Paper Menu >>
Journal Menu >>
J. Biomedical Science and Engineering, 2008, 1, 110-115
Published Online August 2008 in SciRes. http://www.srpublishing.org/journal/jbise JBiSE
Significance of ROI Coding using MAXSHIFT
Scaling applied on MRI Images in Teleradiology-
Pervez Akhtar1, Muhammad Iqbal Bhatti2, Tariq Javid Ali1, 3 & Muhammad Abdul Muqeet2
1EPE Department, NUST, PNEC, Karachi-Ca mpus, Kara chi-75350, Pakistan. 2BME Department, SSUE T, Karachi-75300, Pakistan. 3ISD Department, IT
Division, AKU, Karachi-74800, Pakistan. Correspondence should be addressed to firstname.lastname@example.org, email@example.com, firstname.lastname@example.org, and
Within the expanding paradigm of medical imaging
in Teleradiology-Telemedicine there is increasing
demand for transmitting diagnostic medical imagery.
These are usually rich in radiological contents and
the associated file sizes are large which must be
compressed with minimal file size to minimize
transmission time and robustly coded to withstand
required network medium. It has been reinforced
through extensive research that the diagnostically
important regions of medical images, the Region of
Interest (ROI), must be compressed by lossless or
near lossless algorithm while on the other hand, the
background region be compressed with some loss
of information but still recognizable using JPEG
2000 standard. We develop a compression model
and present its application on MRI images. Applying
on MRI images achieved higher compression ratio
16:1, analogously minimum transmission time, using
MAXSHIFT method proved diagnostically significant
and effective both objectively and subjectively.
Keywords: Image compression, MRI, ROI,
The objectives of teleradiology-telemedicine are to
improve access and to enhance overall quality of care at
an affordable cost. Improved access and cost savings
could be achieved by allowing a doctor to remotely
examine patients or to consult with a specialist. This
reduces or eliminates the time and expense of travel
necessary to bring the patient to the doctor or the doctor
to the patient . Quality of care is improved by
providing the diagnostically important images. Rigorous
research in diagnostic imaging and image compression in
teleradiology-telemedicine is gaining prominence all over
the world, particularly in developing countries .
Engineers are developing technologies and tools,
enabling the medical practitioners to provide efficient
treatment. From the elaborate medical information, the
doctor prefers to focus on certain selected region(s) of
interest. Also the doctors are more comfortable with
image processing and analysis solutions that offer
subjective analysis of medical images than depending on
the objective engineering results alone. Technology
assisted, integrated diagnostic methods are of high
relevance in this context .
An 8-bit gray scale image with 512 x 512 pixels
requires more than 0.2 MB of storage. If the image can be
compressed by 8:1 without any perceptual distortion, the
capacity of storage increased 8 times. This is significant
for teleradiology-telemedicine (TT) scenario due
limitations of transmission medium. If we need T units of
time to transmit an image, then with 16:1 compression
ratio the transmission time will decrease to T/16 units of
time. It has been reinforced through extensive research
that the diagnostically important regions of medical
images must be compressed by lossless or near lossless
algorithm, while on the other hand, the background
region be compressed with some loss of information but
still recognizable using the JPEG2000 (JP2K) standard
. Applying JP2K ROI coding using MAXSHIFT
scaling method on MRI images achieved high
compression ratio 16:1 with varying quantization levels
(1/128, 1/64, and 1/32) analogously reduced transmission
time; the MAXSHIFT method proved very effective both
objectively and subjectively.
The paper layout follows as: In section 2, we introduce
lossless coding schemes and ROI coding in JP2K
standard as concepts. Section 3 describes our approach in
context of MAXSHIFT scaling method and the model
description. In section 4, we give experimental results and
discussion. We conclude our paper in section 5.
SciRes Copyright © 2008
P. Akhtar et al. / J. Biomedical Science and Engineering 1 (2008) 110-115 111
SciRes Copyright © 2008 JBiSE
In the medical scenario, ROI is the area of an image,
which is of clinical (diagnostic) importance to the doctor
. Certain image specific features like the uniformity of
texture, color, intensity, etc. generally characterize as
region of interest. Medical images are mostly in gray-
scale . The gray scales of an M-bit level image (where
M can be 8, 12 or 16 bits) can be represented in the form
of bit-planes , See Figure 1. Identifying and extracting
ROI accurately is very important before coding and
compressing the image data for efficient transmission or
storage. In different spatial regions and identifying the
ROI in the image, it is possible to compress them with
different levels of reconstruction quality. This way one
could accurately preserve the features needed and
transmit those for medical diagnosis or for scientific
measurement, while achieving high compression overall
by allowing degradation of data in the un-important
Figure.1. Colon image and its eight bitplanes.
