- Email: [email protected]
Internet transmission of DICOM images with effective low bandwidth utilization
Digital Signal Processing 16 (2006) 825–831 www.elsevier.com/locate/dsp
Internet transmission of DICOM images with effective low bandwidth utilization B. Ramakrishnan a , N. Sriraam b,∗ a Department of Biomedical Engineering, Manipal Institute of Technology, Manipal, India b Faculty of Information Technology, Multimedia University, Jalan Multimedia, 63100 Cyberjaya, Malaysia
Available online 13 June 2006
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 allowing cost-effective delivery of medical images for primary diagnosis. This paper highlights a wavelet based set partitioning in hierarchical trees (SPIHT) coder for progressive transmission of DICOM images. The header of the DICOM image is first transmitted followed by the compressed image data and then at the receiving end, images are reconstructed from low quality to high (or perfect) quality. The performance of the coder is evaluated using two image quality assessment criteria, namely, mean squared error (MSE) and mean structural similarity (MSSIM) index. The results prove that our method provides diagnostically useful information as rapidly as possible utilizing minimum bandwidth than variants of JPEG as reported in literature. © 2006 Elsevier Inc. All rights reserved. Keywords: DICOM; JPEG; Progressive transmission; SPIHT; Telemedicine; Wavelet compression
1. Introduction DICOM [1] stands for digital imaging and communications in medicine, which is widely adopted by the medical community for the purpose of storing, transmitting and viewing medical images. The consistency of DICOM standard has leveraged the development of computational applications for processing various medical images, thereby, preserving the clinical value. DICOM images are usually stored in the uncompressed raw data format and hence increases the storage size and the bandwidth requirement for transmission. It has been shown in [2] that the JPEG-2000 compression scheme enables the progressive transmission of different frequency fragments, thereby, providing a way to deliver significant information in a short period, before images are completely downloaded. To the best of our knowledge, attempts have not been made to identify the efficient technique for the transmission of large volume of DICOM images with a relatively lower bandwidth. In recent years, wavelet based compression techniques have become increasingly popular due to the fact that they provide exceptional image quality at high compression rates compared to Joint Photographic Experts Group (JPEG) * Corresponding author. Fax: +603 8312 5264.
E-mail address: [email protected] (N. Sriraam). 1051-2004/$ – see front matter © 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.dsp.2006.05.004
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based techniques [3,4]. They, in addition, support progressive lossy to lossless compression [5–7]. Various wavelet based coding methods have been developed utilizing the spatial similarities among the wavelet coefficients. These coders include embedded zero tree wavelet coder (EZW) [8], set partitioning in hierarchical trees (SPIHT) [9], an advancement of EZW, and embedded block coding with optimized truncation (EBCOT) [10], employed in JPEG2000. In this paper, DICOM images are compressed using SPIHT, which is a progressive transmission coder. The DICOM image header is transmitted first followed by the image details in successive stages. The receiver can terminate the transmitting data at any instant once the required information is satisfactory. We evaluate the performance of our scheme in a progressive lossy to lossless scenario. When compared with the existing DICOM supported compression standards (JPEG, JPEG-LS, and JPEG-2000) [11], our method maintains diagnostic feature of the DICOM images at low bit rate, thereby, attaining low bandwidth capabilities for Internet transmission. 2. Progressive transmission The principal notion of using wavelet is that it supports progressive transmission required for telemedicine [12,13]. Currently supported compression standards for DICOM images require that the image be compressed to the desired quantity before transmission. Such techniques are unsuitable for telemedicine since the amount of compression required cannot be known, and the file has to be recompressed at varying compression rates and retransmitted until the demand of the receiver is satisfied. But in the techniques that adopt progressive transmission, the data will be transmitted in successive stages until the required image quality is reached, after which the receiver can cease the process. At the receiving end, the salient features in the image can be realized quickly with minimum bandwidth utilization. If all the information is transmitted, the reconstructed image will be identical to