# An Adaptive Source-Channel Coding with Feedback for Progressive Transmission of Medical Images

Authors: Jen-Lung Lo; Saeid Sanei; Kianoush Nazarpour
Journal: International Journal of Telemedicine and Applications
 (2009)

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## Abstract

A novel adaptive source-channel coding with feedback for
progressive transmission of medical images is proposed here. In
the source coding part, the transmission starts from the region of
interest (RoI). The parity length in the channel code varies with
respect to both the proximity of the image subblock to the RoI and
the channel noise, which is iteratively estimated in the receiver.
The overall transmitted data can be controlled by the user
(clinician). In the case of medical data transmission, it is vital
to keep the distortion level under control as in most of the cases
certain clinically important regions have to be transmitted
without any visible error. The proposed system significantly
reduces the transmission time and error. Moreover, the system is
very user friendly since the selection of the RoI, its size,
overall code rate, and a number of test features such as noise
level can be set by the users in both ends. A MATLAB-based TCP/IP
connection has been established to demonstrate the proposed
interactive and adaptive progressive transmission system. The
proposed system is simulated for both binary symmetric channel
(BSC) and Rayleigh channel. The experimental results verify the
effectiveness of the design.


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## Body

## 1. Introduction

Transmission of medical images aiming at
medical consultation, diagnosis, and treatment, or for training purposes
demands highly reliable and high-speed communication systems. Since, typically,
medical images contain large amounts of crucial clinical data, no visible
encoding error is tolerated in the clinically important regions. Hence,
advanced methodologies for gaining distortion-less reconstructed images, in
fairly acceptable transmission rate, make it possible to transmit a medical image
over a noisy channel. However, even by utilizing current high-speed broadband
connections, the transmission of medical images has not been entirely
successful; although using more parity bits enables a higher protection of the
data against channel noise, the transmission time and the complexity of the
channel coding considerably increase. By prioritizing the image regions based
on their clinical information contents in one hand, and establishing a variable
parity length with regard to the state of the communication channel on the
other hand a higher data speed and immunity against noise can be achieved.The concept of image coding
utilizing discrete wavelet transform (DWT) and embedded zerotree wavelet (EZW)
has been initially proposed in [1] and various
methods have been developed based on this idea [2–8]. In [2], an
effective scheme for image compression has been proposed where the
spatial-spectral features of the image have been taken into account in order to
show that wavelet transform is particularly well suited for progressive
transmission. In [3],
the concepts of DWT, EZW, progressive image transmission, and RoI have been utilized. These
techniques have been effectively employed in our work. In [4], a method
for progressive image transmission using EZW is presented where a number of
constraints are imposed to provide variable bitrates for each frequency band.
In [5], a compression method using linked significant tree (LST) has been utilized
to encode all the significant coefficients in order to reduce the number of
symbols and increase the compression efficiency. The authors in [6] have
developed an image coding algorithm that incorporates the features of zerotree
and zero-block based algorithms. The main contribution in this recent algorithm
is partitioning the wavelet-transformed image into coefficient blocks and to
generate roots in top-most subband by using a block tree. In [7], another
progressive image transmission method has been proposed based on a quadtree
segmentation procedure in order to provide fairly good quality transmitted
images while keeping the computational cost low. The authors in [8] have
developed strategies to exploit the wavelet coefficients in different subbands
for designing different vector quantization (VQ) coding to achieve a fast and
efficient progressive transmission.Shannon's information theory states that the
performance of transmission schemes can be optimized in source and channel
coding separately. However, the result holds with infinite block size, infinite
coding complexity, and stationary channels. Such conditions are difficult to
meet in practice. Hence, joint source-channel coding (JSCC) scheme attracts the
interest of many. The JSCC scheme consists of a quantizer, an entropy, and
channel coders to meet the target source rate, to achieve the required
robustness in channel coding, and to find an optimal bit allocation between source
and channel coding systems. Several methods have focused on designing adaptive
joint source-channel coding (JSCC) schemes and introducing the properties of
unequal error protection (UEP) and rate allocation to achieve efficient
transmission [9–20]. In [9],
authors surveyed several algorithms in JSCC system design and fast
source-channel bit allocation for various transmission channels. They also
provided a local search strategy to improve the initial low-complexity and
rate-optimal scheme to achieve a distortion-optimal solution. An image transmission system was proposed in
connection with JPEG2000 [10, 11]. The JSCC and UEP schemes in this approach try to optimize the
rate-distortion function to achieve an efficient transmission. JPEG2000
provides high compression efficiency and introduces various features not
included in other compression algorithms. This method has also adopted a
standard component for medical image compression by digital imaging and
communications in medicine (DICOM). JPEG2000 adopts embedded block coding with
optimal truncation (EBCOT) as a source coder. The EBCOT algorithm has high
compression efficiency with a high complexity price, which needs a powerful
central processing unit (CPU) for support. The compression part of the proposed
scheme is similar to that of JPEG2000's.
However, unlike JPEG2000's
our scheme provides variable parity length for an adaptive channel coding,
which replaces EBCOT with EZW for compression and progressive image
transmission. In [12], a framework for solving the end-to-end
problem of progressive transmission of images over noisy channels has been
