52

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.

54

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

55

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

56

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

57

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,

58

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

59

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.

60

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

61

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

62

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

63

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

64

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

65

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

66

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

67

(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

68

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)

69

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.

70

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.

71

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

72

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.

73

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

74

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

75

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

76

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

77

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

78

MONO-MODAL REGISTRATION(MRI_MRI)

(a) Reference Image (b) Sensed Image

(c) Aligned Image (d) Registered Image

Fig.3.7. MRI-MRI Registration

79

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

80

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

81

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

82

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

83

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.

84

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.