the original image. In most cases, however, it is not necessary to send the entire information because the diagnostic relevant features in the image will be perfectly recognized after receiving certain amount of information. The advantages of progressive transmission could be understood in the following explanation. Consider an image of size 512 × 512 with each pixel having a resolution of 1 byte (8 bits). Then the total storage size for this image would be 256 kbytes. If the maximum bandwidth available for transmission through Internet is 5 kbytes/s, then the total time required to transmit the entire image would be approximately 51 s. Therefore, the image at the receiving end can be observed only after this time. In the case of progressive transmission, however, the image could be viewed from the first 5 kbytes received enabling swift observation and coalescing this with the fact that wavelet based compression provide excellent image quality at high compression, the image can be easily identified within the first few seconds. 3. Methodology SPIHT is a progressive transmission coder that produces embedded bit-streams. It works on the principle of spatial relationship among the wavelet coefficients at different levels and frequency sub-bands in the pyramid structure of wavelet decomposition. This pyramid structure is commonly known as spatial orientation tree. If a given coefficient at location is significant in magnitude then some of its descendants will also probably be significant in magnitude. The SPIHT algorithm takes advantage of the spatial similarity present in the wavelet space to optimally find the location of the wavelet coefficients that are significant by means of a binary search algorithm. The SPIHT algorithm sends the top coefficients in the pyramid structure using a progressive transmission scheme and thus allows obtaining a high quality version of the original image from the minimal amount of transmitted data. The pyramid wavelet coefficients are ordered by magnitude and then the most significant bits are transmitted first, followed by the next bit plane and so on until the lowest bit plane is reached. This reduces the MSE for every bit plane sent. In our method (Fig. 1), the ‘Transfer Syntax Unique Identification (TSUID)’ field of the DICOM header is modified to indicate that the image is compressed using SPIHT. 2-D lifting wavelet decomposition is applied to the image data. The decomposed image is then coded using SPIHT. The DICOM header is finally added to the embedded bit-stream resulting in the compressed DICOM image. During transmission, the header and the image data are first separated from the compressed DICOM image. The header is first transmitted followed by the bit-stream. As the bit stream is being received, it is decoded by the SPIHT decoder, and wavelet reconstruction is applied to obtain the reconstructed image. The receiver can terminate this process at any instant. The image quality at the receiving end depends on the number of bits received and decoded by the SPIHT decoder.
B. Ramakrishnan, N. Sriraam / Digital Signal Processing 16 (2006) 825–831
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Fig. 1. Compression and transmission scheme.
4. Results Experiments are performed on two types of DICOM images, namely, MR-spine image (MR) and X-ray chest image (XC), each consisting of 10 images with size 512 × 512 and 8-bit resolution. The performances are evaluated in terms of MSE and MSSIM index [14]. Though MSE is a widely used evaluation parameter for image quality, it does not exactly indicate the perceived visual quality of the image. Hence, evaluation is also being done using MSSIM index. MSSIM index is an image quality assessment parameter based very much on the characteristics of human visual system (HVS) and measures the structural similarity rather than error visibility between two images. For comparison purposes with our method, DICOM images were compressed and transmitted using the compression schemes currently supported by the DICOM standard. Tables 1 and 2 show the values of MSE of MR and XC for different compression ratios (CR) which is defined as CR =
original file size . compressed file size
(1)
From Tables 1 and 2, it is obvious that at higher compression ratios, MSE of JPEG-LS and JPEG are very high compared to SPIHT and JPEG-2000. JPEG-LS was developed for lossless and near-lossless compression and therefore, Table 1 Values of MSE for MR CR
SPIHT
JPEG-2000
JPEG-LS
JPEG
80 16 8 5 4
23.07 6.87 3.11 1.55 1.05
24.27 7.16 3.21 1.70 0.89
219.32 25.01 5.37 1.61 0.55
102.11 9.75 4.53 2.10 1.19
Table 2 Values of MSE for XC CR
SPIHT
JPEG-2000
JPEG-LS
JPEG
80 20 16 10 8
4.34 1.86 1.04 0.92 0.56
4.20 2.01 1.42 1.12 0.76
106.19 3.82 1.50 1.24 0.77
457.18 34.2 11.07 3.22 1.80
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(a)
(b) Fig. 2. Bit rate versus MSE for (a) MR and (b) XC.