presented which allows finding the optimal length of parity codes for each
fixed length package to have minimum distortion in the decoded images. In [13],
a joint source-channel decoder-based method for data transmission over noisy
channels has been introduced. Multipath fading for communication over a noisy
channel has become a complex problem. The authors in [13] have used the maximum
a posteriori (MAP) method to design a joint source-channel coding system. An
adaptive source-channel coding scheme based on subband coding has been used in
[14]. In this approach, a suitable source and channel coding rates for each
subblock has been
proposed to minimize the total distortion. In [15], a low-complexity technique for the
transmission of medical images has been proposed whereby the channel noise
information has been effectively exploited. The main idea in [15] is to segment
the bitstream into consecutive subblocks of variable lengths and consider a tradeoff between the
levels of source and channel coding systems. The authors in [16] proposed a JSCC method for transmission of images
over fading channels and demonstrated the application of rate-compatible
low-density parity-check (RC-LDPC) codes constructed by the progressive
edge-growth algorithm, and used the UEP to protect the images. In [17], a
parametric methodology in progressive source-channel coding for rate allocation
has been developed. The channel code rate is chosen based on the properties of
source coder and the conditions of channel. The UEP strategies for efficient
progressive transmission are proposed in [18]. Under the condition of a target
transmission rate, the JSCC algorithm computes a UEP scheme that maximizes the
number of corrected bits over a noisy channel. In [19], authors have used a
concatenation of rate-compatible punctured convolution code and cyclic
redundancy check (CRC) code to form a UEP scheme and find the optimal rate
allocation solutions for progressive image transmission.Many communication systems allow two-way
communication implying that the signals are back from receiver to transmitter
to adjust the system parameters and obtain better system performance. The
authors in [20–23] have utilized the
concept called hybrid automatic repeat request (HARQ) to ask for retransmission
of erroneously received data and tradeoff allocation between the source and
channel codes according to a rate-distortion optimization policy. Many
researches on tradeoff allocation bits between source codes and channel codes
assume that the noise-level in the channel is known in advance. Therefore, the
feedback signal is figured out based on the known noise levels and the
constraints set by the user. In the proposed algorithm, this has been modified
since the parity lengths change according to the noise level in the received
data whereby the amount of detected incorrect data is used to predict the
conditions of the practical transmission channel. For medical image
transmission, the quality of the reconstructed images (especially in the RoI)
should be acceptable. This can be set as the constraint for the quantizer and
the compression algorithm in advance. Therefore, the quality of the reconstructed
image is only affected by the channel state and the proximity to the image RoI.
The feedback signal in the proposed scheme updates the parity length without
the need for retransmission of the data or adding any extra overhead.The principal idea behind all these methods is that in a progressive
transmission framework, the receiver
reconstructs the transmitted image at various bit rates, which makes the fast
and reliable retrieval of large images possible. In other words, the quality of the
reconstructed image totally depends upon the volume of the received data, and
the images can be reconstructed in any bitrate. Furthermore, the image
subblocks are coded separately. Due to the high sensitivity to
transmission noise, progressive transmission of images over noisy channels has
to be accompanied by an appropriate channel
coding, or a joint source-channel coding scheme [12]. The noise in the current communication systems can be due to the
electronic components, fading, Doppler shift for mobile systems, bad weather,
interferences, attenuations, and so forth.The Reed-Solomon (RS) codes utilized here are block-based error
correcting codes and are widely used for channel coding. The RS(p,q) codes correct the symbol error and
not the bit error; lengths in terms of symbols. Thus, RS is suitable for burst
error detection and correction [23].In this paper, we propose a novel
interactive and adaptive joint source-channel coding with feedback algorithm
for progressive transmission of medical images. This approach benefits from the
idea of the JSCC, RoI, UEP, and
feedback technique together as follows.(1)
The conventional RS channel coding has been used.(2)
The variability of the parity code
corresponds to both the proximity to the center of RoI and the state of the practical transmission channel at the
same time; this makes an efficient source-channel coding possible.(3)
The selectivity of the RoI is totally interactive and can be
defined by the user in the receiver. This makes the method favorable to be used
by clinicians who require fast access to the RoI in the image.(4)
An algorithm for detection of the blocks in
error is developed to detect
and recover the corrupted data, estimate the noise level in the practical
transmission channel, and feedback the information of the noisy channel to the
transmitter to control the error rate in the reconstructed images in the
subsequent transmission.By utilizing our flexible system, a minimum
distortion of the transmitted images in a fairly shorter transmission time is
achieved. As the main contribution of this research, we
adaptively control the lengths of parity code streams simultaneously with
respect to the selected region (i.e., longer lengths correspond to the areas
closer to the center of RoI)
and the amount of corrupted received data in the receiver. The system
block diagram is shown in Figure1.Figure 1
The overall system block diagram.This paper is organized as follows. Section3 briefly describes the
concepts of DWT and EZW. Following that, we provide the details of RoI selection. In Section 2,
application of RS channel coding in a variable-parity length scheme will be
explained. In Section 4, an algorithm for detection of the blocks in error is
developed to evaluate the amount of incorrect received data in the receiver. Simulation
results are subsequently reported in Section 5 followed by concluding remarks
in Section 6.


