JPEG2000 ROI CODING IN MEDICAL IMAGING APPLICATIONS

Transcription

1 JPEG2000 ROI CODING IN MEDICAL IMAGING APPLICATIONS George K. Anastassopoulos 1 and Athanassios N. Skodras 2,3 1 Medical Informatics Laboratory, Democritus University of Thrace, GR Alexandroupolis, Greece [email protected] 2 Electronics Laboratory, University of Patras, GR Patras, Greece 3 Research Academic Computer Technology Institute, University Campus, GR Patras, Greece [email protected] ABSTRACT Region of interest (ROI) coding is one of the innovative functionalities supported by JPEG2000. It enables a non-uniform distribution of the available bit budget (i.e. image quality) between a selected region (ROI) and the rest of the image (background). This is achieved by appropriate scaling of the background wavelet coefficients. Two different methods are used for ROI coding, the MAXSHIFT method and the scaling-based method. In the present work it is subjectively and objectively verified that ROI coding offers higher diagnostic value for low bit rate (lossy) compression, as opposed to the non-roi compression of medical images. The MAXSHIFT method, which is included in the baseline algorithm of the standard, proves to be very effective, thus greatly contributing to the use of lossy coding for medical imagery compression. KEY WORDS JPEG2000, medical image compression, ROI (Regionof-Interest) coding 1. INTRODUCTION An increasing number of medical imagery is created directly in digital form. Clinical picture archiving and communication systems (PACS), as well as telemedicine networks require the storage and transmission of this huge amount of medical image data. Efficient compression of these data is crucial. Several lossless and lossy techniques for the compression of the data have been proposed [1-3]. Lossless techniques allow exact reconstruction of the original imagery, while lossy techniques aim to achieve high compression ratios by allowing some acceptable degradation in the image. Lossless compression does not degrade the image, thus facilitating accurate diagnosis, of course at the expense of higher bit rates, i.e. lower compression ratios. On the other hand, lossy compression is gaining acceptance, despite the many hesitations expressed by physicians who fear that such techniques might lead to errors in diagnosis, or imposed by several legal and regulatory issues. The new still image compression standard, JPEG2000, offers a lot of features and fulfils the requirements for the medical imagery compression [4]. Many researchers have extensively worked on its potential for the lossless compression of medical images [5-8]. In the present communication the combination of the lossy compression and the ROI capabilities of the JPEG2000 are evaluated with regard to their diagnostic ability of medical images. It is shown, through extensive subjective tests that the diagnostic value of an image does not degrade even for very low bit rate coding. The remainder of this paper is structured as follows. In Section 2 the main features of the JPEG2000 compression standard are reviewed. In Section 3 the medical imagery data used for the experiments, are presented, while the experimental results are given in Section 4. Finally, in Section 5 conclusions are drawn. 2. THE JPEG2000 IMAGE COMPRESSION STANDARD The Joint Photographic Experts Group developed the JPEG International standard [1]. With the continual expansion of multimedia and Internet applications, the needs and requirements of the technologies used, grew and evolved. In 1997, some of the world s leading companies and top researchers started contributing to the development a new image coding system, the JPEG2000 standard. This project, JTC (ISO/IEC or ITU-T Rec. T.800), was intended to create a new image coding system for different types of still images (b-level, gray-level, color, multicomponent) with different characteristics (natural, scientific, medical, remote sensing images, rendered graphics, etc) allowing different imaging models (real time transmission, image library archival, limited buffer and bandwidth resources etc) preferably within a unified system. The JPEG2000 still image coding system allows for low bit rate operation with distortion and subjective image quality performance superior to existing standards, without sacrificing performance at