Table 3 Values of MSSIM index for MR CR
SPIHT
JPEG-2000
JPEG-LS
JPEG
80 16 8 5 4
0.771 0.916 0.964 0.984 0.989
0.811 0.924 0.964 0.980 0.990
0.431 0.748 0.922 0.975 0.992
0.648 0.916 0.958 0.979 0.988
Table 4 Value of MSSIM index for XC CR
SPIHT
JPEG-2000
JPEG-LS
JPEG
80 20 16 10 8
0.963 0.979 0.987 0.991 0.992
0.966 0.975 0.984 0.989 0.991
0.794 0.962 0.983 0.988 0.989
0.440 0.725 0.899 0.962 0.978
performs poorly at high compression but at low compression, JPEG-LS give comparable results. SPIHT gives the lowest MSE values at high compression rates. A plot of bit rate against MSE (Fig. 2) confirms these findings. Tables 3 and 4 show the values of MSSIM index of MR and XC for different CR. From Tables 3 and 4, it is evident that at higher compression ratios, MSSIM index of JPEG-LS and JPEG are low compared to SPIHT and JPEG-2000. We could also note that the MSE and MSSIM index do not correlate for all values. JPEG-2000, SPIHT, and JPEG-LS give the lowest values at high, medium and low compression, respectively. Figure 3 matches these results. In general, SPIHT and JPEG-2000 performs consistently well for all compression rates compared to JPEG-LS and JPEG. This is because of the progressive transmission properties of SPIHT and JPEG-2000. From Tables 1–4, the evaluation parameters highlight the efficiency of SPIHT compared to other techniques. Further, the progressive transmission was continued until the lossless criterion is realized. For MR, CR achieved for lossless compression is 2.489, 2.485, 2.513, and 1.816 for SPIHT, JPEG-2000, JPEG-LS, and JPEG, respectively. This indicates that JPEG-LS perform slightly better than SPIHT and JPEG-2000 for lossless compression which also holds good for XC. Figures 4 and 5 display the reconstructed MR and XC images during the progressive transmission at several compression ratios. It is evident from Figs. 4 and 5 that in progressive transmission the images are reconstructed from coarse resolution to finer resolution with the quality of the image improving at each stage of transmission. Even though the compression performance are numerically comparable for SPIHT and JPEG-2000, when considering both the evaluation parameters, MSE and MSSIM index in to account, SPIHT performs well than JPEG-2000 at
B. Ramakrishnan, N. Sriraam / Digital Signal Processing 16 (2006) 825–831
(a)
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(b) Fig. 3. Bit rate versus MSSIM index for (a) MR and (b) XC.
Fig. 4. (a) Original MR-spine image, (b), (c), and (d) image reconstructed at compression ratios of 80, 8, and 4, respectively.
low bit rates. Further the embedded coding property of SPIHT allows exact bit rate control thereby avoid bits wasted with padding. This property allows SPIHT encoder as the suitable candidate for transmission of DICOM images with effective low bandwidth utilization. 5. Conclusions In this paper, we have highlighted the advantages of progressive transmission of DICOM images in a low bandwidth Internet using SPIHT coder. Two image quality parameters, namely, MSE and MSSIM index are used to evaluate the
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Fig. 5. Reconstructed X-ray chest DICOM image. (a) Original image, (b), (c), and (d) image reconstructed at compression ratios of 80, 16, and 8, respectively.
MRI spine and X-ray chest images and the results are compared with JPEG coders. The results indicate that in progressive transmission, the images are reconstructed from low resolution to high resolution enabling the receiver to view an approximate image with minimum information being transmitted, thereby, consuming less bandwidth and saving valuable time. It can be concluded that our method provides diagnostically useful information as rapidly as possible utilizing minimum bandwidth than variants of JPEG. References [1] DICOM Standard, http://medical.nema.org/dicom/2004. [2] JPEG-2000, http://www.jpeg.org/jpeg2000. [3] T.A. Iyriboz, M.J. Zukoski, K.D. Hopper, P.L. Stagg, A comparison of wavelet and joint photographic experts group lossy compression methods applied to medical images, Digit. Imag. 12 (1999) 14–17. [4] N.B. Amor, N. Essoukri, B. Amara, DICOM image compression by wavelet transform, in: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, vol. 2, October 2002, pp. 553–557. [5] A. Said, W.A. Pearlman, An image multiresolution representation for lossless and lossy compression, IEEE Trans. Image Process. 5 (9) (1996) 1303–1310. [6] F. Sheng, A. Bilgin, P.J. Sementilli, M.W. Marcellin, Lossy and lossless image compression using reversible integer wavelet transforms, in: Proceedings of the IEEE International Conference on Image Processing, vol. 3, October 1998, pp. 876–880. [7] W. Sweldens, The lifting scheme: A construction of second generation wavelets, SIAM J. Math. Anal. 29 (1998) 511–546. [8] J.M. Shapiro, Embedded image coding using zero trees of wavelet coefficients, IEEE Trans. Signal Process. 41 (12) (1993) 3445–3462. [9] A. Said, W.A. Pearlman, A new fast and efficient image codec based on set partitioning in hierarchical trees, IEEE Trans. Circuits Syst. Video Technol. 6 (3) (1996) 243–249. [10] D. Taubman, High performance scalable image compression with EBCOT, IEEE Trans. Image Process. 9 (7) (2000) 1151–1170. [11] DICOM Part 5: Data Structures and Encoding, http://medical.nema.org/dicom/2004/04_05PU.PDF.