## 2. Discrete Wavelet Transform and Embedded  Zerotree Wavelet

Wireless transmission of medical images involves construction of an effective joint
source-channel coding to not only preserve the diagnostic information but also to
enable progressive streaming of the data from the host to the receiver. EZW is
a simple, efficient, and flexible compression algorithm for low bitrate image
coding. The properties of DWT and EZW
allow us to code and compress the data blocks individually and also compress it
at any bitrate. Therefore, based on progressive encoding, we can compress a
block into a bitstream with increasing accuracy. Traditionally, the
input images are decomposed into many subblocks each to be coded, compressed,
and transmitted individually. Therefore, the input image is segmented into a
number of subblocks firstly. And then wavelet transform (WT) decomposes each
subblock into different time-frequency components.As it is detailed in [24], EZW codes the image into streams of six
symbols, namely, p,n,z,t,0, and
1. In mathematical terms, considering the image amplitude at location (x,y) is denoted by γ(x,y), and tn  is
the threshold in nth
iteration, the definitions of the symbols are given in Table 1. EZW suits progressive
data transmission since it enables hierarchical encoding and decoding.Table 1
Definition of symbols.
SymbolsDescriptionpForγ(x,y)≥tn, called significant coefficients at
threshold tn.nForγ(x,y)<0&|γ(x,y)|≥tn, called negative significant.zForγ(x,y)<tn, but some of its descendants have a value greater than tn, called isolated zero.tForγ(x,y)<tn, and all its descendants have magnitudes less
than tn, called zerotree zero.1,0Refinement bits for reconstructing image.In the proposed system, we chose a 3-level Haar wavelet transform (HWT) to perform the
DWT for each subblock due to its simplicity and being faster and easier to
implement in comparison with other DWT methods [21]. The coefficients in the
lowest frequency subbands show the background information of the subblocks. The
coefficients in the higher frequency subbands represent the details and edges.
After computing the HWT, we compress the coded data according to a variable
thresholding mechanism governed by the EZW. Hence, a suitable approach is to use a variable threshold and transmit
only those coefficients to the decoder that are larger than the threshold. The
first step in the EZW algorithm is to determine the initial threshold level t0
and then repeatedly lowering the threshold by half at a time until the
threshold has become smaller than the smallest coefficient to be transmitted; or
the iteration is stopped by request. The initial threshold t0 is set as follows:(1)t0=2N,N=log2max(|γ(x,y)|), where max(⋅) refers to the maximum value. The final
threshold level determines the length of the bitstream output through the EZW process, the compression ratio
of the input images, and the resolution of the reconstructed image. The length
of the output bitstream Mi is related to the number of times the
threshold is halved as (2)Mi=∑k=0nTiB(t0i2k), where B(t) is the output bitstream of EZW based on
the threshold t. t0i is the initial threshold in the ith subblock, and nTi is the number of times the threshold is halved
in the ith subblock. Therefore, potentially, we can
achieve any resolution in the reconstructed images through setting the initial
threshold, and the number of times the threshold is halved. As an example, Figure 2 shows the three regions of RoI, R1, R2 centered at point (x,y). In Figure 2, the resolution in area R2 is the lowest and that of RoI is the highest. Therefore, the quality of the
reconstructed subblocks and consequently the compression rate depends on the
size of the embedded zerotree. This is set based on the distance from the
center of RoI. Based on the assigned
parameters for EZW, the data in each subblock would be compressed with
different rates depending on the location of the subblock. Often, the physician
is only interested in a particular part of the image. Therefore, the system is
designed in such a way to enable changing the location and size of the RoI without any emphasis on the other regions. In this example, the RoI, R1, and R2 may be defined as
for RoI:(3)(i−x)2+(j−y)2≤ra,forR1: (4)ra<(i−x)2+(j−y)2≤rb,forR2: (5)(i−x)2+(j−y)2>rb,
where x and y are the coordinates of the center of RoI assigned
by the physician using a mouse click, thereafter both values are sent
back to the host. The values of ra and rb are the radius of RoI and R1,
and i and j express the x−y image pixels' coordinates. In the successive
progressive stages, the values of ra and rb gradually
expand in the next progressive transmission to create the reconstructed image
of higher quality. In our proposed algorithm, the first transmitted image is the background low-resolution image. Then, the reconstruction is
progressively continued regarding the highest priority of RoI.Figure 2
Areas of different priorities in an image centered at the center of RoI.


