or different patients (inter-subject) of
same (mono) or different (multi) modalities. The following are the
examples where medical image registration is widely used.
3.3.1. Radiation Therapy
The radiation therapy utilizes the ionizing radiation (X-rays,
Gamma rays) from a linear accelerator to kill or stop the growth of
tumor. The goal of radiation treatment is to deliver energy dose of
radiation to abnormal tissue to stop cancer cells from dividing. At the
same time with precise therapy simulation and planning, damage due to
therapy will be minimized for the surrounding normal tissue. Therefore,
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before therapy treatment, both CT and MRI scans are employed on the
patient. MR imaging is suitable for the localization of tumor [12, 86]. CT
imaging is used for calculation of radiation dose and determination of
optimal path.
3.3.2. Cancer Detection
Image registration is important for the early detection of cancers
[5]. Radiologists need to identify the exact anatomical location of cancer
and monitor its effects on motion. It is still difficult to localize and
determine the tumor with the anatomical information from CT and MR
scans because of the low contrast between the tumor and the
surrounding tissues. SPECT and PET imaging makes it possible to
acquire high contrast images. However, they do not provide enough
anatomic detail to determine the position of a tumor or other lesion. It
would be more useful to align the structural anatomic image from
CT/MR onto the functional image from SPECT/PET.
3.3.3. Template Atlas Applications
As the standard information database, an atlas is constructed
from imaging studies of a large number of subjects. Therefore, an atlas
includes more number of details about the anatomical structure ofthe
subject, which is indispensable for understanding the structure and
function areas of subject. In the functional MRI analysis, matching MR
scans with anatomic atlases provide important means to evaluate and
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identify the features (size, shape, location) of anatomical areas. The
registration process is accomplished through several operations: (1)
manually by manipulating the images into the same location. (2) By
identifying the anatomical landmarks and transforming image to the
atlas space by minimizing the distance among various landmarks. (3) by
deforming the atlas into the shape of any subject. Through these
operations, atlas and the subject image will be overlapped with the
corresponding areas getting aligned, which in turn helpthe researcher to
compare the structures of multiple subjects to the atlas (reference)
quantitatively.
3.3.4. Functional MRI Analysis
In functional MRI experiments, time-sequential 3D images are
acquired for statistical analysis. When the images are analyzed for
drawing inferences on activation response for statistical confidence
level, it is based on assumption that a given pixel of functional area is
located at the same location for all the subjects. If the subject moves
around during the scans, it will throw up false BOLD activation areas
that will get again detected in the time-series analysis. Therefore, it is
critical to register the time series of images from the spatial and
temporal space before the statistical data analysis is carried out.
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3.3.5. Image-guided Surgery
Image-guided surgery is a part of computer-assisted surgery,
which constitutes pre-operative planning and intra-operative navigation.
Pre-operative planning includes obtaining information from CT and MR
scans to localize the lesion or tumor, generating three-dimensional
model and determining the optimal path of surgery. During the intra-
operative navigation each movement of instruments is tracked from the
video camera and superimposed on the image, which assists a surgeon
identify intra-operative movement of the instrument relative to pre-
operative 3D model of patient. This powerful computer technology
provides the ability of 3D rendering and analysis like the real-time
surgery. During the surgery image registration is employed in the
navigation system for real-time tracking of the changes of instruments
in relation to 3D model built from the preoperative CT/MR scans.
3.4. BASIC STEPS IN IMAGE REGISTRATION
Though registration is performed on different criteria majority of the
registration methods consist of the following four steps as per the
Zivota[6]. They are
Feature Detection
Feature Matching
Transform Model Estimation
Image Re-Sampling and Transformation
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3.4.1. Feature Detection
Salient and distinct objects, like closed-boundary regions, edges,
contours, line intersections, corners, etc. are manually or, automatically
detected. For further processing, these features can be represented by
their point representatives (centers of gravity, line endings, distinctive
points), which are called control points (CPs) in the literature. The
detected feature sets in the reference and sensed images must have
enough common elements, even in situations when the images do not
cover exactly the same scene or when there are object occlusions or
other unexpected changes‟. They should be distinctive objects, which
are frequently spread over the images and which are easily detectable.
