This document discusses image fusion and pseudo-coloring algorithms for better visual interpretation of multiple MRI scan images of the same brain section. It proposes fusing MRI scans from different sequences into a single image and assigning each sequence a color channel. This allows tissues to be distinguished that have different intensities across sequences. The techniques can improve reliability by combining information and capabilities by adding complementary data. It reviews existing fusion methods and discusses various MRI sequences and the clinical information they provide. Fusing and pseudo-coloring sequences may help diagnosis by clearly showing differences in tissue types and lesion boundaries.

1. IMAGE FUSION AND PSEUDO scale, multi-spectral or multi-temporal
COLORING IN MRI ANALYSIS remote sensing data and generating a new
data with higher information content‟. The
Project Abstract: Providing a simple, single main objectives of image fusion are
modality Image fusion and pseudo-coloring improved image reliability (by redundant
algorithm for better visual interpretation of information) and also improved image
multiple MRI scan images of the same capability (by complementary information).
section of the brain. The project is also Ideally, the method used to merge data sets
compared with the ISODATA model for with high spatial and high spectral resolution
better visual perception of multiple MRI should not distort the spectral characteristics
scan images of the same section. The of the high-spectral resolution data.
constraints of both methods are also
discussed. The paper also distinguishes II. CATEGORIZATION OF IMAGE
between single modality and multiple FUSION ALGORITHMS
modality based image fusion in MRI while
citing examples of each. The algorithms available for image fusion,
operate on a pixel-level, feature-level and
decision-level. We concentrate on the pixel-
based fusion which is performed at the level
I.INTRODUCTION of spectral radiance values and offers
The purpose of an image fusion process is to minimum of original spectral information.
combine a number of multimodal or This technique requires the input images to
multispectral images into a final entity that be registered with high accuracy of less than
comprises the maximum possible half a pixel, since incorrect registration can
information, which is present in the source cause artificial colors in features of data,
images. The source images often exhibit a thereby leading to falsifying of
high degree of correlation since the same interpretation.
area is covered in different regions of the Image fusion techniques can also be
electromagnetic spectrum or with categorized into three types, color-related,
complementary imaging technologies. Thus, numerical/ statistical-related and combined
the same information can be found in more approaches. All color-related techniques
than one of the source images and is employ slicing of original data into their
described as overlapping information. respective layers, which can be basic RGB,
The additional information of the human perceived IHS, HSV or more
panchromatic band in combination with the scientific luminance–chrominance. This is
multi-spectral bands, allows the retrieval of followed by substitution by a high resolution
maximum image information from the given image in place of one of these channels and
image data set. According to Pohl, Van a back-transformation of this combination
Genderen and Wald, „Image fusion is a into the original RGB domain.
process of performing the alliance of multi-

2. III. REVIEW OF IMAGE FUSION cluster in a feature space the axes of which
TECHNIQUES CURRENTLY IN USE IN represent the signal intensity of that tissue
MR IMAGING on MR images of that type. These clusters
are then represented by different shades of
There are several kinds of Image fusion gray and pseudo-colored using various
techniques in MRI interpretation at the color-maps until a suitable colormap is
moment. One such technique which applies found for the fused image.
multimodal image fusion is the image fusion
of an MRI scan and a CT scan for treatment
planning in Tumor treatment. Treatment IV. CHANNEL BASED IMAGE FUSION
planning based on fused CT and MRI data AND PSEUDOCOLORING
enables better definition of target volume An MRI is defined by its tissue selectivity
and risk structures as compared to treatment for contrast, and physicians generally derive
planning based on CT alone. Here, the diagnostic information from an MRI
image fusion technique only fuses a single because a particular tissue is displayed with
MRI scan based image with a CT scan a different contrast. However, each of these
image and is useful for registration of the MRI scans having different diagnostically
parts(to view the critical organs while useful data can be differently identified
viewing the bone-related information). The using machine parameters such as
term „modalities‟ here refers to either of Repetition Time, Echo Time and Inversion
„CT‟, „MRI‟, „PET‟ and „SPECT‟. The Time. Most of these parameters are
Image fusion techniques presently in use available in the DICOM header file which is
combine images of different modalities to filled in by the machine. There are various
form a fused image. This is often mentioned commonly used MRI scans, namely, T1
in publications as the 3TP method. weighted image scan, T2 weighted image
In terms of single modality based image scan, T1 FLAIR weighted image scan, Post
fusion, a particular technique has been Contrast T1 weighted image scan.
identified as well as published. In this We can combine these different scans for
publication, a multi-parametric MR image better visual perception of data by Image
set was analyzed with the iterative self- fusion with channel based coloring of
organizing data (ISODATA) technique and individual MRI scans. For example, for
it consisted of T1-weighted images, fat- three MRI scans T2 weighted image, T1
suppressed T2-weighted images, and three- FLAIR weighted image and Post Contrast
dimensional fat-suppressed T1- weighted T1 weighted scan we get the fused and
images which were acquired before and channel colored image as:
during contrast material enhancement (see
MR Imaging).These imaging sequences
constitute the conventional breast MR =
imaging examination, and they were
selected for ISODATA analysis because These parameters were obtained by
each sequence provides different contrast to checking each of the physician identified
disclose different tissue types.
images and seeking the parameters from the
If we assume that each tissue type has DICOM header file.
characteristic signal intensity on each MR
image type, then each tissue type will form a