## 3. Joint Source-Channel Coding

The progressive bitstream is heavily
affected by the channel noise; a single bit error may make the reconstruction
of the image impossible [10]. The adaptive joint source-channel coding and the
developed blind technique for evaluation of
errors in the receiver are the effective ways to address this problem. The
channel coding variables can be adjusted by using the feedback information
about the channel state and the image region content. The RS code is a subset of BCH codes; they are linear block codes and
are efficient for bursty-type transmission channels. The RS codes are constructed
by considering a polynomial for the input information and then use the
polynomial coefficients instead of the original data for transmission. In an
RS(p,q) code, p=2m−1 is the number of symbols in a codeword,
and m is the number of bits in each symbol. In the
proposed algorithm, the data in different image regions, as denoted in Figure 2
for three regions, are protected by variable length parity codes as for the
UEP. The data in the RoI is treated
as the most important information and protected by longer length parity codes. The
rest of the data is protected by shorter parity codes.Figure3 illustrates the channel coding strategy and  Figure 4 shows  the receiver.  The overall channel code length
remains fixed and the length of message k, qk; k=1,2,…,n, and the parity length C are variable. For RS
codes, (255−qn)/2  indicates the error-correction capability of RS coder. Here, the RS codes, RS(255,q),
have 255 symbols in length. According to the UEP, the ratio of parity to
overall code length for the n regions
should follow (6)CRoI>CR1>CR2⋯>CRn−1, where CRoI, the length of parity code, is for the RoI and so on. Furthermore, the length
of parity code is also affected
by the noise in the channel, that is, Cregion~(r,N), where r is the distance from the center of RoI, and N expresses
the noise in the practical transmission channel.Figure 3
The transmitter including the proposed channel coding block diagram.Figure 4
The block diagram for the receiver.An adaptive
variable parity allocation requires the error between the transmitted imageI(x,y) and the received image I^(x,y) to be minimized under the desired conditions.
Suppose that the error is defined as (7)ε=∥I(x,y)−I^(x,y)∥2, where ∥⋅∥2 denotes the I2-norm.
Generally, we wish to have the optimum parity length such that (8)Copt=minCεsubjecttoε≤εT, where εT is an acceptable error level in the receiver.
According to the above discussion, the parity length can be defined as (9)C=g(r,SN)=f(r,BER), where S/N and BER stand, respectively, for
signal-to-noise ratio and bit-error rate (the BER here represents the noise situation in the
channel and does not refer to the output bit-error rate). The functions g and f are generally nonlinear functions, which can be defined
empirically based on a number of trials. From Figure 5, f~(α0−αr) for a fixed BER, where r is
measured with respect to the number of pixels, and from Figure 6, it can be
concluded that f~(βBER+β0) for fixed proximity distance r. In more general applications, a more accurate and possibly complicated function
may be adopted. Therefore, a reasonably accurate function can be modeled as (10)f(r,BER)=(α0−αr)(βBER+β0),
or
(11)f(r,BER)=μ3BER−μ2r⋅BER−μ1r+μ0, where μis are constant coefficients and
can be easily found based on Figures 5 and 6.Figure 5
The ratios between the
lengths of parity codeCi and overall codeword of 255 symbols in different
regions at various fixed noise levels.Figure 6
Lengths of the parity codes based on various channel noise levels.