Based on the features, registration methods can be classified as area
based[1,3,5,6] and feature based methods[3,6,8,9,10,11]. Medical
images are more acquainted with the noise hence area based methods
are more appropriate.
3.4.2. Feature Matching.
The objective of this step is to establish the correspondence [5]
between the detected features in the sensed image and the reference
image. Various feature descriptors and similarity measures along with
the spatial relationships among the features are used for this purpose.
Physically corresponding features can be dissimilar due to different
imaging conditions and/or due to different spectral sensitivity of the
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sensors. The choice of the feature description [3] and similarity measure
[2, 14, 18, 21, and 84] has to consider these factors. The type of the
mapping functions should be chosen according to the priori information
about the acquisition process and expected image degradations. The
popular similarity measures are Sum of Squared Difference (SSD)[17],
Cross Correlation coefficient (CC)[87] and Mutual Information (MI)[91].
In this work suitability of Gradient code mutual information (GCMI) and
ACMI [25]are also studied on medical images. Due to tradeoff between
complexity and accuracy, finally MI is used as the similarity metric for
the entire work. For maximum similarity more MI is required. Mutual
information between images „X‟ and „Y‟ is computed using the following
equation.
MI(X, Y) = H(X) + H(Y) − H(X, Y).
WhereH(X) and H(Y) represents the entropies of X and Y respectively.
H(X, Y) represents joint entropy of X and Y.
MI(X, Y) represents the mutual information.
3.4.3. Transform Model Estimation
Transformation model estimation specifies how the target image
can be transformed to match the source. The objective is to determine
the type and parameters of the mapping functions align the sensed
image with the reference image. These parameters are computed by
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means of established feature correspondence. In this chapter, affine
transformation consists of 12 degrees of freedom (DOF) is used for the
alignment. The transformation is defined as a mapping of location of
points in one image I1 to a new location in another image I2.
i.e. I2 (x1, x)= I1 (T (x), x).
x1=T(x)
where T is the transformation
When two images have translational, rotational, and scaling
differences, the relation between them can be written by the
transformation of the Cartesian coordinate system.
X = S[xcos(θ) + y sin(θ)] + tx,
Y = S [−x sin (θ) + y cos(θ)] + ty.
where S represents scaling,
tx, tyrepresents displacements in x and y directions and
θ represents the rotation angle.
In this case two images are aligned [13, 18, 20, 21] geometrically
by applying rotation, translation, scaling and shear in x- and y-
directions. The spatial transformation modifies the spatial relationship
between the pixels in an image, mapping pixel locations in an input
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image to new locations in an output image. Some spatial
transformations [88] are rigid (Box), affine, projective and composite.
Affine transformation [3, 7, and 13] includes translation, rotation,
scaling and shear.
In projective transformation straight lines remain straight. But parallel
lines converge towards vanishing points. Box transformation is the
special case of affine transformation where each dimension is shifted
and scaled independently. Two or more transformations can be
combined and applied as the composite transformation. The effect of
above mentioned transforms on CT brain scan is shown in Fig.3.2.
3.2. Image Transformations
Original Rigid
Affine
Projective
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Further maximization of the matching criterion is performed through
the optimization process. The components involved in the registration
process are shown in Fig.3.3. below.
Multi-modal or temporal images are usedto perform registration.
In this chapter work is presented on medical scans with the affine
transformation is applied to the search space. Translations and
rotations can be considered in aligning images. In first step both the
images must be cropped to the same size.
Fig.3.3. Components of a Registration Process
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Prior to each step of angle in the range (0, 2*pi) the MI between 2
images is computed. In the same manner, translation of edges of the
left image on to the edges of the right image, a measure of similarity in
MI is calculated Moving the source image along the length of reference
image along x and y and also rotating the entire feature space in steps
of 2 pixels in translation and 1/100 degrees in rotation respectively.
