Performance Analysis of Medical Image Compression

8/3/2019 Performance Analysis of Medical Image Compression

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Performance Analysis of Medical Image Compression

K.Vidhya

Research Scholar

Electronics & Communication Engineering

College of Engineering, Anna University

Guindy, Chennai, Tamilnadu

e-mail: [email protected]

Dr.S.Shenbagadevi

Assistant Professor

Electronics & Communication Engineering

College of Engineering, Anna University

Guindy, Chennai, Tamilnadu

e-mail: [email protected]

Abstract — The paper deals with evaluation of medical images

by objective quality measures. Two main approaches to assess

image quality are objective testing and subjective testing.

Objective measures correlate well with the perceived image

quality for the proposed compression algorithm. This paper

presents an effective algorithm to compress and to reconstructDICOM (Digital Imaging and COmmunications in Medicine)

images. DICOM is a standard for handling, storing, printing

and transmitting information in medical imaging. These

medical images are volumetric consisting of a series of

sequences of slices through a given part of the body. DICOM

series of images are decomposed using Cohen-Daubechies-

Feauveau (CDF) biorthoganal wavelet. The wavelet coefficients

are encoded using Set Partitioning In Hierarchical Trees

(SPIHT). Consistent quality images are generated by this

method at a lower bit rate compared to JPEG and Fractal

compression algorithms. The image quality is evaluated by

various objective quality measures.

Keywords- Medical imaging, Set Partitioning In Hierarchical

Trees (SPIHT), Cohen-Daubechies-Feauveau biorthoganal wavelet (CDF), Digital Imaging and COmmunications in

Medicine (DICOM), DICOM previewer, Objective Quality

Measures.

I. I NTRODUCTION

This paper will propose an approach to improve the

performance of medical image compression. JPEG and

Wavelet compression methods are most popular methods

preferred by the medical community.The well-known JPEG

compression standard described in [1].In this lossy method,

the image is divided into sub-images. Discrete Cosine

Transform (DCT) is performed on the sub-images, and the

resulting coefficients are quantized and coded.Wavelet compression has been developed by many

authors [2, 3]. In [4] a network algorithm to compress and to

reconstruct DICOM images is presented. Said and A.

Pearlman developed a SPIHT coding algorithm [5], a

refined version of embedded zero tree wavelet coder (EZW)

coder [6]. In Fractal Image Compression technique [7, 8]

possible self-similarity within the image is identified and

used to reduce the amount of data required to reproduce the

image. But these methods have been time consuming.

Images can be compressed using lossy or lossless

compression techniques. Lossless compression involves

with compressing data which, when decompressed, will be

an exact replica of the original data without any loss.

Typical compression rates for lossless techniques are around

2:1 to 4:1. Lossy techniques do not allow for exact recovery

of the original image once it has been compressed. But these

techniques allow for compression rates that can exceed

100:1 depending on the compress quality level and the

image content, sacrificing some of the finer details in the

image for the sake of saving a little more bandwidth and

storage space.

Current DICOM standard is based on JPEG image

compression. In this paper wavelet coding has been proved

to be a very effective technique for DICOM images giving

significantly better results than the JPEG algorithm. A set of

DICOM files containing full series of transverse MR images

are used for experiments. The Discrete Wavelet Transform

of the image is calculated with CDF biorthoganal filter. Thewavelet coefficients are encoded using SPIHT. This method

yields better compression than other standard methods. The

experimental results are compared with standard JPEG

algorithm and fractal compression. A major design goal of

any compression method is to obtain the visual quality with

lowest bit rate. However, the quality and the bit rate are the

trade-offs that must be considered simultaneously.

Some of the objective picture quality measures [9] apart

from the common measures Mean Squared Error (MSE) and

Peak Signal to Noise Ratio (PSNR) are Normalized Cross-

Correlation (NK), Average Difference (AD) and Structural

Content (SC).

The rest of the paper is organized as follows. Section II presents an overview of medical image compression.

Section III discusses the DICOM images used for

compression. Section IV gives an overview of wavelets.

Section V explains the SPIHT coder. Section VI presents

the proposed coding method for a set of DICOM files

containing full series transverse MR images. Section VI

shows experimental results of the proposed work compared

with JPEG algorithm and fractal compression algorithm.

Section VIII gives some concluding remarks.

2009 International Conference on Signal Processing Systems

978-0-7695-3654-5/09 $25.00 © 2009 IEEE

DOI 10.1109/ICSPS.2009.183

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II. MEDICAL IMAGE COMPRESSION

The compression of medical images has a great demand.

The image for compression can be a single image or

sequence of images. The medical community has been very

reluctant to adopt lossy algorithms in clinical practice.