## 4. Detection of the Blocks in Error

Embedded coding
has many advantages, especially for progressive image/video transmission,
because the reconstructed images can be decoded at any bitrate. However, it is
highly sensitive to transmission noise and frequently collapses if even a single
transmitted information bit is incorrectly decoded. In most cases, the receiver sends back a
signal called HARQ to the transmitter requesting for retransmission under the
new constraints. Here, the channel noise level has to be somehow estimated in
the receiver. This will enable the change in the parity length in the following
transmissions. The estimation process is blind since there is no a priori
information about the channel. In some practical cases however, a test image
can be transmitted occasionally and evaluated in the receiver.In the proposed scheme, we have developed an algorithm to detect the
corrupted data in the receiver. This algorithm detects the address in which the
number of symbols for each subblock is indicated. The algorithm reassigns the
number of symbols to each subblock according to a built-in decision making
criterion (policy) when the number of symbols within the received data is
determined incorrectly by the receiver. Since the header information provides
the number of symbols per each data package, an extra check can be carried out
to ensure that the header is divisible by 4 and is not greater than 64 (to
address each pixel) for the8×8 subblocks. For more accuracy, the algorithm
checks the next address of the number of symbols to ensure that the current
data is correct. If successive error data is detected, the algorithm is able to
determine the number of subblocks and reassign the number of symbols for each
subblock in the set of detected incorrect data. Although, the proposed algorithm does not have capability of recovery of
the current corrupted package, but it can conjecture an appropriate number to
replace the corrupted data based on the built-in decision making criterion
(policy) to avoid the reconstructed image to collapse. This adjusts the system
for the transmission of the subsequent package and prevents propagation of the
error. This not only enhances the quality of the reconstructed image but also
provides a feedback for the adjustment of parity length.The performance of the system in different noise-levels is evaluated using the
peak signal-to-noise ratio (PSNR) defined as(12)PSNR(dB)=10log10A2MSE,
where A is the maximum image amplitude, and MSE is
defined as (13)MSE=∥I^−I∥22∥I∥22, where I^ represents the reconstructed subblock of the
image, and I is the subblock of the original input image.Finally, the
parallel structure of the channel coding and decoding scheme is very suitable
in hardware implementation of the system. A number of parallel boards can be
easily used in order to speed up the overall process.