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[12] B. Ramakrishnan, N. Sriraam, Compression of DICOM images based on wavelets and SPIHT for telemedicine applications, in: Proceedings of the ICBMP, March 2005. [13] R.S. Dilmaghani, A. Ahmadian, M. Ghavami, A.H. Aghvami, Progressive medical image transmission and compression, IEEE Signal Process. Lett. 11 (10) (2004) 806–809. [14] Z. Wang, A.C. Bovik, H.R. Sheikh, E.P. Simoncelli, Image quality assessment: From error visibility to structural similarity, IEEE Trans. Image Process. 13 (4) (2004) 600–612.
B. Ramakrishnan received the B.Sc. degree in biomedical engineering from Ajman University of Science and Technology, UAE, in 2003, and the M.Tech. degree in biomedical engineering from Manipal Institute of Technology, India, in 2005. He carried out his M.Tech. final project at Multimedia University, Malaysia, under the guidance of Mr. N. Sriraam. His research interests include medical image processing and compression, medical image analysis, and telemedicine. Currently he is serving as Managing Director of M/s Tii Techno Testing Instruments Pvt. Ltd., India. N. Sriraam received the B.E. (ECE) degree from National Engineering College, India, and the M.Tech. (biomedical engineering) [Distinction] degree from MIT, Manipal, India. He has secured the University First Rank and was awarded the gold medal in the M.Tech. He has 10 years of experience in teaching and his area of interest includes biomedical signal and image processing, bioinformatics, data mining, and neural networks. Currently he is working as a lecturer in the Faculty of Information Technology at Multimedia University, Malaysia, and concurrently doing his Ph.D. program. He is a Member of IEE, IEEE, IEEE EMB Society, and IEEE Signal Processing Society.
Internet transmission of DICOM images with effective low bandwidth utilization B. Ramakrishnan a , N. Sriraam b,∗ a Department of Biomedical Engineering, Manipal Institute of Technology, Manipal, India b Faculty of Information Technology, Multimedia University, Jalan Multimedia, 63100 Cyberjaya, Malaysia
Available online 13 June 2006
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 allowing cost-effective delivery of medical images for primary diagnosis. This paper highlights a wavelet based set partitioning in hierarchical trees (SPIHT) coder for progressive transmission of DICOM images. The header of the DICOM image is first transmitted followed by the compressed image data and then at the receiving end, images are reconstructed from low quality to high (or perfect) quality. The performance of the coder is evaluated using two image quality assessment criteria, namely, mean squared error (MSE) and mean structural similarity (MSSIM) index. The results prove that our method provides diagnostically useful information as rapidly as possible utilizing minimum bandwidth than variants of JPEG as reported in literature. © 2006 Elsevier Inc. All rights reserved. Keywords: DICOM; JPEG; Progressive transmission; SPIHT; Telemedicine; Wavelet compression
1. Introduction DICOM [1] stands for digital imaging and communications in medicine, which is widely adopted by the medical community for the purpose of storing, transmitting and viewing medical images. The consistency of DICOM standard has leveraged the development of computational applications for processing various medical images, thereby, preserving the clinical value. DICOM images are usually stored in the uncompressed raw data format and hence increases the storage size and the bandwidth requirement for transmission. It has been shown in [2] that the JPEG-2000 compression scheme enables the progressive transmission of different frequency fragments, thereby, providing a way to deliver significant information in a short period, before images are completely downloaded. To the best of our knowledge, attempts have not been made to identify the efficient technique for the transmission of large volume of DICOM images with a relatively lower bandwidth. In recent years, wavelet based compression techniques have become increasingly popular due to the fact that they provide exceptional image quality at high compression rates compared to Joint Photographic Experts Group (JPEG) * Corresponding author. Fax: +603 8312 5264.
