16-02-2013, 09:40 AM
CURVELET FUSION OF MR AND CT IMAGES
[attachment=51120]
Abstract
This paper presents a curvelet based approach for the
fusion of magnetic resonance (MR) and computed tomography (CT)
images. The objective of the fusion of an MR image and a CT image
of the same organ is to obtain a single image containing as much
information as possible about that organ for diagnosis. Some attempts
have been proposed for the fusion of MR and CT images using the
wavelet transform. Since medical images have several objects and
curved shapes, it is expected that the curvelet transform would be
better in their fusion. The simulation results show the superiority of
the curvelet transform to the wavelet transform in the fusion of MR
and CT images from both the visual quality and the peak signal to
noise ratio (PSNR) points of view.
INTRODUCTION
Image fusion is the process of merging two images of the same scene
to form a single image with as much information as possible. Image
fusion is important in many different image processing fields such as
satellite imaging, remote sensing and medical imaging. The study in
the field of image fusion has evolved to serve the advance in satellite
imaging and then, it has been extended to the field of medical imaging.
Several fusion algorithms have been proposed extending from the
simple averaging to the curvelet transform. Algorithms such as the
intensity, hue and saturation (IHS) algorithm and the wavelet fusion
algorithm have proved to be successful in satellite image fusion. The
IHS algorithm belongs to the family of color image fusion algorithms
[1–3]. The wavelet fusion algorithm has also succeeded in both satellite
and medical image fusion applications [3–5].
WAVELET FUSION
The most common form of transform type image fusion algorithms is
the wavelet fusion algorithm due to its simplicity and its ability to
preserve the time and frequency details of the images to be fused [11].
THE CURVELET TRANSFORM
The curvelet transform has evolved as a tool for the representation of
curved shapes in graphical applications. Then, it was extended to the
fields of edge detection and image denoising [9, 10]. Recently, some
authors have proposed the application of the curvelet transform in
image fusion [1, 2].
The algorithm of the curvelet transform of an image P can be
summarized in the following steps [8–10]:
A) The image P is split up into three subbands Δ1,Δ2 and P3 using
the additive wavelet transform.
Tiling
Tiling is the process by which the image is divided into overlapping
tiles. These tiles are small in dimensions to transform curved lines
into small straight lines in the subbands Δ1 and Δ2 [11–13]. The
tiling improves the ability of the curvelet transform to handle curved
edges.
Ridgelet Transform
The ridgelet transform belongs to the family of discrete transforms
employing basis functions. To facilitate its mathematical representation,
it can be viewed as a wavelet analysis in the Radon domain. The
Radon transform itself is a tool of shape detection. So, the ridgelet
transform is primarily a tool of ridge detection or shape detection of
the objects in an image.
THE PROPOSED FUSION ALGORITHM
It is known that different imaging modalities are employed to depict
different anatomical morphologies. CT images are mainly employed
to visualize dense structures such as bones. So, they give the general
shapes of objects and few details. On the other hand, MR images are
used to depict the morphology of soft tissues. So, they are rich in
details [12–16]. Since these two modalities are of a complementary
nature, our objective is to merge both images to obtain as much
information as possible.
EXPERIMENTAL RESULTS
The proposed algorithm for the fusion of MR and CT images is
tested and compared to the traditional wavelet fusion algorithm. Two
experiments are conducted for this purpose. For the evaluation of the
performance of the fusion algorithms, the visual quality of the obtained
fusion result as well as the quantitative analysis are used.
CONCLUSION
The paper has presented a new trend in the fusion of MR and CT
images which is based on the curvelet transform. A comparison study
has been made between the traditional wavelet fusion algorithm and
the proposed curvelet fusion algorithm. The experimental study shows
that the application of the curvelet transform in the fusion of MR and
CT images is superior to the application of the traditional wavelet
transform. The obtained curvelet fusion results have higher PSNR
values than the wavelet fusion results. Also, curved visual details are
better in the curvelet fusion results than in the wavelet fusion results.