Interpolation is performed using the nearest neighbor, bilinear and bi-
cubic methods. Optimization is done using a simplex method. Mutual
Information (MI) is computed in each case in all the directions. It is
observed that MI is maximum when both the images were aligned
properly and information is minimum. At the end information is fused
into single image by using Wavelet Transformation Method. The images
registered by using translation and rotation optimize the similarity
criterion.
3.4.4. OPTIMIZATION
Finding the minimum of dissimilarity measure or the maximum of
similarity measure is a multidimensional optimizations [1, 5, and 6]
problem. The optimization algorithms are broadly classified as (1)
Search methods [26, 27] (2) Evolution methods [90]. Search methods
yield global extreme solution is an exhaustive search over the entire
image. These methods help to localize the maxima or minima.
(1) Gauss-Newton optimization
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(2) Level Berg-Marquardt optimization.
(3) Powell multi-dimensional direction set methods.
(4) Gradient decent optimization method
Optimization contains dissimilarity/similarity measure terms as well
as so called regularization or penalty terms which interconnect the
transformation and data to be transformed there two terms form the
cost function (energy) associated with the registration and the aim of the
optimization is to minimize it. These methods are referred to as energy
minimizations methods.The objective of evolutionary optimization
methods is to find the best fit for the template in the scene. They are
(1)Genetic Algorithm (GA)
(2)Simulated annealing
(3) Particle Swarm Optimization (PSO)
The term evolution refers to the fact that the optimization solution
gradually evolves from the population of individuals that share
information and share group dynamics. All evolutionary optimization
methods have the following operations.
a. Evolution
b. Selection
c. Alteration
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An initial population of individuals is initialized covering the
parameter spaced and the objective function in evaluated for each
individual. From the data, a subset of individuals is selected and altered
to form new individuals. The degree to which each of the operations is
performed in GA, SA and PSO varies from algorithm to algorithm. The
evolutionary methods typically based on the process which occur in the
natural world such as genetics, the swarming behavior bees and the
annealing of metals.
3.4.5. IMAGE RE-SAMPLING AND TRANSFORMATION
The sensed image is transformed by means of the mapping
functions. Image values in non-integer coordinates are computed by the
appropriate interpolation technique [22, 23, 24, and 85]. The choice of
the appropriate type of re-sampling technique depends on the trade-off
between the demanded accuracy of the interpolation and the
computational complexity. The nearest-neighbor or bilinear
interpolation is sufficient in most cases.
Interpolation estimates gray values of one image at positions other
than grid points. It achieves the process by fitting continuous functions
through the discrete input samples. Interpolation reconstructs the
signal lost in the sampling process by smoothing the data. The image
quality highly depends on the interpolation techniques used.
Interpolation techniques are divided into two categories. (1)
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Deterministic assume a certain viability between the sample points. (2)
Statistical approximate the signal by minimizing the estimation error.
They are computationally efficient.
The most commonly used deterministic methods are recent
neighbor, linear cubic and spline, techniques polynomial and language
interpolation methods. Interpolation reduces the band width of a signal
by applying low pass filter to the discrete signal i.e., Interpolation
reconstructs the signal that lost in the sampling process by smoothing
the data samples with an interpolation function. The numerical
accuracy and computational cost of interpolation algorithm are directly
tied to the interpolation kernel.
Nearest Neighbor Interpolation
Each output interpolated pixel is assigned the value of the nearest
sample point in the input image. The interpolation kernel for the nearest
neighbor kernel is defined as
ℎ 𝑥 = 1 0 ≤ 𝑥 < 0.5
0 0.5 ≤ |𝑥|
H(w) =Sinc(w/2)
This technique achieves magnitude functions by pixel replications and
magnification by space point sampling.
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Bi-Linear Interpolation
It is the first degree method that possesses straight lines through
every two consecutive points of the input signal.