## 5. Simulation Results

In this work, the proposed system is simulated in both binary symmetric
channel (BSC) and flat-fading Rayleigh channel. The BSC is the simplest channel
model, only zeros and ones are conveyed in the channel. Therefore, we can
simplify the analysis and enable a fast software implementation. But the
wireless mobile communication channels are often considered to be with
flat-fading Rayleigh noise. In this paper, we simulated both BSC and
flat-fading channel models and tested the performance of the proposed
techniques against both models. RS(255,q) is used in the proposed scheme. The
RS codes correct the symbol error and not the bit error. The noise in the
simulated channel has been considered in such a way to set a BER of 0.01 in the received end. For when the errors
are uniformly distributed, the average parity length is 42 for a 255 length
code length. This recovers the RoI perfectly
when either BSC or Rayleigh channel is considered and the channel noise is
equivalent to BER = 0.01.The developed
algorithm has been tested for a number of images, two of which are analyzed
here. The first image is a150×123 pixels color dental implant image, and the
second image is a 508×512 pixels monochrome X-ray bone image. Both are to be transmitted via TCP/IP. The proposed system follows the diagram in Figure 1. Each noisy channel involves a binary symmetric channel
(BSC) and flat-fading Rayleigh channel with a certain BER. In this simulation, the error bits are
generated by randomly inverting a
certain percentage of bits in the EZW bitstream. To verify the effectiveness of
the system, the image regions
are progressively transmitted through four stages of P1, P2, P3, and P4. During P1, the
background image is transmitted. P2 and P3 are the second and third stages, both for transmission of RoI, R1,
and R2. P4 is the fourth stage mainly to transmit the details of the RoI (and the rest of the image if necessary). In the
approach presented here, firstly, the user (physician) specifies the address of
the transmitted medical image in the transmitter within the dialogue-based
software. After receiving the low-resolution background image, the user
identifies the center of RoI by a
mouse and the radius of RoI by
entering a value within the dialogue-based software. Then, the algorithm adjusts the length of the parity
codes initially proportional to the proximity of the image regions to the
center of RoI as C0, C0−2, C0−4
for RoI, R1, and R2, respectively.Accordingly, the receiver detects and counts the packages in error by
estimating the status of the channel. The parity lengths remain the same if the
distortion in the reconstructed image is acceptable. Otherwise, the codes will
be adjusted automatically. Typically, the ratio of parity code to the overall
code length is larger for the clinically higher priority areas, that is, the
areas closer to the center of RoI as
stated in (6). Figure 5 indicates the ratios of parity length and the overall
codeword according to the experimental results.Table2 gives an example of the
percentage of information for the regions R1, R2, and R2 for fixed proximity levels of ra and rb, as in Figure 9. Table 3 indicates
the average compression ratios for various regions before the channel coding during the four successive transmission stages. The compression ratio is defined as the
ratio between the data volume of the coded data and the original data. However,
by changing one of RoI coordinates, or ra and rb, data in Tables 2 and 3 are also changed.Table 2
The information percentage transmitted for
each area during the stagesP1 to P4 images.
Percentage of areaP1P2P3P4RoI0%18.15%41.86%100%R10%37.04%44.07%0%R2100%44.81%14.07%0%Table 3
The average compression ratio for various regions for the
four stages of the progressive image transmission.
Compression ratiosP1P2P3P4Overall image3.0631.0690.7160.405RoI00.3530.4010.405R101.2321.8140R23.0633.7463.3870In Figure6, the parity lengths are found
by averaging the results of 10 trials under various noise levels. These are estimated by the algorithm developed for
detection of the blocks in error in the receiver. Data in RoI is the most important data in the overall image; therefore, the
length of parity codes is longer than that in R1 and R2. In the proposed system, the codeword length of RS codes is 255, and the number
of error bit is generated at random.Figure7 shows the frequency of the set
of parity lengths in 10 trials for when the channel noise is set by BER = 0.003
equivalent to the occurrence of 7 errors. As long as the error in the receiver
remains higher than a threshold th, the length of parity increases.
Consequently, if the error falls below a level tl<th the parity length increases. These threshold levels are empirically selected by
following the constraint in (8). In these cases, the parameters in (10) and
(11) are approximately α0=0.08, α=2×10−4, β0=5, β=5×103 and accordingly μ0=α0β0=0.4, μ1=αβ0=10−3, μ2=αβ=1, and
μ3=α0β=4×10−2.Figure 7
Average distribution (frequency) of the set
of parity lengths in 10 trials for BER = 0.003.Figure8 shows the PSNRs for successive transmission of four stages under various noise-level conditions. Figure 9(a) shows the background image sent during P1 stage. Figure 9(b) is progressively reconstructed image after stage P2 in which the RoI, R1, and R2 are reconstructed. At this stage, the center of RoI is
denoted by the user via mouse click. Figure 9(c) represents the
reconstructed image at stage P3 during which the regions RoI, R1, and R2 are
reconstructed. The regions of RoI and R1 are
gradually increased in resolution. Figure 9(d) is the final and
complete image after stage P4. The same procedure can be followed for encoding
and transmission of any other
medical image. However, the coordinates of the center of RoI as well as the size of RoI maybe adjusted according to the requirement by the user. For
example, in Figure 10, the RoI is
selected in the corner. Figure
11 demonstrates that a fixed-size parity code is not suitable for an efficient
transmission system. However, the system has been modified based on the
proposed method in Sections 2 and 4 to allow variable lengths of parity. Figures
12 and 13 show no error in the RoI stating
that the overall system has been remarkably improved. In Figure 14, another
example of a decoded image (a
508×512 monochrome X-ray bone image) is given, and
the variable length parity has been examined. The background image suffers from heavy noise. However, the transmission
can continue until the last stage during which a complete error-free image is
reconstructed.Figure 8
PSNR for successive transmission of four stages at
different BERs under various noise-level conditions.The transmitted image over the low noise:
(a) the background image at stageP1 and the location of RoI in the center of the image, (b) the transmitted image after stage P2, (c) the transmitted image after stage P3, and (d) shows the
completely decoded image after stage P4.
(a)(b)(c)(d)Similar results as in Figure9 when the RoI is selected
in the corner of the image.
(a)(b)(c)(d)An example of quality of the decoded image with fixed length parity: (a)
background image with the location of RoI in the center,
(b) the image reconstructed at stageP2; several subblocks in error in the area of RoI
and some error subblocks in the vicinity of RoI, (c) the
number of subblocks in error increases when the volume of
the data in the receiver increases, that is, the resolution of the
higher-priority regions increases, and (d) the complete transmitted image.
(a)(b)(c)(d)The decoded image with variable length of parity codes over the noisy channel.
(a) A background image and the location of RoI selected in the
center of image reconstructed after stageP1,
(b) the reconstructed image after stage P2, no
error subblocks are found in the reconstructed image because the lengths of
parity codes are adjusted automatically based on the previous
volume of incorrect data in the receiver, (c) the reconstructed image after
stage P3; the lengths of parity bits in stage 3
are as same as in stage 2 because no incorrect data was found in the
reconstructed image after stage 2, (d) the complete transmitted image with no
error subblocks.
(a)(b)(c)(d)A decoded image with variable length of parity codes
over noisy channel: (a) several error subblocks are detected in stage 1, (b)
several error subblocks still are found in stage 2 indicating that the feedback
message is incorrect or the channel condition becomes noisier. However, RoI
is still error-free based on the UEP. (c) No error subblock is detected in stage 3 because the length of
parity is adjusted again according to previous channel state, although there
can still be some error. (d) The complete transmitted image with no
error subblocks.
(a)(b)(c)(d)As another example, a decoded image with variable length parity, which is
a508×512 pixels monochrome X-ray bone image: (a) a background image and the
location of RoI selected in the center of image reconstructed
after stage P1; many subblocks are in error in
the background image, (b) the reconstructed image after stage P2, no error subblock is found in the reconstructed image. (c) The
reconstructed image after stage P3, and (d) the complete transmitted image having no error subblocks.
(a)(b)(c)(d)


