MEDICAL IMAGE COMPRESSION USING REGION-OF-INTEREST VECTOR QUANTIZATION

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Nov 29, 2013 (3 years and 8 months ago)

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MEDICAL IMAGE COMPRE
SSION USING REGION
-
OF
-
INTEREST

VECTOR QUANTIZATION


András Czihó
*#$
, Guy Cazuguel
*#
, Basel Solaiman
*#
, Christian Roux
*#

* ENST
-
Bretagne, Dépt. ITI, B.P. 832 , 29285 Brest Cedex
-

France

# Laboratoire de Traitement de l'Information Médi
cale (LATIM), Brest
-

France

$Technical University of Budapest, Dept IIT, Budapest XI, Muegyetem rkp. 9, 1521
-

Hungary

E
-
mail: Andras.Cziho@enst
-
bretagne.fr


Abstract

In this paper an image compression approach is
proposed for medical applications. The a
lgorithm is based on
the Vector Quantization and adopts the idea of Region
-
Of
-
Interest. The image to be compressed is first segmented into
regions and a separate codebook is used for compressing
every region. The size and the number of codewords may be
dif
ferent in the codebooks according to the diagnostic
importance of the corresponding image region. This permits
to create appropriate codebooks with representative
codewords, and to obtain good reconstruction quality in
relevant zones, while reinforcing the

compression in less
important regions. The proposed approach is tested on
ultrasound esophagus images and is shown to be very
promising.


I. Introduction

Amongst lossy signal compression approaches the
Vector Quantization [1] is the optimal method in the
sense
that by increasing the vector length and the codebook size,
better performance can be obtained than using any other
block coding technique. Although the rapidly growing
memory and computation requirements do not permit
approximate arbitrarily closely

the optimal performance, VQ
has been proved to be a very straightforward image
compression approach[2]. For instance, the use of variable
size codewords was proposed according to the quadtree
decomposition of images in order to proceed with large
blocks w
henever it is possible [3,4,5].

VQ has the particular advantage of being able to
exploit prior knowledge on the images to be compressed.
Since a codebook has to be generated before compression,
one has to have a training set, i.e. several images that are
r
epresentative of the images to be compressed. Thus, VQ is
not a "universal" approach (as e.g. JPEG may be called) since
it cannot work well for images of types that differs from the
ones the training set was issued from. However, for images of
the same typ
e it can work very well (better than general
methods), since the codebook contains representative
codewords.

The use of digital images in medical field verifies the
above mentioned condition. That is, every images of the same
medical domain represents the
same thing: the same part of
the human body, the same organ, etc. Thus, a very suitable
codebook can be created and thus VQ can work very
efficiently. In [4,5] we have shown that a quadtree VQ
scheme works better than the JPEG compression standard in
medic
al environment, especially at high compression rates. In
the present paper we propose another VQ scheme that also
uses variable size codewords. However, in this case the
appropriate codebook to be applied is chosen according to
prior knowledge on the diagn
ostic importance of every
region. Thus, the proposed algorithm is a region
-
of
-
interest
(ROI) approach, and aims at exploiting as much information
known a priori as possible.

In the next section the standard VQ compression
algorithm is revisited, while Sect
ion III. presents the general
scheme of the proposed method referred to as ROI
-
VQ. The
concrete application we study is the echoendoscopy imaging
of the esophagus presented in Section IV. Simulation results
are reported in Section V., and the paper is clos
ed with the
conclusions.


II. Vector Quantization

In VQ, the image is divided into small non
-
overlapping blocks. E.g. 4x4 pixel blocks are considered as
vectors of dimension 16. At the encoder, each vector
x
i

of the
image is compared to the elements of a c
odebook
W={
w
0
,
w
1
,...,
w
N
-
1
}, called the codevectors or codewords,
and only the index of the nearest codevector is transmitted.
The best matching codevector is selected according to some
distortion measure, which is in general the mean square error.
The deco
der reconstructs the signal by simply performing
table
-
lookup operation to fetch codevectors from a codebook
which is identical to that of the encoder. The encoding and
decoding scheme is shown in Fig.1.

index
Original
image
block
...
Codebook
Codebook
...
Reconstructed
image
Best matching
search
Table
look-up

Fig. 1: VQ encoder
and decoder



The codebook must contain vectors that represent
well the images to be compressed. Several methods are used
in constructing codebooks. They apply, in general, a learning
method on the training set issued from available images
which are suppos
ed to be representative of the images to be
compressed.


III. ROI Vector Quantization

In medicine images are being used to a well
-
defined
task: the analysis of different parts of the human body.
Moreover, on a certain image different regions may have very
different diagnostic importance, since eventual pathologies
may happen only on several regions of the image
corresponding to well defined organs or parts of organ. For
example, on an X
-
ray hand image the expert is mainly
interested in the states of the bon
es. These bones occupy
finally a very restricted part of the image, therefore the rest, a
large image zone may be compressed with a low bitrate since
a poorer reconstruction quality is tolerable.