ℎ 𝑥 = 1 − |𝑥| 0 ≤ 𝑥 < 1
0 |𝑥| ≥ 1
H(w) = Sinc2(w/2)
The frequency response of the linear interpolation kernel is superior to
that of the nearest neighbor interpolation results in the image
smoothing due to the improved stop band.
BI-Cubic Interpolation
Bi-Cubic interpolation is the third degree algorithm that fairly well
approximates the theoretical sinc interpolation function. B-Spine is not
interpolator since it does not satisfying the necessary constraints. It is
as approximation function that passes near the points but not
necessarily through time. This is due to kernel is strictly positive. Hence
it is more suitable for image processing applications since gray values
are always positive.
3.5. CLASSIFICATION OF REGISTRATION METHODS
Maintz later in his survey paper [5] has given a more detailed
andaugmented version of classification based on nine basic criteria [1].
Table.3.1. illustrates the classification.
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Table.3.1.Classification of Registration Process
S.No Criteria Description
1
Dimensionality
2D or 3D
2D/2D, 2D/3D, 3D/3D are possible.
Sometimes time could be the fourth dimension.
2
Nature of Registration
basis( Based on the
features)
Extrinsic image
Intrinsic
Non-image based
3 Nature of transformation
Rigid
Affine
Projective Curved
4 Domain of
Transformation
1.Global 2.Local
Depending on whether the whole image or its part is to be
registered.
5 Interaction
Depending on the role of user
1.Automatic
2.Semiautomatic
3.Manual
6 Optimization Procedure
Parameters computed directly
Parameters searched for
The parameters of the transformation can be find out
using direct or search oriented methods.
7 Modalities Involved
Mono-modal
Multimodal
Patient to modality
8 Subject
Inter-subject
Intra-subject
Atlas
9 Object
Head
Thorax
Abdomen
Pelvis
Limbs
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3.6. IMPLEMENTATION OF AUTOMATIC REGISTRATION
The objective of this chapter is to develop an algorithm for the
automatic registration of medical MRI, PET and CT images using affine
transformation with optimal interpolation and optimization techniques.
The registration process is worked withdifferent similarity metrics like
MI, ECCI, ECCG, and ACMI. The process is also executed for different
optimization methods downhill simplex method, Quasi-Newton method,
Gauss-Newton method and the Mini-max method. The results of the
registration process are thenused in justifying the suitability of medical
tomograph registration with different similarity metrics.
In this section the implementation of fully automatic registration
of brain scans is explained with the help of flowchart. Geometric
transformations are used to correct the errors in translation, rotation
and scaling of the input image to that of reference image. The objective
of the process is geometric correction process is applied automatically
without user interaction. Flowchart represented in Fig.3.5.explains the
automatic image registration process.
The basic objective of the registration process is to bring the
respective reference and target images into spatial alignment called
registration in a common coordinate system. After registration, fusion is
required for the integrated display of the aligned images. From the
flowchart the process can be summed up into four basic steps.
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3.6.1. Acquiring Information from Two Images
It is nothing but reading both the images. The images used in this
process are of same size. If they are differ in size, reformatting of the
image set (“floating” or secondary) is to be performed to match that of
the other image set (the reference or primary image) i.e., both the
images into a common format.Usually the higher spatial resolution (CT)
image is the primary image and the functional image is the secondary
image.
3.6.2. Pre-Processing to Improve the Quality of Images
Most of the times images acquired mix up with noise and hence
image quality is insufficient. Image quality is improved with
preprocessing operations like filtering and gray level adjustment and
contrast enhancement.
3.6.3. Finding a Mapping betweenTwo Images to Determine
Transformation Functions
The transformation of the reformatted secondary image set is to be
computed to spatially align it with primary image set. In aaffine
transformation, the secondary image is translated, scaled and rotated
with respect to the primary image. A widely used automated registration
algorithm is based on the statistical concept of Mutual Information
(MI).MI measures the information about X that is shared by Y if X and Y
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are independent. If X contains no information about Y then MI is zero. If
X and Y are identical then MI is maximized. If a patient is imaged by two
different modalities like MRI and CT, then it is presumably considerable
MI between the spatial distances of the respective signals in two image
sets. Accurate spatial registration of the two such images sets then
results in the maximization of their MI and vice versa. Optimization is
used for further matching.