3. Parameter T1 T2 T2 PCT1 contrast between a fat tissue and a water
FLAIR FLAIR tissue while a FLAIR sequence image forms
Echo >60 60 <30 <30 a set of elements while nullifying the fluid
Time data in the image. This can be thought of
Inversion More Less 500- Less
two sets of elements with a common factor.
Time than than 1000 than
1000 1000 1000 Identifying both these sets and their
Repetition >3000 > 1500- 1500- common elements must be done for
Time 3000 3000 3000 diagnosis of the problem. This can be further
Contrast - - - „IV‟ simplified by fusing the images and giving
Info each of these series a particular color
channel. The physician can then diagnose
the problem by identifying regions of
Are the parameters for a „GE‟ MRI scan
problem occurrence in each of the
machine of 1.5 T field strength.
underlying colors. This method, helps
V.MRI SEQUENCES AND THEIR because, it provides greater amount of
INFORMATION information in the image as well as better
visual interpretation of the image.
Each of these series provides a different
form of information to the physician. For T2 weighted image (also referred to
example, Fluid attenuated inversion as T2WI) is one of the basic pulse
recovery (FLAIR) is a pulse sequence sequences in MRI and demonstrates the
differences in the T2 relaxation time of
an inversion recovery technique that nulls
tissues. The T2WI relies upon the transverse
fluids. It can be used in brain imaging to relaxation of the net magnetization vector
suppress cerebrospinal fluid (CSF) effects (NMV). T2 weighting tend to have
on the image, so as to bring out the peri- long TE and TR times. In a T2 weighted
ventricular hyper-intense lesions, such image, the fat portion of a tissue appears
as multiple sclerosis (MS) plaques. Its intermediate bright whereas the water
usefulness is different from a T1 weighted portion appears very bright.
image. T1 weighted image (also referred to So if the doctor wants to identify between
as T1WI) is one of the basic pulse white matter and gray matter in the brain.
sequences in MRI and demonstrates the He cannot do that purely on the basis of a T1
differences in the T1 relaxation time of weighted image. He needs to look into both
tissues. T1WI relies upon the longitudinal the T1 weighted image and the T2 weighted
relaxation of the Net Magnetization Vector. image. The white matter is wrapped in a
Fat has a large longitudinal and transverse fatty layer called myelin, which insulates the
magnetization vector and hence appears axons and allows them to conduct signals
bright on a T1 weighted image. On the other quickly, much like rubber insulation does
hand, water appears to have less longitudinal for electrical wires. The type of fat in myelin
magnetization prior to the RF pulse. Thus, makes it look white, so myelin-dense white
water has low signals and appears dark. matter takes on a white hue as well. Because
Therefore the T1 weighted image shows a gray cells are not surrounded by white

4. myelin, they take on the natural grayish automated tumor identification and
color of the neurons and glial cells. Now classification. This approach may enable the
suppose that the T1 weighted MRI was identification of specific tissue signatures
fused with the T2 weighted MRI. We could characterisic of benign versus malignant
from this fused image directly distinguish tumors.
between gray matter and white matter which
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Some sample images are shown below with
their processed equivalent images.
CONCLUSION
The proposed algorithm will make it easier
for doctors to make informed diagnosis
based on MRI scan information for

5. (a) Post contrast T1 weighted image
(b) T1 FLAIR weighted image
(c) T2 FLAIR weighted image
(d) Fused and Pseudo-colored image