This idea and the VQ advantages lead to the
proposed Region
-
Of
-
Interest VQ (ROI
-
VQ) approach. In this
method, a separate codebook is generated for every region (or
image 'object', i.e. organ). The properties of these codebooks,
i.e. the codebook size and the block size, are chosen
according to the medical importanc
e of the given object. If an
object is diagnostically important, a large codebook
containing small codewords is created. Inversely, for less
important regions, the block size is smaller and/or the
codebook contains less codevectors. Fig. 2. illustrates the

ROI
-
VQ coding and decoding scheme.

Segmentation
Best matching
search
...
...
Region
description
Region codebooks
Region codebooks
index
Original
image
block
Segmentation
data
Table
look-up
...
Codebook
1.
Codebook
2.
Codebook
K.
Codebook
1.
Codebook
2.
Codebook
K.
...
Reconstructed
image
Fig. 2: ROI
-
VQ encoder and decoder


First of all, an image analysis is necessary in order to
determine the regions, to locate the objects. This step may
mainly consist of segmentation, obj
ect detection (contour
detection, classification), etc. Since a strong prior knowledge
is exploitable, specified and efficient segmentation techniques
can be developed for a given image type.

The results of the image analysis is a model, i.e. the
global st
ructure of the image. This description is needed to be
transmitted to the decoder, and it serves to guide the
compression. This data does not take in general a large space
although its importance is primary. According to the
segmentation information result
ing from the image analysis,
the encoder and the decoder always uses the appropriate
codebook. Thus, the image is compressed with a high quality
wherever it is required, and with a low bitrate whenever a
higher distortion is permitted.


IV. Esophagus image

compression by ROI
-
VQ

The proposed compression algorithm has been
adapted for echoendoscopic images of the esophagus wall.
The Endoscopic Ultrasonography is a very efficient tool in
the detection and study of various gastrointestinal cancers.
This imagin
g technique can also be used in the detection of
anastigmatic recurrence of tumors, and in the correct
evaluation of tumor response to chemotherapy or radiation
therapy.

The sonographic pattern of the oesophageal wall
consists of hypo and hyperechoic laye
rs and there is a good
correlation between the echolayers and the hystologic layers
of the digestive track wall. The anatomical structure of the
oesophageal wall is illustrated in Fig. 3. The actual
endosonographic acquisition system displays the ultrasoni
c
response of the tissues on a video monitor. These video
images are digitized and stored in a micro
-
computer. An
example of a 2D echo
-
endoscopic slice is shown in Fig. 4.


1st layer : hyperechogene
(Interface)
2nd : Hypoechogene
(Mukosa)
3rd : Hyperechogene
(Interface)
4th : Hypoechogene
(Muscle)
5th : Hyperechogene
(Interface)

Fig.3. The structure of the esophagus wall


Fig.4. An ultrasound esophagus image


Hence, the most important zone of these images is
the one corresponding to the esophagus wall, because the
diagnosis depends mainly on the content of this region. The
surrounding region belongs to several tissues of the human
breast, eventually the heart

or the aorta. This region can also
represent some pathologies, but a slight distortion is tolerable,
since only the main structure is important. However, the
central part of the image does not contain any relevant
information. This zone is black containin
g several bright
circles which do not correspond to any organ but are due to
the ultrasound acquisition technique.

All these considerations lead to divide the image to
be compressed into three parts representing the «

empty

», the
«

esophagus

» and the «

tissues

», respectively, as shown in
Fig. 5. In [6] there is described a contour detection algorithm
specified for esophagus images, which is able to detect the
internal contour of the esophagus wall. Thus, using this
algorithm the image segmentation is pe
rformed: the «

empty

»
is the zone surrounded by the detected contour, the
«

esophagus

» is a fixes width region surrounding the
«

empty

» and the «

tissues

» are the rest.


Fig.5. ROI
-
VQ compression scheme for esophagus images


As detailed in the previou
s section, the result of the
segmentation algorithm has to be transmitted to the decoder.
In the case of esophagus images this consist of transmitting
the detected contour, which allows the decoder to segment the
image. The contour can be described as the
two coordinates
of an arbitrary point (2x9 bits if the image size is inferior to
512), and 3 bits are sufficient to transmit every other adjacent
pixel. Since, a given point has only 8 possible neighboring
pixels, which can be coded on 3 bits. Experiments
showed
that the contribution of the contour description to the
compressed data amount is very modest, for the studied
images it is about 0.02
-
0.03 bpp.