3.6.4. Reconstruction of Images
Fusion [2, 84] is the process for theintegrated display of registered
image.
3.7. RESULTS AND CONCLUSIONS
Thedeveloped“fullyautomatic registration of brain algorithm” is
appliedon CT, MRI and PET images. In this case, affine transformation
is applied with six degrees of freedom two translations along x and y,
rotation and scaling. At first images should be reformatted to the same
size. Then source image is moved along x and y in steps of two pixels,
and also rotating the entire feature space in a step of 1/100 degrees in
rotation respectively. Interpolation is performed using the nearest
neighbor, bilinear and bi-cubic methods. Optimization is done using a
simplex method. Mutual Information (MI) is computed in each case in
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No
yesAutomation
NO No
Yes
Fig.3.5. Automatic Registration of Brain Scans
Read Reference Image
Start
Stop
Issize
same?
Is MI Maximum
?
Resize
Apply the Transformation
Calculate Mutual Information
Taking the Transformation results and applying on the Image
Applying Optimization
Applying Fusion
Read Sensed Image
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allthe directions. It is observed that MI ismaximum when both the
images arealigned properly and information is minimum.
At the end, information is fused into a single image by using
Wavelet Transformation method. This algorithm is applied to mono-
modal CT-CT, MRI-MRI and multi-modal CT-MRI images. Results are
tabulated quantitatively in table.3.2. Registration process is also shown
qualitatively in Fig.3.6,Fig.3.7, and Fig.3.8.
From the Table.3.2, it is observed that MI is maximum in CT-MRI
case with bi-cubic interpolation method because of more complementary
information.Same fact is justified by the graph in Fig.3.9. In this
process Simplex method is used as the optimization technique. For
registered images MI is more for MRI-MRI (0.9865)and CT-CT (0.9763)
compared to CT-MRI (0.2534) due to more exact alignment from i.e.
geometric match is more.
The process is also evaluated with gradient codes. The concern
similarity parameter ECCG is used. The information covered in ECCG is
very low and hence combination of ECCG and ECCI known as ACMI is
used as similarity metric. The ECCI, ECCG and ACMI vsdisplacement in
2-D and 3-D are shown in Fig.3.10. From the Fig.3.10.,ECCI having
multiple minima whereas ECCG has single minima hence it is having
maximum similarity. But it contains less information due to gradients.
To have two merits, ACMI with maximum information and
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distinguishable maxima is used. The algorithm is applied on MRI and
PET images the results are shown in Fig.3.10. The result of different
optimization methods is shown in Fig.3.12.
MONO-MODAL REGISTRATION(CT-CT)
(a)(b)
(a) (b)
(c) (d)
(a) Reference Image (b) Sensed Image
(c) Aligned Image (d) Registered Image
Fig.3.6. CT-CT Registration
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MONO-MODAL REGISTRATION(MRI_MRI)
(a) Reference Image (b) Sensed Image
(c) Aligned Image (d) Registered Image
Fig.3.7. MRI-MRI Registration
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MULTI-MODAL REGISTRATION(MRI-CT)
(a) (b)
(c) (d)
(a) Reference Image (b) Sensed Image
(c) Aligned Image (d) Registered Image
Fig.3.8. CT-MRI Registration
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The computation time of the process is observed with different
optimization criteria to improve the similarity. The quantitative analysis
is shown in Table.3.3.and Fig.3.12. It is observed that Quasi-Newton
method is more accurate.