2 other points in the rate distortion spectrum, incorporating at the same time many interesting features [9-16]. Some of the features of the JPEG2000 standard include: Lossless and lossy compression: It provides lossless compression naturally in the course of progressive decoding. An example of application that can use this feature is in medical images, where image size has to be reduced, without sacrificing the image quality and the diagnostic information. It has also the property of creating embedded bit-stream thus allowing progressive lossy to lossless build-up. Superior low bit-rate performance: This standard offers performance superior to the current standards at low bit-rates. This significantly improved low bit-rate performance is achieved without sacrificing performance on the rest of the rate-distortion spectrum. Continuous-tone and bi-level compression: The standard is capable of compressing both continuous-tone and bi-level images. The system compresses and decompresses images with various dynamic ranges for each color component. Robustness to bit-errors: Robustness to bit-errors is important in mobile and Internet applications. Portions of the code-stream may be more significant than others in determining decoded image quality. Proper design of the code-stream aids subsequent error correction systems in alleviating catastrophic decoding failures. Scalable coding of still images by means of SNR scalability and spatial scalability. SNR scalability is used in systems with the primary common feature that a minimum of two layers of image quality is necessary. SNR scalability involves generating at least two image layers of the same spatial resolution, but different qualities, from a single image source. The lower layer is coded by itself to provide the basic image quality and the enhancement layers are coded to enhance the lower layer. An enhancement layer, when added back to the lower layer, regenerates a higher quality reproduction of the input image. Spatial scalability is intended for use in systems with the primary common feature that a minimum of two layers of spatial resolution is necessary, such as fast database access as well as for delivering different resolutions to terminals with different capabilities in terms of display and bandwidth capabilities. Spatial scalability involves generating at least two spatial resolution layers from a single source such that the lower layer is coded by itself to provide the basic spatial resolution and the enhancement layer employs the spatially interpolated lower layer and carries the full spatial resolution of the input image source. Region-of-Interest (ROI) Coding: Often there are parts of an image that are of greater importance than others. This feature allows users to define certain ROI s in the image to be coded and transmitted without any loss or in a better quality and less distortion than the rest of the image. More about ROI coding The ROI coding scheme of the standard is based on the MAXSHIFT method (Part 1) and on the general scalingbased method (Part 2) [17-23]. The principle of the general ROI scaling-based method is to scale (shift) coefficients so that the bits associated with the ROI are placed in higher bit-planes than the bits associated with the background. Then, during the embedded coding process, the most significant ROI bit-planes are placed in the bit-stream before any background bit-planes of the image. Depending on the scaling value, some bits of the ROI coefficients might be encoded together with non-roi coefficients. Thus, the ROI will be decoded, or refined, before the rest of the image. Regardless of the scaling, a full decoding of the bit-stream results in a reconstruction of the whole image with the highest fidelity available. If the bit-stream is truncated, or the encoding process is terminated before the whole image is fully encoded, the ROI will be of higher quality than the rest of the image. According to the MAXSHIFT method, the scaling value is computed in such a way that it makes possible to have arbitrary shaped ROI s without the need for transmitting shape information to the decoder. This means also that the decoder does not have to perform ROI mask generation either (this might still be needed at the encoder). The encoder scans the quantized coefficients and chooses a scaling value S such that the minimum coefficient belonging to the ROI is larger than the maximum coefficient of the background (non- ROI area). The decoder receives the bit-stream and starts the decoding process. Every coefficient that is smaller than S belongs to the background and is therefore scaled up. The decoder needs only to upscale the received background coefficients. The advantages of the MAXSHIFT method, as compared to the scaling-based method, is that encoding of arbitrary shaped ROI s is possible without the need for shape information at the decoder and without the need for calculating the ROI mask. The encoder is also simpler, since no shape encoding is required, i.e. shape is implicit. The decoder is almost as simple as a non- ROI capable decoder, while it can still handle ROI s of arbitrary shape. Finally, the MAXSHIFT method can be handled by all JPEG2000 decoders, as it is included in Part 1 of the standard, the baseline coding system [24]. The drawbacks of the MAXSHIFT method, as compared to the general scaling-based method, are that it may result in slightly higher bit-rates, and that no background information is available before the whole ROI is decoded. 3. THE MEDICAL IMAGERY DATA The medical imagery data are imported from a urology database that has been developed in the Urology Clinic of the University Hospital of Alexandroupolis, Greece.