V. Simulation results

Ten images were used to create the three separate
training sets corresponding to
the "empty", the "esophagus"
and the "tissues". After detecting the internal contour of the
esophagus wall, the three regions were determined and
divided into blocks in order to form the training sets. The
Kohonen neural learning method was applied to crea
te every
codebook [7].

For the "empty" region 64 codewords of 16x16 pixel
size is used resulting in a very high, 1:341.3 local
compression rate. Since the esophagus wall contains the most
diagnostically relevant information, it must be compressed
with a hi
gh fidelity. Therefore only 2x2 block size is applied,
and the codebook size is 256, leading to a 1:4 local
compression rate. In order to obtain a stronger compression,
4x4 block size is proposed for the "tissues". However, the
codebook is also larger, con
taining 1024 codevector, in order
to avoid a visually annoying reconstruction quality in this
region. Thus, the "tissues" are compressed with a 1:12.8
compression rate.

The method was tested on several images lying
outside the training set. The results pre
sented in the following
concerns the original image shown in Fig.4. Fig. 6 shows the
resulting compressed image. The final compression rate is
about 1:11. Clearly, the visual image quality is very good on
the esophagus wall du to the small applied blocks.

Furthermore, the center part of the image is quasi
-
perfectly
reconstructed, which might appear surprising because of the
large (16x16) codeword size. However, since this zone is
almost the same on every image, the generated "empty"
codebook contains per
fect patterns. On the "tissues" we can
remark a slight blocking artifact, but this distortion is still
tolerable (and also can be reduced using some post
-
processing method).

For comparison purposes we report the JPEG image
on Fig. 7. compressed approximate
ly with the same
compression rate. The Peak Signal to Noise Ratio (PSNR)
values calculated both for the whole images and for the three
regions are detailed on Table I. (The PSNR is defined as

PSNR
MSE







10
255
10
2
log

where MSE is the mean square error betwee
n the original and
the restored image.)


As shown, the objective reconstruction quality
measure is slightly higher when using ROI
-
VQ than with
JPEG. Furthermore, it is much more important that the
critical zone, i.e. the esophagus wall is compressed with a

much higher fidelity: ROI
-
VQ results in an improvement of
about +2 dB comparing to JPEG.


VI. Conclusions

We have presented an image compression method,
which deeply exploits the advantages of VQ in medical
application environment. Based on prior knowledg
e and
applying the ROI aspect, our approach compresses
diagnostically important regions with a very good
reconstruction quality. Moreover, a rather high overall
compression rate is obtained due to the strong compression in
less important image zones. Sinc
e separate VQ codebooks are
created to compress different objects, every codebook is well
adapted to the given region.

Simulation results provided by compressing the
studied ultrasound images of the esophagus validated the
interests of the proposed approa
ch. Not only a good
rate/distortion performance is obtained, but the quality is
preserved on the most important part, i.e. on the esophagus
wall. Furthermore, the use of separate codebooks permitted to
apply such a high block size as 16x16 resulting nevert
heless
in a very good quality.


References

[1]

A.Gersho, R.M.Gray, "Vector quantization and signal
compression", Klower Academic Publisher, Boston, 1992

[2]

N.M. Nasrabadi and R.A. King, "Image coding using
vector quantization: a review", IEEE Trans.Com.,
Vol.36, pp.
957
-
971, Aug. 1988.

[3] J.Vaisey, A. Gersho, " Image Compression with variable
Block Size Segmentation", IEEE Transactions on Signal
Processing, Voll.40, No 8, August 1992

[4]

G.Cazuguel, A.Cziho, B.Solaiman, C.Roux,
M.Robaszkiewicz, “Improvin
g Spatial Vector Quantization
by use of a Quadtree Scheme. Application to Echoendoscopic
Image Compression”, Annual International Conference Of
The Engineering In Medicine And Biology Society, pp. 894
-
897, Chicago, USA, 1997

[5]

G.Cazuguel, A.Czihó, B.Sola
iman, C.Roux, “Medical
image compression and analysis using Vector Quantization,
the Self
-
Organizing Map, and the quadtree decomposition”
Conference on Information Technology Applications in
Biomedicine, Washington, USA, May 1998.

[6]

F.Pipelier, B.Solaima
n, S.Grassin, C.Roux, “A new
dynamic contour model: application on ultrasound images”,
IEEE Engineering in Medicine and Biology Society, p. 167,
1996.






Fig.6. ROI
-
VQ compressed image

Fig.7. JPEG compressed image


Compression rate: 1 : 10.90 ; PSNR: 31.71 dB



Compression rate: 1 : 10.77 ; PSNR: 31.60 dB


PSNR values [dB]

ROI
-
VQ

JPEG

“empty”

40.78

34.00

“esophagus”

31.58

29.01

“tissues”

31.14

31.76

overall

31.71

31.60

Tabl
e I. Comparison of ROI
-
VQ and JPEG