Table.3.2. Performance Comparison of AutomaticRegistration
Process with Various Interpolation Methods
Fig.3.9. MI with Different Interpolation Methods
00.5
11.5
22.5
33.5
CT-CT MRI-MRI CT-MRI
Fused
1 Nearest Neighbor2 Bilinear
3 BiCubic
S.No 1 2 3
Optimization Simplex Simplex Simplex
Interpolation Nearest
Neighbor Bilinear Bi-Cubic
CT-CT Registered .8984 .9102 .9763
Fused 2.5215 2.6132 2.8315
MRI-MRI Registered .8333 .9217 .9865
Fused .9742 .9846 .9958
CT-MRI Registered .2343 .2452 .2534
Fused 2.9453 3.1463 3.2652
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Fig.3.10.ECCI,ECCG, and ACMIvsDisplacement
Fig.3.11. ECCI,ECCG,and ACMI for PET and MRI Images
(a) ECCI
(b) ECCG
(c) ACMI
(a) ECCI
(b) ECCG
(c) ACMI
(a)MRI
(b)GCM
(c)ECCG
(d)PET
(e)GCM
(f)ACMI
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Table.3.3.Performance of Automatic Registration for Different
Optimization Techniques
Fig.3.12. MI for Different Optimization Methods
-5
-4
-3
-2
-1
0
1
Op
tim
izat
ion
Met
ho
d
Do
wn
hill
-Sim
ple
x
Qu
asi-
New
ton
Gau
ss-N
ewto
n
Min
imax
ECCI ECCG ACMI
Similarity measure Computational time(sec)
Optimization
Method
ECCI ECCG ACMI ECCI ECCG ACMI
Downhill-Simplex
-0.874 -0.651 -0.6509 66.21 63.6516 66.15
Quasi- Newton
-0.874 -4.725 -4.725 5.234 39.719 37.5
Gauss-
Newton
-0.874 0.4259 0.8636 6.103 6.203 6.094
Mini-max
-0.874 -0.651 -0.6509 23.984 22.578 27.797
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3.8. VALIDATION OF REGISTRAION PROCESS
Registration is one of the advanced digital image processing
technique.By using appropriate computational algorithms, spatial and
intensity mapping (transportation) between two images can be achieved
to produce a new image which has both structural and functional
information useful for the health clinicians for fast and more efficient
diagnosis. Registration process can be multimodal (different sensors),
temporal (images taken at different times of same modal), different
viewpoints (3D object) or template registration (Model based object
recognition). After the completion of registration process it has been
validated by the following parameters.
1. Robustness/Stability
2. Reliability
3. Computational Complexity
4. Accuracy
5. Clinical Use
3.8.1. Robustness/Stability
It refers to the output variations based on the small variations in the
input. If the input images are aligned in a slightly varied orientation,
then also algorithm should converge to the same result, then the
algorithm is said to be more robust. The algorithm proposed is more
robust.
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3.8.2. Reliability
It is the requirement that the algorithm should behave as
expected, given a reasonable range of possible clinical input. The
algorithm proposed is more reliable. It is applied on different
combinations of input images and verified.
3.8.3. Computational Complexity
Computational complexity is measured in terms of computational
time. In case of medical and clinical environment algorithm should use
less time. The algorithm with complex interpolation and optimization
techniques also it uses at maximum uses 60 sec of time.
3.8.4. Accuracy
It is the direct measure referring to the actual or time error
occurring at a specific image location. It applies to a specific registration
instance.Accuracy can be divided into qualitative and quantitative.
Qualitative accuracy can usually supplied using simple visualization
tools and visual spectrum. Quantitative accuracy needs a ground truth
that is invariable in clinical practice. It needs to be evaluated by
reference to another measure. In this qualitative accuracy is used to
justify the algorithm. By observing input, registered and fused images
accuracy can be justified. Further it is quantified with the help of MI.
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3.8.5 Clinical Use
The registration algorithm should be adoptable and also support
current clinical need.It shouldoutweigh available alternatives. It is
application dependent, and a matter of judgment.
3.9. CONCLUSIONS
In this chapter affine registration with various interpolation and
Optimization techniques is automated. The automation of the process
avoids the human creates the possibility that process is applied on more
number of cases with less time. Computational accuracy is also
improved. The process is more useful for the doctors in integrating the
information and diagnosing the problem.