AN IMPROVED CODING TECHNIQUE FOR ...

M. Moorthi,R. Amutha, IntJScRT, 2013v1i2, 1-9

AN IMPROVED CODING TECHNIQUE FOR COMPRESSION OF MEDICAL IMAGES IN TELEMEDICINE

1

R. Amutha2

M. Moorthi1 Research Scholar, Department of Electronics `2

and Communication Engineering, Sri

Professor, Department of Electronics and Communication Engineering

Chandrasekharendra Saraswathi Viswa Mahavidyalaya

SSN College of Engineering, Chennai, India

University, Kanchipuram, India.

E-mail: [email protected] E-mail: [email protected],

Abstract The proposed work promotes a new image compression technique for the coding of medical images. This compression technique invokes that Contourlet Transform is a directional transform and also it is capable of capturing contours and fine details in medical images. Initially wiener filtering and the Contourlet transform are applied to the medical images. The adaptive multi-stage vector quantization (AMSVQ) is applied to the corresponding sub bands of Contourlet coefficients. The AMSVQ is implemented in which the search time and codebook complexity reduction is obtained. The numbers of code vectors are calculated in the adaptive vector quantization scheme depending on the dynamic range of the input image. The proposed method experimentally proves that it achieves better performance in representing edges than wavelets because of its anisotropy and directionality; hence it is well suited for multiscale image representation. Better results are achieved by the proposed method in terms of various factors like compression ratio (CR), peak signal to noise ratio (PSNR), compression and decompression time period for different medical images.

Keywords Compression, Decompression, Contourlet transform, PSNR

communication systems (PACS)[1]. Most of the

I. Introduction

modern medical data is represented as images or

Digital images are very important in many

other types of digital signals, such as Ultrasound,

application areas such as internet browsing, medical

MRI, computer Tomography (CT), Positron Emission

sciences, astronomy and remote sensing. Once

Tomography (PET) [2],[3]. Several compression

personal computers gained the capacity to display

algorithms, such as the JPEG standard [4] for still

sophisticated pictures as digital images, people

images and the MPEG standard [5] for video images

started to seek methods for efficient representation of

are based on DCT [6]. However, the EZW [7], the

these digital pictures in order to simplify their

SPIHT [8], the SPECK [9], the EBCOT [10]

transmission and save disk space. Image compression

algorithms and the current JPEG 2000 [11] standard

has a vital role for medical picture archiving and

are based on the discrete wavelet transform (DWT)

IJSRT | MAY - JUNE 2013 Available [email protected]

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M. Moorthi,R. Amutha, IntJScRT, 2013v1i2, 1-9

[12]–[14]. N.M .S.Rahim et al [15] proposed a

II.

Proposed method

method for Image compression using an improved

The proposed method is using contourlet transform

feature map finite vector quantization in 2002. A

and adaptive multistage vector quantization followed

model using a modified vector quantization for

by Huffman coder .it is shown in Figure 1.

compressing a medical image was designed by Jayantakumar et al [16] in 2008.DWT is capable of

Input Image

solving the blocking effect introduced by DCT; the correlation between the neighbouring pixels is also

Pre processing

reduced. DWT gives multi scale sparse representation Contourlet Transform

of the image.

One of the properties of Contourlet is to preserve

Adaptive Multistage Vector Quantization

edges and fine details in the image. In the proposed scheme the encoding complexity is less when compared to tree structured quantization. Wavelet is

Encoding

Compressed Image

a useful denoising tool for its properties of sparsity, locality and multiresolution. However, commonly used

separable

wavelet transforms

Noise removal and Decoder

which are

constructed from tensor products of one-dimensional filter banks. Together with curvelet, wedgelet, etc., Contourlet is considered to be the new generation of

Inverse Adaptive Multistage Vector Quantization

Inverse Contourlet Transform

wavelet in two and higher dimensions.

The Contourlet transform enables the representation

High Quality Decompressed Image

of images with a large degree of sparsity. For most images, a large fraction of image energy is captured by very few contourlet coefficients. Capitalizing on this property, contourlet transform can be applied in a wide range of image processing tasks, such as denoising,

texture

classification

and

fusion

.Furthermore, it has been applied in the field of synthetic aperture radar (SAR) image processing as well as medical image processing

and shows its

potentials. The size of the captured data is becoming even larger, and causes considerable problems especially in the fields of teleradiology and telemedicine. IJSRT | MAY - JUNE 2013 Available [email protected]

Figure 1.Block diagram of proposed method The contourlet transform of the input medical image is taken to minimize the correlation present in the input image. Different pyramidal and directional filters are used for decomposition. Both the transform coefficients and the residual coefficients are vector quantized. One main difference is that the transform coefficients are adaptively vector quantized in a multistage manner, while the residual coefficients are just vector quantized. The quantized coefficients are lossless coded using static Huffman code. In

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M. Moorthi,R. Amutha, IntJScRT, 2013v1i2, 1-9

decoding; the decoder basically performs the reverse

The laplacian pyramid in contourlet filter bank uses

process of the above steps.

orthogonal filters and down sampling by 2 in each

A. Noise Removal

dimension M=diag (2, 2).

