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Diagnostic Quality Evaluation of Compressed Medical Images for
Telemedicine Applications
Seddeq E. Ghrare, M. Alauddin M. Ali, M. Ismail, K. Jumari
Universiti Kebangsaan Malaysia (UKM)
Faculty of Engineering
{ seddeq, mama, mahamod, kbj }@vlsi.eng.ukm.my
Abstract
Many techniques for achieving data compression have
been introduced. The fundamental goal of image data
compression is to reduce the bit rate for transmission or
storage while maintaining an acceptable reproduction
quality, but it is natural to raise the question of how much an
image can be compressed and still preserve sufficient
information for a given telemedicine application. Evaluation
of the quality of compressed medical image for telemedicine
applications still remains an important issue. In this paper,
the evaluation of diagnostic quality of compressed medical
images using objective and subjective testing will be
presented. Three different medical image modalities which
are CT, MRI, and X-ray have been compressed and
decompressed using DWT for different compression ratios.
The quality of the reconstructed images has been measured
objectively using objective measures such as MSE, MAE,
SNR, and PSNR. Ten non specialist observers have been
involved to carry out the subjective evaluation. Based on the
quality of the reconstructed images, the PSNR obtained has
been between 35.3dB to 58.0dB for CT scan images, 38.6dB
to 55.0dB for MRI and 34.5dB to 51.0dB for x-ray images.
For clinical applications such as telemedicine or
teleradiology, the compression ratio of 30:1 is acceptable for
CT images, and a compression ratio of 40:1 is acceptable for
MRI, and compression ratio of 20:1 is acceptable for x-ray
images.
Keywords: Medical Image, Wavelet Transform,
Telemedicine.
minimizing storage requirement and speeding
transmission time. The primary goal of medical image
compression is to achieve the best possible fidelity for
the available communication and storage channels,
therefore the objective of compression is to reduce the
data volume and to achieve a low bit rate in the digital
representation of radiological images without perceive
loss of image quality [2]. Image data compression can
be classified into two broad categories: Lossy and
Lossless (information preserving) [3]. Lossy
compression schemes have not been widely used for
both clinical and legal reasons. However standard and
newer Lossless compression algorithms such as
JPEG2000 and wavelet-based compression can yield
images statistically identical diagnostic results
compared with using the original images without any
loss [4,5], therefore lossless image coding is important
for medical image compression because any
information loss or error caused by the image
compression process could affect clinical diagnostic
decision [6].
The aim of this paper is to evaluate a set of
compressed medical images using wavelet transform
technique for an acceptable degree of the reconstructed
CT, MRI, and X-ray images for different compression
levels. Both objective and subjective methods are
applied for this evaluation.
2. Image Quality Measures
1. Introduction
Remote medical monitoring is a telemedicine
application in which dynamic fluoroscopy images
during a radiological interventional procedure are
transmitted in real time or near real time to another
location, where the physician or specialist can advice
regarding the diagnostic and therapeutic strategies. [1].
To represent such large raw medical images with
smallest possible number of bits, image data
compression is essential and plays an important role in
Methods for image quality evaluation can be
classified as objective and subjective measures. By
objective measures some statistical indices are
calculated to indicate the reconstructed image quality
and by subjective measure viewers read images directly
to determine their quality
.
2.1 Objective Measures
A widely used measure of reconstructed image for
an N x M size image is the mean square error (MSE) as
given by [6].
INFOS2008, March 27-29, 2008 Cairo-Egypt
© 2008 Faculty of Computers & Information-Cairo University
HBI-15
MSE =
1
NM
N −1 M −1
∑ ∑ ⎡⎢⎣ f (i, j ) − f (i, j )
*
1= 0 j = 0
2
⎤
⎥⎦
(1)
Where f (i, j ) the original is image data and
Signal-to-Noise Ratio (SNR) is widely used in the
signal processing literature (since it is related to the
signal power and noise power), and is perhaps more
meaningful because it gives 0 dB for equal signal and
noise power. SNR is used more commonly in the
f * (i, j ) is the compressed image data.