2.1. Lossless coding and JPEG 2000 standard
In medical context the regionally lossless schemes have to
be studied more closely. They can be any of the following
based on different types of end-user/observer or context:
1) Visually lossless: non-clinical human observer.
2) Diagnostically lossless: clinical-observers; in the
diagnosis significant degrees of observer
dependent variations exist.
3) Quantifiably lossless: mostly non-human observer
or computer assisted detection.
One important consideration here is that, what may be
visually lossless or quantifiably lossless may not be
diagnostically lossless .
Most of the commonly used methods use JP2K
standard that involves the following important steps .
Along with these mentioned below, additional processing
related to ROI mask generation and customized coding
that suits the user requirement is done. Discrete Wavelet
Transform (DWT) is performed on the tiles or the entire
image based on size of the image .
1) If the ROI is identified then ROI mask is derived
extracting the region indicating the set of
coefficients that are required for lossless ROI
2) The wavelet coefficients are quantized as per
desired quality of reco nstruction.
3) The coefficients that are out of the ROI are scaled
up/down by a specific scaling value. If there are
more than one ROI, these can be multiply coded
with different scaling values.
4) The resulting coefficients are progressively
entropy encoded (with the most significant bit
planes first). As overhead information, the scaling
value assigned to the ROI and ROI mask
generation but scales up the background
coefficients in order to recreate the original
3.1. MAXSHIFT and general scaling methods
While compressing the medical images it is important to
consider ROI masking methods so as to get diagnostically
important area as a lossless region. MAXSHIFT method
in comparison to general scaling method supports the use
of any mask since the decoder does not need to generate
the mask, See Figure 2. Thus, it is possible for the
encoder to include an entire subband, that is, the low-low
subband, in the ROI mask and thus send a low-resolution
version of the background at an early stage of the
progressive transmission. This is done by scaling of all
quantized transform coefficients of the entire subband. In
other words, the user can decide in which subband he will
start having ROI and thus, it is not necessary to wait for
the whole ROI before receiving any information for the
background. However, since the background coefficients
are scaled down rather than scaling up ROI coefficients,
this will only have the effect that in certain
implementations the least significant bitplanes for the
background may be lost. In other words, the background
received in a degraded state (lossy) but still recognizable.
The advantage is that the ROI, which is considered to be
the most important part of the image, is still optimally
treated while the background is allowed to have degraded
quality, since it is considered to be less important.
In medical situations during compression phase, lossy
schemes are not preferred. To avoid the chance of loosing
any diagnostic information, a 32x32 code block size is
selected with considering ROI size less than one fourth of
the original image. Lossless schemes prove costly with
less compression efficiencies and are ineffective in
certain application domains. Regionally lossless schemes
prove as a valuable/meaningful solution between the
completely lossless or lossy ones. In these, lossless
coding is done for the ROI and lossy coding to the less
significant background image .
3. OUR APPROACH
We develop our approach based on the following
1) The doctor prefers to use eyes (subjective decision)
to select the region that is of importance through
interactive evaluation and manual marking or
selection of regions [12, 5].
112 P. Akhtar et al. / J. Biomedical Science and Engineering 1 (2008) 110-115
SciRes Copyright © 2008 JBiSE
Figure 2. The MAXSHIFT method: (top left) ROI and background at the same level, (top right) general scaling method, and (bottom)
MAXSHIFT method, See subsection 3.1 for details.
2) The pixels that represent the diagnostically
relevant data are of interest to the doctor. These
bits need not belong to visually significant data
like sudden intensity variations in images.
3) The need of higher compression for a faster
An ROI is a part of an image that is coded earlier in the
codestream than the rest of the image. Coding steps in the
MAXSHIFT method are :
1) Generate an ROI mask.
2) Find the scaling value which is greater than or
equal to the largest number of magnitude bit
planes for any background coefficient in any
code-block in the current component.