However, the diagnostic data produced by hospitals has

geometrically increased and a compression technique isneeded that results with greater data reductions and hence

transmission speed. In these cases, a lossy compression

method that preserves the diagnostic information is needed.

In medical image sequences called Volumetric Medical

Image (VMI) or 3-D medical data needs efficient image

compression solving storage and transmission problems and

also preserving diagnostic information. Visually

indistinguishable resultant images at high quality can be

obtained using lossy compression techniques, for

compression rates much greater than those obtained by

lossless compression techniques. That is, the human eye

cannot detect a difference between the original image and

the compressed then decompressed image with the lossy

compression method.

III. DICOM IMAGES

DICOM differs from other data formats in that it groupsinformation together into a data set. A DICOM data objectconsists of a number of attributes, containing such items asname, ID, etc, and also one special attribute containing theimage pixel data. The previewer gives a list of files from aselected directory. Header and image data of the selected fileare displayed immediately.

IV. WAVELETS

Algorithms based on wavelets have been shown to work

well in image compression. Separating the smoothvariations and details of the image can be done by

decomposition of the image using a Discrete Wavelet

Transform (DWT). The symmetric extension details were

being perfected for biorthoganal wavelets especially for low

frequency images. Extensive research has shown that the

images obtained with wavelet-based methods yield good

visual quality. At first it was shown that even simple coding

methods produced good results when combined with

wavelets. SPIHT employs more sophisticated coding. In

fact, SPIHT exploits the properties of the wavelet-

transformed images to increase its efficiency.

CDF biorthoganal wavelet preferred to perform better

compared to other wavelets for the compression of DICOM

images. This wavelet, which is used in JPEG 2000

compression, is used along with SPIHT to provide high

compression ratio and also good resolution. The perceived

image quality is significantly improved using CDF wavelet.

V. SPIHT CODER

SPIHT technique is based on a wavelet transform and

differs from conventional wavelet compression only in how

it encodes the wavelet coefficients. SPIHT is based on three

concepts (1) exploitation of the hierarchical structure of the

wavelet transform by using tree-based organization of the

coefficients, (2) partial ordering of the transformed

coefficients by magnitude, (3) ordered bit plane

transmission of refinement bits for the coefficient values.

This leads to a compressed bit stream in which the most

important coefficients are transmitted first. The values of all

coefficients are progressive refined and the relationship

between coefficients representing the same location at

different scales is fully exploited for compression

efficiency. The SPIHT algorithm appears to give extremely

good performance in DICOM image compression. The fully

embedded nature of the output bit stream also makes an

excellent choice for progressive transmission. The interband

spatial dependencies are captured in the form of parent-child

relationships. The arrows in Fig.1 point from the parent

node to its four children. With the exception of the coarsest

subband and the finest subbands each wavelet coefficient at

the i-th level of composition is spatially correlated to 4 child

coefficients at level i-1 in the form of 2x2 blocks of adjacent pixels. These 4 child coefficients are at the same relative

location in the subband decomposition structure. This

relationship is utilized during SPIHT quantization. If a

parent coefficient is insignificant with respect to a particular

threshold then all of its children would most likely be

insignificant and similarly significant coefficients in the

finer subband most likely correspond to a significant parent

in the coarser subband. This results in the significant

savings: only the parent’s position information needs to be

coded since the children’s coordinate scan be inferred from

the parent’s position information.

VI.

CODING OF DICOM IMAGES The previewer gives a list of files from a selected

directory. Header and image data of the selected file are

displayed immediately. These medical images are

volumetric consisting of a series of sequences of slices

through a given part of the body. To maintain uniform

quality for all sequences of slices a single slice is encoded

and compressed bitstream is sent to the decoder. After the

encoder and decoder finish all the slices in a sequence, it

shifts to process the next sequence of slices. The block

diagram of proposed method is shown in Fig. 1.

DICOM images are first decomposed using generalized

CDF wavelet filter and the wavelet coefficients are encoded

using SPIHT. The algorithm starts at the coarsest sub band in the sub band pyramid. SPIHT captures the current bit-

plane information of all the DWT coefficients and organizes

them into three subsets: (1) List of Significant Pixels (LSP),

(2) List of Insignificant Pixels (LIP) and (3) List of

Insignificant Sets of Pixels (LIS)). LSP constitutes the

coordinates of all coefficients that are significant. LIS

contains the roots of insignificant sets of coefficient.

Finally, LIP contains a list of all coefficients that do not

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belong to either LIS or LSP and are insignificant. During

the encoding process these subsets are examined and labeled

significant if any of its coefficients has a magnitude larger

than a given threshold. The significance map encoding (set

partitioning and ordering pass) is followed by a refinement

pass, in which the representation of significant coefficients

is refined. These thresholds used to test significance are powers of two, so in its essence, the SPIHT algorithm sends

the binary representation of the integer value of wavelet

coefficients.