3 The following implementation platform (connected to a 100 MBit Fast Hub Ethernet LAN) was employed: a PC (Pentium Ghz, 512 Mbytes RAM, 40 Gbyte hard disk), containing the entire database, a Heidelberg Lynotype CPS Saphir/Opal Scanner, and a PC (Pentium GHz), 256 Mbyte RAM, 32 Mbyte graphics card, 40 Gbyte hard disk, 21 display. The performance evaluation of JPEG2000 compression standard was based on its application on a series of 42 nephrostograms - as nephrostogram is a severe and urgent condition that necessitates early recognition and immediate management - in order to assure the optimum compression ratio, i.e. the compression ratio that preserves the diagnostic information of the images of the urology database. The images were acquired from typical X-Ray films under typical exposure conditions. These films were scanned in multiple resolutions, in order to achieve the best image according to urologists directions. Image digitization parameters were: constant pixel depth of 8-bits (256 grey levels), constant image size of Regions of Interest (ROIs), varying spatial resolution of the displayed image ( dpi). These images were saved in the PC in DICOM format. A left kidney nephrostogram of size 2240x2180 is presented in Fig. 1a, where lower minor calyx fornix rupture is indicated by contrast medium leak. The digital images were then compressed at variable bitrates, according to: 1. JPEG2000 general coding algorithm, 2. SNR scalability of JPEG2000, 3. ROI MAXSHIFT coding method, and 4. ROI scaling-based coding method. The selection of the ROI has been defined by the expert urologists as an area of the nephrostogram of size 1144x1104 (dashed rectangle in Fig.1a). The compressed image files, were then decompressed, and displayed as 256 grey scale images (8-bit). These images are referred to as reconstructed images in the text. The evaluation of the reconstructed images was based upon mixed criteria including: Mean Opinion Score (MOS): image quality assessment was carried out by visually comparing the specific ROI of the original and reconstructed images, after the application of the above mentioned compression techniques. The images were presented to six observers in random order. The observers were asked to evaluate the reconstructed images in accordance with their diagnostic value. The ranking was done on an integer scale from 1 to 5, i.e. 1 (bad), 2 (poor), 3 (fair), 4 (good) and 5 (excellent). Heavily blurred visualization of the renal collecting system and lower calyx leak, results to poor diagnosis. An image is ranked as acceptable if it maintains satisfactory diagnostic value (i.e. MOS 4). Objective criteria: PSNR (in db) and bit-rate (bits per pixel b/p or bpp). 4. EXPERIMENTAL RESULTS The effect of JPEG2000 lossy coding on nephrostograms for various compression ratios is presented in Fig. 1. Compression is performed by means of the JPEG2000 compression standard (verification model 8.6) [25]. In Fig. 2, the effect of SNR scalability is illustrated. The image is first losslesly compressed and decompressed at b/p up to 0.5 b/p. The objective (PSNR) comparison results for lossy JPEG2000 and for JPEG2000 SNR scalability are graphically shown in Fig. 3. It is seen that the lossy JPEG2000 performs better by approximately 2 db for compression ratios up to b/p. For compression ratios below this value, images are unacceptable. Some diagnostic information is retained when JPEG2000 is used, but no information is left (flat image) when the SNR scalability is applied, i.e. the difference between the two methods is in this case more than 31 db. These findings are in agreement with the MOS results. For compression ratios between b/p and b/p the reconstructed nephrostograms are characterized as fair; between b/p and b/p as good; above 0.01 b/p as excellent. The superiority of the ROI coding scheme, based on the MAXSHIFT method, over the ROI coding scheme, based on the general scaling method, can be subjectively judged with the help of Fig. 4 and Fig. 5, where the reconstructed nephrostograms are shown after compression at 0.7 b/p, 0.3 b/p and b/p, respectively. The objective (PSNR) comparison results for ROI coding scheme, based on the MAXSHIFT method, and for ROI coding scheme, based on the general scaling method, are graphically shown in Fig. 6. It is seen that the MAXSHIFT ROI coding scheme performs better for the critical range [0.2, 2] of bit rates. For compression ratios above 2 b/p, both schemes have a PSNR of 54 db, although below 0.2 b/p, they have 9.56 db. From this compression ratio and below, the diagnostic value of the non ROI area is absent. The comparison of the ROI area only (i.e. including only the most significant diagnostic information) between the original and the reconstructed ROIs, in all the coding schemes for a bit rate of b/p, provides the following results (Table 1). It is seen that both the ROI coding schemes provide better results in the selected area, that the lossy JPEG2000 and the SNR scalability. Table 1: PSNR results for the ROI area at b/p. lossy SNR ROI J2K scalability (MAXSHIFT) PSNR (db) ROI (Scaling) The MOS ranking method has similar results with the lossy JPEG2000 and SNR scalability results, because the visualization of the renal collecting system and lower calyx leak, was almost the same in all methods.

