Development of Medical Image Compression Techniques

Dr.S.Shenbaga Devi K.Vidhya Assistant Professor Research Scholar College of Engineering College of Engineering Anna University Anna University Guindy, Chennai Guindy, Chennai E-mail: kvidhyasri@gmail.com

Abstract Progressive transmission of medical images through Internet has emerged as a promising protocol for teleradiology applications .The major issue that arises in teleradiology is the difficulty of transmitting large volume of medical data with relatively low bandwidth. Recent image compression techniques have increased the viability by reducing the bandwidth requirement and cost- effective delivery of medical images for primary diagnosis. In this paper, we have presented an effective algorithm to compress and to reconstruct Digital Imaging and Communications in Medicine (DICOM) images. DICOM is a standard for handling, storing, printing and transmitting information in medical imaging. The algorithm consists of two stages: DICOM images are first decomposed using generalized Cohen-Daubechies-Feauveau biorthoganal wavelet and the wavelet coefficients are encoded using Set Partitioning In Hierarchical Trees (SPIHT). This method provides a way to generate consistent quality images at a lower bit rate compared to JPEG. In this method, Cohen-Daubechies-Feauveau biorthoganal wavelet is used along with SPIHT to provide high compression ratio and also good resolution. Key words: medical image compression, Set Partitioning in Hierarchical Trees (SPIHT), Cohen-Daubechies-Feauveau biorthoganal wavelet (CDF), Digital Imaging and Communications in Medicine (DICOM). 1. Introduction

Recently the amount of medical diagnostic data produced by hospitals has increased

exponentially. Furthermore, for telemedicine applications, transmitting a large amount of digital data through a bandwidth-limited channel becomes a heavy burden. The storage and transmission problems can be solved by digital compression techniques.

The principles behind wavelet compression have been well developed by many authors [1,2] The well-known JPEG compression standard described in [3]. Current DICOM standard is based on JPEG image compression. The quality control scheme in [4] is operated on the pure set partitioning in hierarchical trees (SPIHT) algorithm. In [5] a network algorithm to compress and to reconstruct DICOM images is presented.

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. The Discrete Wavelet Transform (DWT) of the image is calculated with generalized CDF biorthoganal filter. The wavelet coefficients are encoded using SPIHT. This method yields better compression than other standard methods. We report the result of experiments comparing such coding to more conventional JPEG compression. In general, a major design goal of any compression method is to

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obtain the best visual quality with the lowest bit rate. However, the quality and the bit rate are the tradeoffs that must be considered simultaneously.

The rest of paper is organized as follows. Section 2 presents a coding method for DICOM images. Section 3 shows experimental results comparing our method with the previous work. Finally conclusions are given in Section 4. 2. Coding of DICOM images 2.1. DICOM images

DICOM differs from other data formats in that it groups information together into a data set. A DICOM data object consists of a number of an attributes, containing such items as name, ID, etc, and also one special attribute containing the image pixel data.

2.2. Wavelets

Algorithms based on wavelets have been shown to work well in image compression. 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 [6] 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 especially in the textured regions of the images.

2.3. SPIHT coder The SPIHT coding algorithm was proposed by Said and Pearlman [7], presented as a refined version of embedded zero tree wavelet coder (EZW) coder [8]. 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 is illustrated in the Fig.1. 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 decomposition is spatially correlated to 4 child coefficients at level i-1 in the form of 2x2 block of adjacent pixels. These 4 child coefficients are at the same relative location in the

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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.

Figure1.Data structure used in the SPIHT algorithm

2.4 Coding algorithm

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 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.

3. Achieved results

DICOM images of resolution 512x512 pixels are used for experiments and CDF wavelet filter is used for wavelet transform. Our method shows performance as good as other algorithms

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at a bit rate lower than 0.5 bpp. SPIHT have better visual quality than JPEG at high compression. The original and reconstructed images with bit rate of 0.2, 0.3, 0.5, 0.7 and 1.0 bpp processed by this algorithm is shown in Fig.2. The reconstructed images at a low bpp show good quality without distortion. The encoding and decoding time increases as bit rate increases. Fig.3 show the Peak Signal-to-Noise Ratio (PSNR) produced at different bit rates.

(a) (b) (c)

(d) (e) (f)

Fig.2(a)Original image(b)Reconstructed image at 0.2bpp (compression rate=8.82);PSNR=25dB PSNR=25 dB (c)0.3bpp (compression rate =4.32);PSNR=37 dB (d)0.5bpp (compression rate = 4.16);PSNR=39 dB; (e) 0.7 bpp (compression rate=3.7);PSNR=41 dB (f) 1.0bpp (compression rate=3.21);PSNR=44 dB

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Rate (bpp)

Fig 3. Performance of the coding method for the DICOM image

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The effectiveness of the algorithm described above can be statistically modeled and

evaluated. Many number of DICOM images collected from hospitals was used as material for this statistical survey.The aim of this survey is to compare the compression rates and PSNR for different algorithms. In every case PSNR was calculated. The performance of our method for

������������������������������ Table 1.Compression rate for CDF-SPIHT, LZW, JPEG, LZW-JPEG algorithms

Algorithm CDF-SPIHT LZW JPEG LZW-JPEG

Compression rate (min)

3:1

3:1

1:1

7:1

Compression rate (max)

13:1

4:1

10:1

20:1

DICOM images is much better than JPEG and comparable to LZW-JPEG [5] Our 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 log 10 (255 2 / MSE) dB

The rate vs. PSNR results are excellent. At a bit rate of 0.5 bpp the compressed images exhibit better subjective quality with PSNR of 38 dB. The evaluation results are shown in Table 1. 4. Conclusion

We have described a wavelet-based compression with set partitioning in hierarchical

trees, which appears to give extremely good performance in the DICOM standard for medical image compression. This algorithm outperforms the standard JPEG compression 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 (bpp) and decomposition levels.

We incorporated an effective SPIHT coding which appears to give extremely good performance in the DICOM standard for medical image compression. 10. References [1] P.Schelkens, A.Munteanu, J.Barbarien, M.Galca, X.Giro-Nieto and J.Cornelis, “Wavelet coding of volumetric medical datasets,” IEEE Trans. Med. Imag.vol 22,pp.441-458, Mar.2003. [2] A.R.Golderbank, Ingrid Daubechies, Wim Sweldens and Boon-Lock Yeo, “Lossless image compression using integer to integer wavelet transforms,” IEEE Trans, 1997. [3] Wallace GK.’’The JPEG still picture compression standard,’’ Comm of the ACM3 34:30-44,1991. [4] Shaou-Gang Miaou and Shih_Tse Chen, “Automatic quality control for wavelet-based compression of volumetric medical images using distortion-constrained adaptive vector quantization,” IEEE Trans.Medical Imaging, vol 23.No.11, November 2007. [5] S.Hludov, Chr.Meinel, “DICOM - image compression,” IEEE proceedings on Med.Imag., 1999. [6] Cedric Vonesch, Thierry Blu and Micheal Unser, ‘‘Generalized biorthoganal Daubechies wavelets,’’ Biomedical Imaging, 2006. [7] 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. [8] Shapiro JM,”Embedded image coding using zero trees of wavelet coefficients,” IEEE Trans Signal Processing 41:3445-3462,1993.

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