1. INTERNATIONAL JOURNAL OF ELECTRONICS AND
International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN
0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 2, March – April (2013), © IAEME
COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET)
ISSN 0976 – 6464(Print)
ISSN 0976 – 6472(Online)
Volume 4, Issue 2, March – April, 2013, pp. 250-256
IJECET
© IAEME: www.iaeme.com/ijecet.asp
Journal Impact Factor (2013): 5.8896 (Calculated by GISI) ©IAEME
www.jifactor.com
HYBRID METHOD TO COMPRESS SLICES OF 3D MEDICAL
IMAGES
Mayuri Y. Thorat1 and Vinayak K. Bairagi2
Electronics and Telecommunication Dept, Sinhgad Academy of Engineering,
Kondhwa (Bk), Pune-411048, Maharashtra, India
ABSTRACT
Now days 3D medical images like MRI, CT are integral part of standard health care.
These images are rich in volume and provide important diagnostic information, so there
should be some proper method to compress these images. The method proposed in this paper
is symmetry based technique for lossless compression of 3D medical image data. The
proposed method uses anatomic symmetries present in structures of medical images to reduce
energy of sub-bands. It uses Run Length Encoder and Huffman coder, which encodes the
residual data generated after prediction to provide resolution and quality scalability. The
technique can be compared with other compression techniques like RLE and Huffman. It
gives an average improvement in compression ratios.
Keywords: symmetry; volumetric images; lossless compression; RLE; Huffman.
1. INTRODUCTION
Telemedicine is the use of electronic information to communicate technologies to
provide and support healthcare when distance separates the participants. It needs the fast and
error free communication of medical images to their destination to perform e-consultancy
between various specialists to agree upon the correct diagnosis of patient [1]. As
Telemedicine deals with diagnosis of medical data there is no scope of information loss and
delay in transmission. If we are dealing with medical images they require large storage space,
and large transmission time [2].
In recent years 3D medical images like Magnetic Resonance Imaging (MRI),
Computed Tomography (CT) are considered as important part of standard health care. As the
amount of 3D medical images generated increases, the storage, management, and access to
these large repositories is becoming increasingly complex. Because this data provides
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2. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN
0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 2, March – April (2013), © IAEME
important diagnostic information, care must be taken in compressing it. Compression methods
are classified into lossless and lossy methods. In the medical image compression lossy
schemes are generally not used to avoid possible loss of useful clinical information which
may influence diagnosis [3]. As 3D medical images are large in volume and consist of
valuable data, lossless compression is usually the standard in medical imaging to avoid any
negative effects on image quality and diagnostic capabilities. The most desirable properties of
any compression method for 3D medical images include: high lossless compression ratios,
resolution scalability, and quality scalability. All of we know that human body has vertical
symmetry, which means one half part of body is approximate replica of other half part. In this
paper we are going to make use of this symmetry of human anatomy to achieve maximum
compression of medical images without any losses. There are some examples of symmetric
human body organs as axial view of brain, pupil, labia, cervical, lumber, chest, thorax, larynx,
lungs, etc.
In this paper, we propose a scalable lossless compression method for 3D medical
images that attains the three desired properties listed above and uses the symmetrical
characteristics of the data to achieve a higher lossless compression ratio. The method is well
described in further discussion.
2. BLOCK DIAGRAM
Original Check whether Image Symmetry Left Part –
Medical Image Shifted or Rotated Detection Right Part
Compressed Encoding of
Data Stream Residual Data
Figure 1: Block diagram of proposed method
The input images are 3D medical images which are output of MRI of different human
body organs. These images are having axial symmetry. The very first step is to check
whether the input image is appropriately positioned. That means we have to check that image
is neither rotated nor shifted. If this is so then first step is to convert it in proper form. Next
block performs symmetry detection. After finding axis of symmetry the image is divided in
to two parts as left part and right part. These two parts are separated and subtracted from each
other to get residual data. This residual data is encoded using RLE coding and Huffman
coding to get compressed bit stream.
2.2.DETECTING AXIS OF SYMMETRY
Detecting axis of symmetry is an important issue. Several techniques have been
proposed for detecting symmetry. One of simple and efficient of them is by finding the
centroid of the image. We have first found out the centroid and the edges of the images using
different MATLAB functions. After finding the axis of symmetry of the image, we assume
that the image is almost symmetrical. The assumption is based on the fact that we are mainly
using human medical images and that the human body is said to be largely symmetrical.
2.3. RESIDUAL DATA
Due to the inherent symmetry of the human anatomy, cross-sections of the ROIs
depicted in slices of 3D medical images are typically symmetrical [5]. There are two types of
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3. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN
0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 2, March – April (2013), © IAEME
symmetries, global symmetry and local symmetry. Global symmetry refers to the symmetry
of the whole sub-band that is nothing but a main axis of symmetry, while local symmetry refers
to the symmetry of a small region within the sub-band as defined by a local axis of symmetry [6].
Here in this method we have used only the concept of Global symmetry to avoid complexness of
local symmetry.
After getting centroid of the image next step is to plot axis of symmetry. Along the axis
of symmetry the image is divided into two parts. By doing this we can partition image in to two
areas of equal size as left part (LHi-L) and right part (LHi-R) [1]. We calculated the difference
between the left part and right part that generates the residual data with less energy than the
original data.
So we can consider following example of axial view of brain [1]. The figure 4 shown
below gives high pass sub-band (LH) of an MRI slice. It is easy to find out main vertical axis of
symmetry which is centred in the sub-band. So we can make partition of sub-band into areas
along with axis of symmetry.