Pre-processing should be performed in order to make

The laplacian pyramid in contourlet filter bank uses

the image noise free as the first step. Adaptive wiener

orthogonal filters and down sampling by 2 in each

filtering technique is used for noise removal.

dimension

B. The Contourlet transform

.

The scaling function is given as (1)

The traditional image enhancement methods losing detail geometric information of images and tending to amplify noise.

The continuous function is given as (2)

Contourlet transform and SVD

transform are used to overcome the problems of

The shift invariant subspace denoted by (3)

existing method. The CT expansion is composed of basis images oriented at various directions in multiple

Let

scales, with flexible aspect ratios. Existing image

f with the scaling function

enhancement methods cannot confine the directional

be the input image, the output after the LP state are J

edge information of the image.

band

The Contourlet Transform is a directional transform [5,6]. Contourlet filter bank is doubled iterated filter

the inner product function

pass

image

at a scale L. Let

images

and

the

low

pass

. (i.e )Image is decomposed in to the

coarser image

and detail image

by LP

bank structure which decomposes image in to

(4)

directional sub bands at multiple scales. Here first

(5)

decompose the image by laplacian pyramid (multi scale filter bank), then resulting band passed

Each band pass image

frequency are passed through directional filter bank

by an Lj level DFB in to

(DFB). The flow graph of the contourlet transform is

images

is further decomposed band pass directional

.

shown in Figure 2a.

is decomposed in to coefficients by Discrete Contourlet Transform (n)=

(6) =

(7) -1

Directional (synthesis) filter is represented by (8) DFB is implemented via an L level binary tree decomposition that leads to

subbands with wedge

shaped frequency portioning .The overall sampling Figure 2a: The contourlet transform Block diagram IJSRT | MAY - JUNE 2013 Available [email protected]

matrices is given as

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M. Moorthi,R. Amutha, IntJScRT, 2013v1i2, 1-9

(14)

(9)

This transforms simultaneously reducing image noise The basis function resembles a local Radon transform

and retained Strong edge information corresponding

are called Radonlets

to relevant features.

implemented

via

. DFB is

an

L

level

binary

tree

l

C. Adaptive Vector Quantizer

decomposition that leads to 2 sub bands with wedge

Vector Quantizer transforms the vectors of data into

shaped frequency portioning as illustrated below in

indexes that represents clusters of vectors, which

Figure 2b.

improves the image quality by average training vectors and then splits the average result to Codebook that has minimum distortion. The structure of encoder consists of a cascade of VQ stages and Huffman coder as shown in Figure 3.

Figure 2b: Resulting frequency division. The output of the Lj levels DFB given an image can be written as = It

has

(10) support

length

of

size

width

and

and the parabolic scaling relation

for curves width x length2. When the DFB is applied to the approximation subspaces Vj

(11)

(12)

When the DFB is applied to the detail subspaces W j,

Figure 3: Block diagram of encoder The input vector ‘A’ is quantized with the first stage

then the contourlet transform is given by (13)

codebook producing the first stage code vector VQ0

The index j ,k ,n specify the scale ,direction, location

(A), residual vector y0 is formed by subtracting VQ 0

respectively.

(A) from ‘A’. The second stage codebook is used to

Lj

represent

number

of

DFB

decomposition levels l at different scales j.

quantize y0, with exactly the first stage procedure,

Where

but ‘y0’ is used instead of ‘A’ as the input for

,

quantizing. Thus, a residual vector is generated, in each stage except the last stage and passed to the next IJSRT | MAY - JUNE 2013 Available [email protected]

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M. Moorthi,R. Amutha, IntJScRT, 2013v1i2, 1-9

stage for independent quantization of the other

the last stage. The encoding complexity to the storage

stages. The quantization algorithm is explained as

complexity is rendered by sequential searching of the

follows

stage codebooks

1. First set M=1 and find centroid of vector by eq. no.

D. Entropy coder

15 as follows

The entropy encoding uses a model that produces a

Let A=

suitable code based on the probabilities for each (15)

quantized value, so that the final output code stream

k=1,2,3,…,16 , n =1,2,3,…,16384,j = 1,2,3,…,n; n =

will be of smaller size than the input stream. The

number of the image parts after that use Linde-Buzo-

frequency of occurrence of the data item is the basic

Gray’s (LBG) algorithm .