E-mail address: [email protected] (N. Sriraam). 1051-2004/$ – see front matter © 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.dsp.2006.05.004
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based techniques [3,4]. They, in addition, support progressive lossy to lossless compression [5–7]. Various wavelet based coding methods have been developed utilizing the spatial similarities among the wavelet coefficients. These coders include embedded zero tree wavelet coder (EZW) [8], set partitioning in hierarchical trees (SPIHT) [9], an advancement of EZW, and embedded block coding with optimized truncation (EBCOT) [10], employed in JPEG2000. In this paper, DICOM images are compressed using SPIHT, which is a progressive transmission coder. The DICOM image header is transmitted first followed by the image details in successive stages. The receiver can terminate the transmitting data at any instant once the required information is satisfactory. We evaluate the performance of our scheme in a progressive lossy to lossless scenario. When compared with the existing DICOM supported compression standards (JPEG, JPEG-LS, and JPEG-2000) [11], our method maintains diagnostic feature of the DICOM images at low bit rate, thereby, attaining low bandwidth capabilities for Internet transmission. 2. Progressive transmission The principal notion of using wavelet is that it supports progressive transmission required for telemedicine [12,13]. Currently supported compression standards for DICOM images require that the image be compressed to the desired quantity before transmission. Such techniques are unsuitable for telemedicine since the amount of compression required cannot be known, and the file has to be recompressed at varying compression rates and retransmitted until the demand of the receiver is satisfied. But in the techniques that adopt progressive transmission, the data will be transmitted in successive stages until the required image quality is reached, after which the receiver can cease the process. At the receiving end, the salient features in the image can be realized quickly with minimum bandwidth utilization. If all the information is transmitted, the reconstructed image will be identical to the original image. In most cases, however, it is not necessary to send the entire information because the diagnostic relevant features in the image will be perfectly recognized after receiving certain amount of information. The advantages of progressive transmission could be understood in the following explanation. Consider an image of size 512 × 512 with each pixel having a resolution of 1 byte (8 bits). Then the total storage size for this image would be 256 kbytes. If the maximum bandwidth available for transmission through Internet is 5 kbytes/s, then the total time required to transmit the entire image would be approximately 51 s. Therefore, the image at the receiving end can be observed only after this time. In the case of progressive transmission, however, the image could be viewed from the first 5 kbytes received enabling swift observation and coalescing this with the fact that wavelet based compression provide excellent image quality at high compression, the image can be easily identified within the first few seconds. 3. Methodology SPIHT is a progressive transmission coder that produces embedded bit-streams. It works on the principle of spatial relationship among the wavelet coefficients at different levels and frequency sub-bands in the pyramid structure of wavelet decomposition. This pyramid structure is commonly known as spatial orientation tree. If a given coefficient at location is significant in magnitude then some of its descendants will also probably be significant in magnitude. The SPIHT algorithm takes advantage of the spatial similarity present in the wavelet space to optimally find the location of the wavelet coefficients that are significant by means of a binary search algorithm. The SPIHT algorithm sends the top coefficients in the pyramid structure using a progressive transmission scheme and thus allows obtaining a high quality version of the original image from the minimal amount of transmitted data. The pyramid wavelet coefficients are ordered by magnitude and then the most significant bits are transmitted first, followed by the next bit plane and so on until the lowest bit plane is reached. This reduces the MSE for every bit plane sent. In our method (Fig. 1), the ‘Transfer Syntax Unique Identification (TSUID)’ field of the DICOM header is modified to indicate that the image is compressed using SPIHT. 2-D lifting wavelet decomposition is applied to the image data. The decomposed image is then coded using SPIHT. The DICOM header is finally added to the embedded bit-stream resulting in the compressed DICOM image. During transmission, the header and the image data are first separated from the compressed DICOM image. The header is first transmitted followed by the bit-stream. As the bit stream is being received, it is decoded by the SPIHT decoder, and wavelet reconstruction is applied to obtain the reconstructed image. The receiver can terminate this process at any instant. The image quality at the receiving end depends on the number of bits received and decoded by the SPIHT decoder.
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Fig. 1. Compression and transmission scheme.