## 6. Conclusion

In this paper, we presented a new
adaptive source-channel coding with feedback for the progressive transmission
of medical images. The system is adaptive to both image
content and channel specifications. However, this application is merely
for wireless channels (generally narrowband).  The capability  of data error detection and correction with automatic adjustment,
low image transmission time, and efficient
communication are the key features
in this proposed system. Therefore, the length of parity codes can be adjusted
automatically based on the location of the image subblocks and the practical characteristics of the
communication channel to provide an adequate data protection. The overall code length for the
channel encoder/decoder is fixed. This makes it easy for hardware
implementation. A wide range of fluctuations in the channel characteristics
(mainly noise level) can be tolerated in the system. The algorithm of detection
of subblock in errors can detect the packages in error in the receiver and feed
it back to the transmitter for adjustment of the parity length. The proposed
system has also been tested for the communication channels with different
capacities and noise levels. The
presented results verify the effectiveness of the system in terms of both
adaptivity and flexibility of interaction. A MATLAB-based TCP/IP
connection has been established to demonstrate the proposed interactive and
adaptive progressive transmission system. Although some theoretical results are
comparable to those of other new techniques such as the UEP or rate allocation
approaches, this contribution provides a practical, flexible, and interactive
method which suits medical applications.



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*Source: 519417-2009-01-26.xml*