4 PSNR (db) ,02 0,04 0,06 0,08 0,1 0,12 Bitrate (bpp) JPEG 2000 SNR scalability Figure 3: PSNR vs bitrate for the JPEG2000 and SNR scalability compression PSNR (db) ,5 1 1,5 2 2,5 Bitrate (bpp) MAXSHIFT General scaling method Figure 6: PSNR results for the ROI coding of the nephrostogram 5. DISCUSSION AND CONCLUSIONS The JPEG2000 is the new tool for still image compression. It is a wavelet transform-based coding system that offers a plethora of features and capabilities. In the present work the ROI coding capabilities of the standard were evaluated on medical images. It was seen that the MAXSHIFT ROI coding method, which is the one included in Part 1 of the standard and thus it is present in all compliant decoders, is very effective, with performance similar to that of the scaling-based method, and better than lossy coding of the whole image or SNR scalable coding. This is due to the fact that the MAXSHIFT method devotes its entire bit budget to the ROI, before it starts dealing with the background information. This does not happen with the other cases mentioned above. Such an approach leads to better diagnostic value, as has been proved by the subjective tests, since the ROI is exactly the area where all diagnostic information resides. 6. ACKNOWLEDGEMENTS We are grateful to the urologists of the Urology Clinic of the University Hospital of Alexandroupolis and to the radiologists participated in the subjective tests. REFERENCES [1] W.B. Pennebaker and J. L. Mitcell, JPEG: Still Image Data Compression Standard, (Van Nostrand Reinhold, 1993). [2] K.R. Rao and J.J. Hwang, Techniques and Standards for Image, Video and Audio Coding, (Englewood Clifs, NJ: Prentice Hall, 1996). [3] A. Bovik (Ed.), Handbook of Image & Video Processing, (Academic Press, 2000). [4] ISO/IEC JTC1/SC29/WG1 N1271, JPEG2000 Requirements and Profiles, March [5] M.D. Adams and F. Kossentini, Reversible Integerto-Integer Wavelet Transforms for Image Compression: Performance Evaluation and Analysis, IEEE Trans. Image Proc., 9(6), June 2000, [6] A. Munteanu, J. Cornelis, P. De Muynck and P. Cristea, Wavelet Lossless Compression of Coronary Angiographic Images, Proc. Computers in Cardiology, Vol. 24, pp , [7] A. Bilgin, G. Zweig and M.W. Marcellin, Efficient Lossless Coding of Medical Image Volumes Using Reversible Integer Wavelet Transforms, Proc Data Compression Conf., pp , Snowbird, Utah, March [8] G. Anastassopoulos, A. Tsalkidis, I. Stephanakis, G. Mandellos and K. Simopoulos, Application of JPEG2000 Compression in Medical Database Image Data, Proc. 14 th Int. Conf. Digital Signal Processing (DSP2002), Santorini, Greece, [9] B.E. Usevitch, A Tutorial on Modern Lossy Wavelet Image Compression: Foundations of JPEG2000, IEEE Sig. Proc. Magazine, Sep [10] A.N. Skodras, C. Christopoulos and T. Ebrahimi, The JPEG2000 Still Image Compression Standard, IEEE Sig. Proc. Magazine, 18(5), Sep. 2001, [11] C.A. Christopoulos, A.N. Skodras and T. Ebrahimi, The JPEG2000 Still Image Coding System: An Overview, IEEE Trans. on Consumer Electronics, 46(4), Nov. 2000, [12] M.J. Gormish, D. Lee and M.W. Marcellin, JPEG2000: Overview, Architecture and Applications, Proc. IEEE Int. Conf. Image Proc. (ICIP 2000), Vancouver, Canada, Sept.10-13, [13] M. W. Marcellin, M. Gormish, A. Bilgin, M. Boliek, An Overview of JPEG2000, Proc. IEEE Data Compression Conf., Snowbird, Utah, March [14] M. Boliek, J. Scott Houchin and G. Wu, JPEG2000 Next Generation Image Compression System Features and Syntax, Proc. IEEE Int. Conf. on Image Proc., Vancouver, Canada, Sep [15] T. Ebrahimi, D. Santa Cruz, J. Askelöf, M. Larsson and C. Christopoulos, JPEG2000 Still Image Coding Versus Other Standards, Proc. SPIE Int. Symposium, San Diego, California, 30 July - 4 Aug [16] D. Santa Cruz and T. Ebrahimi, An Analytical Study of the JPEG2000 Functionalities, Proc. IEEE Int. Conf. Image Proc. (ICIP 2000), Vancouver, Canada, Sept.10-13, [17] E. Atsumi and N. Farvardin, Lossy/Lossless Region-Of-Interest Image Coding Based On Set Partitioning In Hierarchical Trees, Proc. IEEE Int.

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6 (a) (b) (c) Figure 1: Nephrostogram (a) original, (b) reconstructed after JPEG2000 compression at 0.01 b/p and (c) at b/p (a) (b) (c) Figure 2: SNR scalability. Reconstructed nephrostogram at: (a) 0.01 b/p, (b) b/p and (c) at b/p (a) (b) (c) Figure 4: ROI encoding results (MAXSHIFT method). Reconstructed nephrostogram at: (a) 0.7, (b) 0.3 and (c) 0.03 b/p (a) (b) (c) Figure 5: ROI encoding results general scaling method). Reconstructed nephrostogram at: (a) 0.7, (b) 0.3 and (c) 0.03 b/p.