4. Results Experiments are performed on two types of DICOM images, namely, MR-spine image (MR) and X-ray chest image (XC), each consisting of 10 images with size 512 × 512 and 8-bit resolution. The performances are evaluated in terms of MSE and MSSIM index [14]. Though MSE is a widely used evaluation parameter for image quality, it does not exactly indicate the perceived visual quality of the image. Hence, evaluation is also being done using MSSIM index. MSSIM index is an image quality assessment parameter based very much on the characteristics of human visual system (HVS) and measures the structural similarity rather than error visibility between two images. For comparison purposes with our method, DICOM images were compressed and transmitted using the compression schemes currently supported by the DICOM standard. Tables 1 and 2 show the values of MSE of MR and XC for different compression ratios (CR) which is defined as CR =
original file size . compressed file size
(1)
From Tables 1 and 2, it is obvious that at higher compression ratios, MSE of JPEG-LS and JPEG are very high compared to SPIHT and JPEG-2000. JPEG-LS was developed for lossless and near-lossless compression and therefore, Table 1 Values of MSE for MR CR
SPIHT
JPEG-2000
JPEG-LS
JPEG
80 16 8 5 4
23.07 6.87 3.11 1.55 1.05
24.27 7.16 3.21 1.70 0.89
219.32 25.01 5.37 1.61 0.55
102.11 9.75 4.53 2.10 1.19
Table 2 Values of MSE for XC CR
SPIHT
JPEG-2000
JPEG-LS
JPEG
80 20 16 10 8
4.34 1.86 1.04 0.92 0.56
4.20 2.01 1.42 1.12 0.76
106.19 3.82 1.50 1.24 0.77
457.18 34.2 11.07 3.22 1.80
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(a)
(b) Fig. 2. Bit rate versus MSE for (a) MR and (b) XC.
Table 3 Values of MSSIM index for MR CR
SPIHT
JPEG-2000
JPEG-LS
JPEG
80 16 8 5 4
0.771 0.916 0.964 0.984 0.989
0.811 0.924 0.964 0.980 0.990
0.431 0.748 0.922 0.975 0.992
0.648 0.916 0.958 0.979 0.988
Table 4 Value of MSSIM index for XC CR
SPIHT
JPEG-2000
JPEG-LS
JPEG
80 20 16 10 8
0.963 0.979 0.987 0.991 0.992
0.966 0.975 0.984 0.989 0.991
0.794 0.962 0.983 0.988 0.989
0.440 0.725 0.899 0.962 0.978
performs poorly at high compression but at low compression, JPEG-LS give comparable results. SPIHT gives the lowest MSE values at high compression rates. A plot of bit rate against MSE (Fig. 2) confirms these findings. Tables 3 and 4 show the values of MSSIM index of MR and XC for different CR. From Tables 3 and 4, it is evident that at higher compression ratios, MSSIM index of JPEG-LS and JPEG are low compared to SPIHT and JPEG-2000. We could also note that the MSE and MSSIM index do not correlate for all values. JPEG-2000, SPIHT, and JPEG-LS give the lowest values at high, medium and low compression, respectively. Figure 3 matches these results. In general, SPIHT and JPEG-2000 performs consistently well for all compression rates compared to JPEG-LS and JPEG. This is because of the progressive transmission properties of SPIHT and JPEG-2000. From Tables 1–4, the evaluation parameters highlight the efficiency of SPIHT compared to other techniques. Further, the progressive transmission was continued until the lossless criterion is realized. For MR, CR achieved for lossless compression is 2.489, 2.485, 2.513, and 1.816 for SPIHT, JPEG-2000, JPEG-LS, and JPEG, respectively. This indicates that JPEG-LS perform slightly better than SPIHT and JPEG-2000 for lossless compression which also holds good for XC. Figures 4 and 5 display the reconstructed MR and XC images during the progressive transmission at several compression ratios. It is evident from Figs. 4 and 5 that in progressive transmission the images are reconstructed from coarse resolution to finer resolution with the quality of the image improving at each stage of transmission. Even though the compression performance are numerically comparable for SPIHT and JPEG-2000, when considering both the evaluation parameters, MSE and MSSIM index in to account, SPIHT performs well than JPEG-2000 at
B. Ramakrishnan, N. Sriraam / Digital Signal Processing 16 (2006) 825–831
(a)
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(b) Fig. 3. Bit rate versus MSSIM index for (a) MR and (b) XC.