Compressed

image

Reconstructed Compressed

Image image

Figure 1. Block diagram of Coding method.

VII. EXPERIMENTAL R ESULTS

DICOM series of images of resolution 256 pixels by 256

pixels and 512 pixels by 512 pixels are used for

experiments. SPIHT have better visual quality than JPEG

compression and Fractal image compression. From the

selected directory the files will be listed by the previewer.

From sequences of slices a single slice is first decomposed

using generalized CDF wavelet filter and the wavelet

coefficients are encoded using SPIHT coder and compressed

bitstream is sent to the decoder. After the encoder and

decoder finish all the slices in a sequence, it shifts to processthe next sequence of slices. The original and reconstructed

images with bit rate of 0.7 bpp and decomposition level of

4, processed by this algorithm is shown in Fig. 2. The

reconstructed images at a low bit rate show good quality

without distortion. The encoding and decoding time

increases as bit rate increases. The effectiveness of the

algorithm described above can be statistically modeled and

evaluated. Many number of DICOM images collected from

hospitals is used as material for this statistical survey. The

aim of this survey is to compare the objective quality

measures of the proposed method with standard JPEG

compression and Fractal image compression algorithms.

The performance of the proposed method for DICOM

images is much better than JPEG and Fractal techniques. This algorithm shows good performance as good as other algorithms at a lower bit rate. The peak signal to noise ratio

(PSNR) is defined by

PSNR = 10 log10

(255 2 / MSE) dB

The rate vs. PSNR results is excellent. At a bit rate of 0.5

bpp the compressed images exhibit better subjective quality

with PSNR exceeding 35 dB.

The Normalized Cross-Correlation is defined by

NK = ∑ ∑x (i, j) x ٨ (i, j) / ∑ ∑ x (i, j) 2

i j i j

The Average difference is defined as

AD = ∑ ∑ (x (i, j) - x ٨ (i, j)) / MN

i j

The Structural Content is evaluated by

SC = ∑ ∑ x (i, j) 2 / ∑ ∑ x ٨ (i, j) 2

i j i j

The objective quality measures correlate with the perceived

image quality and compared across different compression

algorithms. The numerical results are shown in Table I.

TABLE I. NUMERICAL R ESULTS OF SLICE 1

CompressionResults

Proposed JPEG Fractal

PSNR (dB)

38.71 24.45 28.32

CR 45.7295

47.65 162

Encoding time(sec)

2.64 2.5982 112.93

Decoding time(sec)

0.94 5.83 0.44

NK 73.2983

47.65 145

AD -0.6114 5.93 -0.0343

SC 1.0001

2.2495 1.0075

VIII. CONCLUSION

A wavelet based compression with set partitioning in

hierarchical trees appears to give extremely good

performance in DICOM series of images for medical image

compression. The proposed algorithm outperforms the

standard JPEG compression and fractal compression

DWT SPIHT coder

SPIHT

decoder

IDWT

DICOM

previewer

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algorithms in terms of both objective and subjective

measure. The subjective measure is based on the visual

inspection of the compressed images and the evaluations are

carried out among different images at various bit rates and

decomposition levels. Future work will include using the

methodology to preprocess the image for producing

significant improvement in compression efficiency at lower bit rates and also quality evaluation of various medical

volumetric datasets.

ORIGINAL RECONSTRUCTED ORIGINAL RECONSTRUCTED

IMAGE IMAGE IMAGE IMAGE

Figure 2. Visual performance of the slices taken from MRI data set at 0.7 bpp.

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R EFERENCES

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Trans. Med. Imag.vol 22, pp.441-458, Mar.2003.

[3] A.R.Golderbank, Ingrid Daubechies, Wim Sweldens and Boon-Lock Yeo, “Lossless image compression using integer to integer wavelet

transforms,” IEEE Trans, 1997.[4] S.Hludov, Chr.Meinel, “DICOM - image compression,” IEEE, 1999.

[5] Said A.Pearlman WA,”A new fast and efficient image codec based

on set partitioning in hierarchical trees,” IEEE Trans. Circuits Syst.

Video Technology, vol.6, pp. 243-250, June 1996.[6] Shapiro JM,”Embedded image coding using zero trees of wavelet

coefficients,” IEEE Trans Signal Processing. 41:3445-3462, 1993.

[7] Y.Fisher, “Fractal Image compression: Theory and Application”,

Springer Verlag, N.Y., 1995.[8]Barnsley.M. “Fractals Everywhere,” Academic press. San Diego,

1989.[9] Marta Mrak, Sonja Grgic and Mislav Grgic, “ Picture Quality

Measures in Image Compression Systems,” EUROCON 2003, Slovenia.

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Performance Analysis of Medical Image Compression

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