3) Scale down all background coefficients given by
mask in step (1) using the scaling value from step
4) Write the scaling value into codestream.
3.2. Transmission hierarchy
Recent acceptance and deployment of picture archiving
and communications system (PACS)  in hospitals and
the availability of digital imaging and communications in
Medicine (DICOM) medical images via PACS is and
important building block of telemedicine. Figure 3
illustrates transmission hierarchy, the extension of a
PACS to remote sites (imaging center); using
4. EXPERIMENTAL RESULTS
MRI images were obtained from a famous national health
care institution. We used IrfanView  for image format
conversion, MATLAB  for mathematical treatment
and graphs, JJ2000  for image compression, and
Microsoft Office Excel to organizing the data. The
performance evaluation of JP2K compression standard
using MAXSHIFT method is an individual application on
selected images from each of the modalities. Figures 4
and 5 show our developed compression model and an
Figure 3. Transmission hierarchy.
P. Akhtar et al. / J. Biomedical Science and Engineering 1 (2008) 110-115 113
SciRes Copyright © 2008 JBiSE
Figure 4. The compression model for TT in Fig. 3.
Figure 5. Application of the compression model in Fig. 4 to a CT image.
4.1. Subjective and Objective Measurements
The evaluation of the reconstructed images was based
upon mixed criteria including: Mean Opinion Score
(MOS)  for which image quality assessment was
carried out by visually comparing the specific ROI of the
original and reconstructed images, after the application of
the above mentioned compression techniques. The images
were presented to six radiologists in a random order. The
observers were asked to evaluate the reconstructed
images in accordance with their diagnostic value. The
ranking was done on an integer scale based on Moving
Picture Quality Metric (MPQM) model  from 1 to 5,
that is, 1 (bad), 2 (poor), 3 (fair), 4 (good) and 5
(excellent). An image is ranked as acceptable if it
maintains satisfactory diagnostic value (MOS ≥ 4).
When reconstructed images to be encoded contain ROI,
Peak Signal-to-Noise Ratio (PSNR) is calculated for the
ROI alone and over whole image (for the ROI and the
background). For all of the ROI experiments a five level
114 P. Akhtar et al. / J. Biomedical Science and Engineering 1 (2008) 110-115
SciRes Copyright © 2008 JBiSE
Figure 6. (Left) MRI image with ROI marked in red color and
(right) the reconstructed image at the quantization level 1/128 and
compression ratio 16:1.
DWT is used and all of the coefficients from the lowest
level are included in the ROI. Experiment was completed
using JJ2000. The effect of JP2K compression standard
on MRI images for lossless compression ratio is presented
in Figure 6. The images, at first, are compressed and
decompressed at 0.08 bpp up to 4.0 bpp (128:1, 64:1,
32:1, 16:1, 8:1, 4:1 and 2:1 compression ratios) at 1/128,
1/64 and 1/32 quantization levels.
The MRI image in Figure 6 with ROI (cranial blockage)
marked in red color is initially obtained from MRI
modality and archived in a DICOM imaging database
which is later converted to Portable Gray Map (PGM)
format through IrfanView for encoding with JJ2000
software. The image then passed through various stages
of the JP2K ROI coding algorithm and finally we get a
compressed image ready for storage or transmission. The
reconstructed image then followed a similar pattern in
reverse when a compressed image is received or accessed
from archival. The size of ROI is less than one fourth and
typically about 1/6 of the original image.
The superiority of the ROI coding scheme, based on
the MAXSHIFT method, over without ROI, can be
subjectively and objectively judged at different
quantization levels. At 1/128 quantization level achieved
a compression ratio of 16:1, and subjectively got MOS >
4 and objectively gained up to 20.31 dB, See Figure 7. At
1/64 quantization level with same compression ratio,
subjectively got MOS ≥ 4, and objectively gained up to
9.31 dB. At 1/32 quantization level with same
compression ratio, subjectively got MOS < 4, and
objectively show no gain.