Fig. 4. (a) Original MR-spine image, (b), (c), and (d) image reconstructed at compression ratios of 80, 8, and 4, respectively.
low bit rates. Further the embedded coding property of SPIHT allows exact bit rate control thereby avoid bits wasted with padding. This property allows SPIHT encoder as the suitable candidate for transmission of DICOM images with effective low bandwidth utilization. 5. Conclusions In this paper, we have highlighted the advantages of progressive transmission of DICOM images in a low bandwidth Internet using SPIHT coder. Two image quality parameters, namely, MSE and MSSIM index are used to evaluate the
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B. Ramakrishnan, N. Sriraam / Digital Signal Processing 16 (2006) 825–831
Fig. 5. Reconstructed X-ray chest DICOM image. (a) Original image, (b), (c), and (d) image reconstructed at compression ratios of 80, 16, and 8, respectively.
MRI spine and X-ray chest images and the results are compared with JPEG coders. The results indicate that in progressive transmission, the images are reconstructed from low resolution to high resolution enabling the receiver to view an approximate image with minimum information being transmitted, thereby, consuming less bandwidth and saving valuable time. It can be concluded that our method provides diagnostically useful information as rapidly as possible utilizing minimum bandwidth than variants of JPEG. References [1] DICOM Standard, http://medical.nema.org/dicom/2004. [2] JPEG-2000, http://www.jpeg.org/jpeg2000. [3] T.A. Iyriboz, M.J. Zukoski, K.D. Hopper, P.L. Stagg, A comparison of wavelet and joint photographic experts group lossy compression methods applied to medical images, Digit. Imag. 12 (1999) 14–17. [4] N.B. Amor, N. Essoukri, B. Amara, DICOM image compression by wavelet transform, in: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, vol. 2, October 2002, pp. 553–557. [5] A. Said, W.A. Pearlman, An image multiresolution representation for lossless and lossy compression, IEEE Trans. Image Process. 5 (9) (1996) 1303–1310. [6] F. Sheng, A. Bilgin, P.J. Sementilli, M.W. Marcellin, Lossy and lossless image compression using reversible integer wavelet transforms, in: Proceedings of the IEEE International Conference on Image Processing, vol. 3, October 1998, pp. 876–880. [7] W. Sweldens, The lifting scheme: A construction of second generation wavelets, SIAM J. Math. Anal. 29 (1998) 511–546. [8] J.M. Shapiro, Embedded image coding using zero trees of wavelet coefficients, IEEE Trans. Signal Process. 41 (12) (1993) 3445–3462. [9] A. Said, W.A. Pearlman, A new fast and efficient image codec based on set partitioning in hierarchical trees, IEEE Trans. Circuits Syst. Video Technol. 6 (3) (1996) 243–249. [10] D. Taubman, High performance scalable image compression with EBCOT, IEEE Trans. Image Process. 9 (7) (2000) 1151–1170. [11] DICOM Part 5: Data Structures and Encoding, http://medical.nema.org/dicom/2004/04_05PU.PDF.
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[12] B. Ramakrishnan, N. Sriraam, Compression of DICOM images based on wavelets and SPIHT for telemedicine applications, in: Proceedings of the ICBMP, March 2005. [13] R.S. Dilmaghani, A. Ahmadian, M. Ghavami, A.H. Aghvami, Progressive medical image transmission and compression, IEEE Signal Process. Lett. 11 (10) (2004) 806–809. [14] Z. Wang, A.C. Bovik, H.R. Sheikh, E.P. Simoncelli, Image quality assessment: From error visibility to structural similarity, IEEE Trans. Image Process. 13 (4) (2004) 600–612.
B. Ramakrishnan received the B.Sc. degree in biomedical engineering from Ajman University of Science and Technology, UAE, in 2003, and the M.Tech. degree in biomedical engineering from Manipal Institute of Technology, India, in 2005. He carried out his M.Tech. final project at Multimedia University, Malaysia, under the guidance of Mr. N. Sriraam. His research interests include medical image processing and compression, medical image analysis, and telemedicine. Currently he is serving as Managing Director of M/s Tii Techno Testing Instruments Pvt. Ltd., India. N. Sriraam received the B.E. (ECE) degree from National Engineering College, India, and the M.Tech. (biomedical engineering) [Distinction] degree from MIT, Manipal, India. He has secured the University First Rank and was awarded the gold medal in the M.Tech. He has 10 years of experience in teaching and his area of interest includes biomedical signal and image processing, bioinformatics, data mining, and neural networks. Currently he is working as a lecturer in the Faculty of Information Technology at Multimedia University, Malaysia, and concurrently doing his Ph.D. program. He is a Member of IEE, IEEE, IEEE EMB Society, and IEEE Signal Processing Society.