image-coding field. So, the SNR that is used
corresponding to the above error is defined as
N −1 M −1
⎧
2
f (i , j )
∑
∑
⎪
⎪
i =0 j =0
SNR = 10 log ⎨ N −1 M −1
⎪ ∑ ∑ f (i , j ) − f * (i , j )2
⎪⎩ i = 0 j = 0
[
⎫
⎪
⎪ dB
⎬
⎪
⎪⎭
]
(2)
Another quantitative measure is the peak signal-tonoise ratio (PSNR), based on the mean square error of
the reconstructed image. The formula for PSNR is
given by :
⎛ 2B −1⎞
⎟⎟ dB
PSNR = 10 log⎜⎜
⎝ MSE ⎠
(3)
Where B is the bit depth of the image. For an 8-bit
image, the PSNR is computed by:
⎛ (255)2
PSNR = 10 log⎜⎜
⎝ MSE
⎞
⎟ dB
⎟
⎠
(4)
2.2 Subjective measure
Subjective evaluation by viewers is still a method
commonly used in measuring image quality. The
subjective test emphatically examines fidelity and at
the same time considers image intelligibility. When
taking subjective test, viewer's focus on the difference
between reconstructed image and the original image,
they notice such details where information loss cannot
be accepted. The representative subjective method is
Mean Opinion Score (MOS) [7, 8, and 9]. It has two
kinds of scores: one is absolute and another is relative.
Two examples are shown below in Table 1. In our
experiment, we use absolute score in order to seek the
consistency between subjective and objective
measures. Each viewer compares the reconstructed
image with the original one to decide which level it
belongs to and gives the score.
3. Results and Discussion
Three different medical image modalities which are
CT, MRI, and digitized x-ray as shown in figure 1 have
been used in this study. The test results obtained by
both objective and subjective measures are shown in
figures 2-4. Table 2 summarizes the result for MSE and
PSNR for these images and Figure 2 illustrates the
PSNR values versus compression ratio. For the
subjective evaluation results, Table 3 represents the
average score of 10 non specialists' student observers
from the National University of Malaysia. A score of 5
is no distortion (Excellent), score of 4 represents a little
distortion which can be ignored (Good), score of 3
shows distortion which can be seen evidently but it can
be accepted (Fair), score 2 shows a lot of distortion,
which can not be accepted (Bad), and finally score of 1
shows too much distortion, therefore can not be
tolerated (Very Bad).These results have been illustrated
in figure 3 and a comparison between the original and
reconstructed images is illustrated in figure 4.
Table 1. Mean Opinion Score (MOS) method used for subjective evaluation
Absolute Score
Relative Score
5
Excellent
5
The best in the group
4
Good
4
Better than the average
3
Fair
3
The average of the group
2
Bad
2
Worst than the average
1
Very Bad
1
The worst in the group
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Table 2. The MSE and PSNR results for CT, MRI, and X-Ray images
CT Modality
MRI Modality
X-Ray Modality
Compression
Ratio
MSE
PSNR(dB)
MSE
PSNR(dB)
MSE
PSNR(dB)
10:1
0.1
58.0
0.2
55.0
0.5
51.0
15:1
1.1
47.7
0.7
49.7
1.9
45.3
20:1
3.9
42.2
1.3
47.0
4.0
42.1
25:1
7.0
39.7
3.1
43.2
9.0
38.6
30:1
10.8
37.8
4.2
41.8
13
37.9
35:1
14.5
36.5
6.5
40.0
20
35.1
40:1
19.2
35.3
8.9
38.6
23
34.5
Table 3. Subjective evaluation results for CT, MRI, and X-Ray images
The Average score of all readers
Compression
Ratio
Score for CT Image
Score for MR Image
Score for X-Ray Image
Original Image
5
5
5
10:1
4
5
4
20:1
4
4
4
30:1
3
4
3
40:1
3
4
2
A
B
C
Figure 1. Original Test Images: (A) CT (B) MRI (C) X-Ray
PSNR for CT
PSNR for MRI
PSNR for Xray
PSNR (dB)
80
60
40
20
0
10
15
20
25
30
35
Compression Ratio
Figure 2. PSNR and compression ratio for CT, MRI, and X-ray images
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40
MOS
CT Image
MRI
X-ray
6
5
4
3
2
1
0
Original Image
10
20
30
40
Compression Ratio
Figure 3. Subjective score results for CT, MRI, and X-ray images
A
B
30:1
40:1
C
20:1
Figure 4. Comparison between original and compressed images
4. Conclusion
In this study; three different modalities of medical
images which are CT, MRI, and X-Ray have been
compressed and reconstructed using wavelet transform.
Objective and subjective evaluation has been done to
evaluate the diagnostic quality of the reconstructed
images. For the objective evaluation, the results show
that the PSNR which indicates the quality of the
reconstructed image is ranging from (35.3dB to58.0dB,
38.6dB to 55.0dB, and 34.5dB to 51.0dB) for CT, MRI,
and X-Ray respectively. For the subjective evaluation
test, the results show that the compression ratio of 30:1
was acceptable for CT image, and a compression ratio
of 40:1 was acceptable for MRI whereas for X-Ray
image 20:1 was acceptable for clinical applications.
HBI-18
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