phenomenon of Huffman coding. The method is to

2. Compare between M and Nc, when Nc is the

use a minimum number of encoding bits to encode

number of vector in codebook. If M equal Nc then we

the frequently occurring data. A code book is

keep Ym and complete the algorithm but if M not

constructed for every image or a group of images to

equal Nc .we will update the new codebook, the

store the codes for each image. For the purpose of

average value

decoding, the code book along with the encoded data

is calculate by eq. no. 16

must be transmitted. (16) III. Quality Measures where (17)

The Quality of the reconstructed image is measured in terms of mean square error (MSE) and peak signal

Continue the loop until M=Nc. Finally quantized coefficients are coded by Huffman coder. The decoder is shown in Figure 4,

to noise ratio (PSNR) ratio [25]. The MSE is often called reconstruction error variance q2. The MSE between the original image X and the reconstructed image Y at decoder is defined as:

q2=MSE=

(18)

Where the sum over i,j denotes the sum over all pixels in the image and M*N is the number of pixels in each image. Xij -original image, Yij-Reconstructed image. The peak signal-to-noise ratio is defined as the ratio between signal variance and reconstruction error variance. The PSNR in terms of decibels (dBs) is given by: Figure 4: Block diagram of decoder It receives for each stage an index identifying the stage code vector selected and forms the reproduction A by summing the identified vectors. The total quantization error is the quantization residual from IJSRT | MAY - JUNE 2013 Available [email protected]

PSNR=10

(

)

(19)

Generally when PSNR is 40 dB or greater, then the original and the reconstructed images are virtually indistinguishable by human eyes.

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M. Moorthi,R. Amutha, IntJScRT, 2013v1i2, 1-9

The compression ratio is calculated as Compression ratio =

input data size Output data size

(20)

The term ‘compression ratio’ is used to characterize the compression capability of the system. There are two time factors in the compression algorithm which are important. The algorithm having less compression and decompression times is effective with respective to the time factor. IV. Results and Conclusion The performance of the proposed method and the existing method are tested for the Peak Signal to Noise Ratio (PSNR) and Time period. The values

Fig 5a) comparison of PSNR value with paper [15]

have been analysed on 256 x 256 bit size of different medical images and the results are given in Table 1 and Table 2. The new medical image compression scheme proved that preserve the Meta information like

edges

using

successive

approximation

quantization of the image and achieved the PSNR around 63 db. Table 1.The compression and decompression time for different medical images Compression Medical images

Time(sec)

1.Eye image

Decompression Time(sec)

0.97

0.07

2.MRI brain image

1

0.07

3.MRI knee image

0.99

0.072

4.MRI heart image

0.98

0.072

5.MRI spinal image

0.992

0.072

6.CT brain image

0.967

0.082

Fig 5b) comparison of PSNR value with paper [16] Figure 5a, 5b shows the comparison of PSNR value

Table 1 show the time period, which varies greatly

with reference papers [15] and [16]. The PSNR

between the proposed and existing method. The

obtained using contourlet transform is better than that

proposed method takes only half of the time for

of the wavelet transform. Also the compression and

compression and decompression as compared with

decompression time for the proposed method are

the existing method.

minimum. Comparison with other existing schemes

IJSRT | MAY - JUNE 2013 Available [email protected]

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M. Moorthi,R. Amutha, IntJScRT, 2013v1i2, 1-9

showed

that

the

proposed

scheme

achieves

competitive image quality in terms of PSNR. The

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M.E - Medical Electronics in the year 2007 at Anna University, Gundy campus, Chennai, India. He has 12 years of teaching experience and he is currently working as Assistant Professor in the department of Electronics and Communication Engineering at Prathyusha Institute of Technology and management, Chennai. He is a member of the Institute of Electrical and Electronics Engineers (IEEE), Indian Society for Technical Education (ISTE), IETE. He has published and presented papers in National and International Conference in the area of Image processing. He has been the reviewer for 2012 & 2013 IET image processing. His research interests are Image Segmentation, Image Compression, Neural network, Fuzzy logic, microprocessor and microcontroller.

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Dr.R.Amutha, Professor, ECE department graduated from Thiagarajar college of Engineering in the year 1987. She obtained her M.E degree from PSG college of Technology. She got her Ph.D from Anna University in 2006. She has 24 years of teaching and 10 years research experience. Her research area includes coding theory, Wireless communication network and Image processing. She published 3 International and 2 national journal papers. She has 20 International and national conference papers to her credit. She reviewed three international journal papers. She is a recognized research supervisor of Anna University and SCSVMV University for Ph.D and M.S (by research). She is supervising 9 Ph.D research scholars.

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Informatin

Fusion:

Architectures, Algorithms, and Applications. Bellingham:SPIE, 6242-6245(2006). AUTHOUR

M.Moorthi pursuing his Ph.D program at Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya University, Kanchipuram. He completed his B.E degree at Arulmigu Meenakshi Amman College of Engineering, Kanchipuram, in Electronics and Communication Engineering in the year 2001 and IJSRT | MAY - JUNE 2013 Available [email protected]

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