[attachment=51120]
Abstract
This paper presents a curvelet based approach for the
fusion of magnetic resonance (MR) and computed tomography (CT)
images. The objective of the fusion of an MR image and a CT image
of the same organ is to obtain a single image containing as much
information as possible about that organ for diagnosis. Some attempts
have been proposed for the fusion of MR and CT images using the
wavelet transform. Since medical images have several objects and
curved shapes, it is expected that the curvelet transform would be
better in their fusion. The simulation results show the superiority of
the curvelet transform to the wavelet transform in the fusion of MR
and CT images from both the visual quality and the peak signal to
noise ratio (PSNR) points of view.
INTRODUCTION
Image fusion is the process of merging two images of the same scene
to form a single image with as much information as possible. Image
fusion is important in many different image processing fields such as
satellite imaging, remote sensing and medical imaging. The study in
the field of image fusion has evolved to serve the advance in satellite
imaging and then, it has been extended to the field of medical imaging.
Several fusion algorithms have been proposed extending from the
simple averaging to the curvelet transform. Algorithms such as the
intensity, hue and saturation (IHS) algorithm and the wavelet fusion
algorithm have proved to be successful in satellite image fusion. The
IHS algorithm belongs to the family of color image fusion algorithms
[1–3]. The wavelet fusion algorithm has also succeeded in both satellite
and medical image fusion applications [3–5].
WAVELET FUSION
The most common form of transform type image fusion algorithms is
the wavelet fusion algorithm due to its simplicity and its ability to
preserve the time and frequency details of the images to be fused [11].
THE CURVELET TRANSFORM
The curvelet transform has evolved as a tool for the representation of
curved shapes in graphical applications. Then, it was extended to the
fields of edge detection and image denoising [9, 10]. Recently, some
authors have proposed the application of the curvelet transform in
image fusion [1, 2].
The algorithm of the curvelet transform of an image P can be
summarized in the following steps [8–10]:
A) The image P is split up into three subbands Δ1,Δ2 and P3 using
the additive wavelet transform.
Tiling
Tiling is the process by which the image is divided into overlapping
tiles. These tiles are small in dimensions to transform curved lines
into small straight lines in the subbands Δ1 and Δ2 [11–13]. The
tiling improves the ability of the curvelet transform to handle curved
edges.
Ridgelet Transform
The ridgelet transform belongs to the family of discrete transforms
employing basis functions. To facilitate its mathematical representation,
it can be viewed as a wavelet analysis in the Radon domain. The
Radon transform itself is a tool of shape detection. So, the ridgelet
transform is primarily a tool of ridge detection or shape detection of
the objects in an image.
THE PROPOSED FUSION ALGORITHM
It is known that different imaging modalities are employed to depict
different anatomical morphologies. CT images are mainly employed
to visualize dense structures such as bones. So, they give the general
shapes of objects and few details. On the other hand, MR images are
used to depict the morphology of soft tissues. So, they are rich in
details [12–16]. Since these two modalities are of a complementary
nature, our objective is to merge both images to obtain as much
information as possible.
EXPERIMENTAL RESULTS
The proposed algorithm for the fusion of MR and CT images is
tested and compared to the traditional wavelet fusion algorithm. Two
experiments are conducted for this purpose. For the evaluation of the
performance of the fusion algorithms, the visual quality of the obtained
fusion result as well as the quantitative analysis are used.
CONCLUSION
The paper has presented a new trend in the fusion of MR and CT
images which is based on the curvelet transform. A comparison study
has been made between the traditional wavelet fusion algorithm and
the proposed curvelet fusion algorithm. The experimental study shows
that the application of the curvelet transform in the fusion of MR and
CT images is superior to the application of the traditional wavelet
transform. The obtained curvelet fusion results have higher PSNR
values than the wavelet fusion results. Also, curved visual details are
better in the curvelet fusion results than in the wavelet fusion results.