Figure 4: Horizontal high pass sub-band of slice of an MRI volume of the axial view of
human head
Let’s denote area to the left of the axis as LHi-L and the area to the right as LHi-R. If LHi-L is to
be flipped along the axis of symmetry, it would be expected to provide a good approximation to
LHi-R and can therefore be used to predict LHi-R. So this data i.e. residual data obtained from
right part and flipped left part and complete right part is used as input to the encoder.
2.4. ENCODING RESIDUAL DATA
Run-length encoding (RLE) is a very simple form of data compression in which runs of
data are stored as a single data value and count, rather than as the original run [12]. RLE works
by reducing the physical size of a repeating string of characters. The main advantage of RLE is
that, it performs lossless compression of data. The other encoder we are using is Huffman
encoder. Huffman codes are variable-length codes and are optimum for a source with a given
probability model. In Huffman coding, more probable symbols are assigned shorter codewords
and less probable symbols are assigned longer codewords to find the code.
So at the output of this block we are getting data which is compressed bit stream. Now this data
can be easily stored and transmitted.
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4. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN
0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 2, March – April (2013), © IAEME
2.5.RECONSTRUCTION OF ORIGINAL IMAGE.
The output of the encoder is compressed bit stream which is to be reconstructed at the
decoder. At the receiver side we have to reconstruct image by following inverse of the
algorithm which is used for encoding. So we have to first decode the compressed bit stream
by run length decoder or Huffman decoder whichever is used to encode the data. This data is
then converted to image. The last step is to add the residual part in that image which will give
the image same as the input image. Hence the technique is completely lossless.
3. RESULTS
Part I:
The results shown in table1 are achieved by performing simple subtraction of 3D medical
images.
Table 1: Compression achieved by performing simple multiplication
Original Image Subtracted Image Size of Size of
Name Entropy Size (kb) Difference Entropy Size (kb) image after decoded
RLE image
A83GL6 5.2828 530 1 5.1701 178 154 178
G0 2D 178
A83GL6 5.4097 652 1-2 1.9645 94 90 218
G2 2D 218
A83GL6 5.3654 652 2-3 1.7083 82 65 219
G4 2D 219
A83GL6 5.4397 647 3-4 2.7519 138 129 217
G6 2D 217
A83GL6 5.3867 620 4-5 3.1915 157 149 208
G8 2D 208
First of all we convert 3D image into 2D image. The first image is as the reference for
reconstruction so it is stored as it is. Second image is now subtracted from first image and
only residual part is stored as second image instead of complete second image. Then third
image is subtracted from second image and only residual part is stored as third image just
like did for second image. And hence so on. So by doing this we can achieve good
compression. But this method results in some minor losses which we cannot afford while
working with medical images. The same idea we used in our algorithm by making use of
anatomic symmetry.
Part II:
The results shown in the table 2 gives comparison between the RLE, Huffman and
proposed method. Eight data sets of MRI images of different body organs are taken. The
images are compressed first by the proposed method of symmetry and then compression ratio
is calculated. Then images are compressed by only RLE and also by Huffman, again
compression ratio is calculated. The table shows that our proposed method gives good results
over RLE and Huffman both.
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5. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN
0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 2, March – April (2013), © IAEME
Table 2: Comparison of compression ratios of MRI Images from database
Original Compression Ratio
Image Only RLE RLE + Symmetry Only Huffman Huffman + Symmetry
(a) Brain 3.95:1 4.5:1 2.87:1 3.37:1
(b) Brain 4.47:1 5.43:1 2.56:1 3.54:1
(c) L Spine General 2.96:1 3.63:1 2.07:1 2.3:1
(d) L Spine 5.52:1 6.37:1 3.68:1 4.47:1
(e) C Spine General 1.52:1 1.84:1 1.18:1 1.32:1
(f) T Spine General 3.34:1 4.40:1 2.62:1 3.01:1
(g)Pelvis & Hip 2.20:1 3.11:1 1.92:1 2.23:1
General
(h) Abdomen 3.36:1 4.56:1 2.55:1 3.35:1
The input images shown in figure 5 to which the above coding algorithm is applied for to
generate compressed bit stream. Then decompression algorithm is applied to get the original
image back from the compressed data, which is shown in the figure 6. The output image is the
decompressed image i.e. from the figure 6 it is clear that the output image of the algorithm is
exactly same as the input image. So proposed method is exactly lossless.
Image 1 Image 2
Figure 5: Original Medical Images
Image 1 Image 2
Figure 6: Reconstructed Images obtained after applying proposed algorithm
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6. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN
0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 2, March – April (2013), © IAEME
4. FUTURE WORK
In this paper we have got comparative results for RLE, Huffman and proposed
method. Our next approach is to use several different encoding techniques to compress and
reconstruct medical images without any loss. The comparative study will help to choose better
method. We will try to improve compression ratio more.
5. CONCLUSION
In this paper, an image compression algorithm that utilizes anatomic symmetries
present in medical images is proposed. The algorithm first divides image slice into two parts
along the axis of symmetry. The residual part is found out by comparing two parts, which is to
be encoded using Huffman or RLE. This gives compressed bit stream at the output of encoder
which is decoded at receiver to reconstruct the image.
This paper focused on the evaluation of several commonly used algorithms for lossless
compression. The proposed method is a 3D scalable lossless compression of medical image
data. So our aim is to increase compression ratio with reducing complexity while achieving
compression.
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0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 2, March – April (2013), © IAEME
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