Our results show that 16:1 compression ratio on 1/128
quantization level using 32x32 code block size, with gain
exceeding 18 db appropriate for the MRI images to be
reconstructed in lossless settings reducing transmission
Our results of reconstructed medical images quality,
subjectively and objectively, have shown that the
application of JP2K standard based on DWT compression
technique with ROI coding using MAXSHIFT scaling
method proved diagnostically significant in MRI medical
imagery and helpful in identifying the diseases zone. A
gain exceeded 18 dB at 1/128 quantization level achieved
with minimum transmission time through various network
medium. The results have shown MRI images
compression ratio 16:1, acceptable, and may be employed
for diagnostically lossless transmission in a teleradiology-
telemedicine scenario. In future, we will be preparing our
implementation (model) for real-time transmission of
diagnostic imagery through automating of image format
conversions and simulate on a software/hardware
environment. We are also interested in exploration of
other radiological areas with rich radiological contents in
computed radiography like Mammography.
Figure 7. Objective measurement for MRI image in Fig. 6, See subsection 4.3 for details.
P. Akhtar et al. / J. Biomedical Science and Engineering 1 (2008) 110-115 115
SciRes Copyright © 2008 JBiSE
We acknowledge radiology departments of Aga Khan University
Hospital Karachi, Pakistan, as well as Sindh Institute of Urology and
Transplantation, Karachi, Pakistan, for their consistent support without
which this work would not be achievable.
 M. Moore. (1993) Elements of Success in Telemedicine Projects.
Report of a research grant from AT&T Graduate School of Library
and Information Science, the University of Texas at Austin.
 B.S. Bedi. (2004) Standardization Telemedicine: National Initiative
in India. ATA-2004, Tampa.
 S. Grimes. (2007) Clinical Engineers: Stewards of Healthcare
Technologies. IEEE Engineering in Medicine and Biology Magazine,
 C. Christopoulos, A. Skodras, & T. Ebrahimi. (20 00) The JPEG2000
Still Image Coding System: An Overview. IEEE Transactions on
Consumer Electronics, 46, 1103-1127.
 M.D. Srinath. (2005) Image Processing: A Clinical Radiologist
Perspective. Proceedings of National Conference on Image
Processing, Bangalore, India.
 P.N. Jayakumar. (2005) Challenges in Medical Image Processing.
Proceedings of National Conference on Image Processing,
 S.T. ChandraShekar & G.L. Varanasi. (2005) Region of Interest
Coding in Medical Images using Diagnostically Significant
Bitplanes. Proceedings of Conference on Emerging Aspects of
Clinical Data Analysis, Italy.
 J. Carrino. (2003) Digital Image Quality: A Clinical Perspective.
Quality assurance. The Society for Computer Applications in
Radiology, Great Falls, VA, 29-37.
 C. Christopoulos, J. Askelf, & M. Larsson. (2000) Efficient methods
for encoding regions of interest in the upcoming JPEG2000 still
image coding standard. IEEE Signal Processing Letters, 7(9), 247-
 S. Burak, C. Tomasi, B. Girod, & C. Beaulieu. (2001) Medical
Image Compression based on Region of Interest with Application to
Colon CT images. Proceedings of 23rd International Conference of
the IEEE Engineering in Medicine and Biology Society, Istanbul,
 K. Varma & A. Bell. (2004) JPEG2000 - Choices and Tradeoffs for
Encoders. IEEE Signal Processing Magazine, 21(6), 70-75.
 R.S. Kalawsky. (2000) The Validity of Presence as a Reliable
Human Performance Metric in Immersive Environments. 3rd
International Workshop on Presen ce, Delft, Netherlands.
 M. Boliek. (2000) JPEG 2000 Part I Final Committee Draft Version
 D. Leotta & Y. Kim. (1993) Requirements for picture archiving and
communications. IEEE Engineering in Medicine and Biology
Magazine, 12(1), 62-69.
 I. Skiljan. (2004) Irfan view . Vienna University of Technology.
 R. Gonzalez, R. Woods, & S. Eddins. (2004) Digital image
processing using Matlab, Pearson Education, Inc.
 D. Santa-Cruz, R. Grosbois, & T. Ebrahimi. (2003) JJ2000: The
JPEG 2000 reference implementation in Java. Proceedings of the
First International JPEG 2000 Workshop, Lugano, Switzerland, 46-
 T. Kratochvil & P. Simicek. Utilization of MATLAB for Picture
Quality Evaluation. Institute of Radio Electronics, Brno University
of Technology, Brno.
 M. Adams & F. Kossentini. (2000) Reversible integer-to-integer
wavelet transforms for image compression: performance evaluation
and analysis. IEEE Transactions on Image Processing, 9(6), 1010-