Lossless Compression for Medical Image ... - Semantic Scholar

Proceeding of NCIEEE‟13 (ISBN: 978-81-924031-9-9) 21st – 22nd February 2013

Lossless Compression for Medical Image Sequences Using Combination Methods M.Ferni ukrit ResearchScholar, SathyabamaUniversity,Chennai Email:[email protected] Abstract-Image compression is significant in the medical industry. The sequence of images produced is of great importance which requires lossless compression technique. Among the several lossless image compression algorithms JPEG-LS becomes a promising technique to compress the medical image sequences which gives the best performance both in terms of compression ratio and compression speed.JPEG-LS compress only a single picture with intracoding. To enhance the compression with medical image sequences we have combined JPEG-LS with interframecoding. This combination has provided the compression ratio of 4.8.To further increase the compression ratio we propose a new scheme Head Code Compression.Experimental results of our proposed algorithm achieve 20% more reduction than the prior arts. Keywords-Lossless Compression, Medical Image Sequences, JPEG-LS, Interframe Coding, Head Code Compression. I.INTRODUCTION

Hospitals and various medical organizations produce huge volume of digital medical image sequences which includes Computed Tomography(CT),Magnetic Resonance Image(MRI),Ultrasound and Capsule Endoscope(CE) images. These medical image sequences require considerable storage space[1].The solution to this problem could be the application of compression. Medical image compression is very important in the present world for efficient archiving and transmission of images. Image compression can be classified as lossy and lossless. In lossy compression scheme there is loss of information and the original image is not recovered exactly. Lossy scheme seems to be irreversible.But lossless scheme is reversible and this represents an image signed with the smallest possible number of bits without loss of any information thereby speeding up transmission and minimizing storage requirement. Lossless reproduces the original image without any quality loss[2].Medical imaging does not require lossy compression due to the following reason.The first reason is the incorrect diagnosis due to the loss of useful information.The second reason is the operations like image

Dr.G.R.Suresh Professor Easwari Engineering College,Chennai, Email:[email protected] enhancement may emphasize the degradations caused by lossy compression. Hence efficient lossless compression methods are required for medical images[3].Lossless compression includes Discrete Cosine Transform,Wavelet Compression,Fractal Compression, Vector Quantization and Linear PredictiveCoding.Lossless consist of two distinct and independent components called modeling and coding.The modeling generates a statistical model for the input data.The coding maps the input data to bit strings [4]. Several Lossless image compression algorithms were evaluated for compressing medical images. Among the lossless image compression algorithms like Lossless JPEG,JPEG 2000,PNG and CALIC,JPEGLS is considered to be the best algorithm both in terms of ratio and speed[5],[14].JPEG-LS has excellent coding and best possible compression efficiency[1].This is a simple and baseline algorithm which consists of two independent and distinct stages called modeling and encoding. This is developed with the aim of providing a low complexity lossless and near-lossless image compression. This is based on LOCO-I(Low Complexity Lossless Compression for Images)algorithm[6] using adaptive prediction, context, modeling and Golomb coding. It supports near lossless compression by allowing a fixed maximum sample error.A continuous image is generally compressed best in JPEG-LS[7]. Most of the lossless image compression algorithms take only a single image independently without utilizing the correlation among the sequence of frames of MRI or CE images.Since there is too much correlation among the medical image sequences, we can achieve a higher compression ratio using interframe coding.The idea of compressing sequence of images was first adopted in [8] for lossless image compression and was used in [9],[10],[11] for lossless video compression.The Compression Ratio(CR) was significantly low(i.e.)2.5 which was not satisfactory because using JPEG-LS alone can obtain a higher CR.Hence in [1] they have combined JPEG-LS with interframe coding to find the correlation among image sequences and the obtained

Department of EEE, Sathyabama University, Chennai-119

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Proceeding of NCIEEE‟13 (ISBN: 978-81-924031-9-9) 21st – 22nd February 2013 ratio was 4.8.However this ratio can still be further improved by using a new technique Head Code Compression (HCC).HCC scheme is used to compress code words[12]. In this paper, we propose a hybrid algorithm for medical image sequences. The proposed algorithm combines JPEG-LS with interframe coding and a new innovative scheme HCC to achieve a high compression ratio. The CR can be calculated by the equation (1)

(1) II. PROPOSED ALGORITHM A. Overview Since most of the lossless compression techniques deal with only a single image, they do not exploit the interframe correlation of images. The aim of the proposed algorithm is to improve the compression rate using JPEG-LS and HCC.Figure1 illustrates the complete encoding technique.

Image Sequence

Current Frame

Reference Frame

The input can be 8-bit grey-level image. Given an image sequence, the first image will be compressed by 8-bit. The input can be 8-bit grey-level image. Given an image sequence, the first image will be compressed by 8-bit JPEG-LS coding since there is no reference frame. Now the second frame will be the current frame and the first frame becomes the reference for the second frame and the process continues. Then Motion Estimation and Motion Compensation (MEMC) are done for two images. After MEMC is done the difference of images is processed for compression. The difference is also compressed using JPEG-LS compression. The Motion Vectors (MV) derived from motion estimation will also be compressed. Then the flag bits and the encoded bits for one image frame are concatenated into single bit stream. This processing will be repeated for the next image frame until the end of image sequence. B. Motion Estimation and Motion Compensation Motion estimation is the estimation of the displacement of image structures from one frame to another in a time sequence of 2D images. The displacement vector at location r in frame at time t, describing the motion from frame at t to frame at t+Δt, as

where r=[x,y]T,the continuous spatial coordinates and superscript T denotes vector transposition. The dependency of the displacement vector on spatial coordinates for each location r at frame t, a displacement vector can be defined as in Figure 2

Motion Estimation and Motion Compensation

t + Δt JPEG-LS Compression

d (r)

r t

Figure 2.Structure of motion estimation Compressed File Head Code Compression

Compressed Image

Figure 1.Flow chart of the Compression Phase

The steps in MEMC is stated as Find displacement vector of a pixel or a set of pixels between frame Via displacement vector, predict counterpart in present frame Prediction error, positions,motion vectors are coded & transmitted The Figure 3 illustrates the block diagram of motion compensated coding


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Proceeding of NCIEEE‟13 (ISBN: 978-81-924031-9-9) 21st – 22nd February 2013 Motion analysis

Prediction and Differentiation

Encoding Figure 3. Block diagram of motion compensated coding

Motion estimation can be very computationally intensive and so this compression performance may be at theexpense of high computational complexity.The motion estimation creates a model bymodifying one or more reference frames to matchthe current frame as closely as possible. The current frame is motion compensated by subtracting the model from the frame to producea motioncompensated residual frame. This is coded and transmitted, along with the information required for the decoder to recreate the model (typically a set of motion vectors).At the same time, the encoded residual is decoded and added to the model toreconstruct a decoded copy of the currentframe (which may not be identical to theoriginal frame because of coding losses).This reconstructed frame is stored to be usedas reference frame for further predictions.The interframe coding should include MEMC process to remove temporal redundancy. Difference coding or conditional replenishment is a very simple interframe compression process during which each frame of a sequence is compared with its predecessor and only pixels that have changes are updated. Only a fraction of pixel values are transmitted. An intercoded frame will finitely be divided into blocks known as macro blocks. After that, instead of directly encoding the raw pixel values for each block, as it would be done for an intraframe, the encoder will try to find a similar block to the one it is encoding on a previously encoded frame, referred to as reference frame. This process is done by a block matching algorithm. [13]. If the encoder succeeds on its search, the block could be directly encoded by a vector known as motion vector, which points to the position of the matching block at the reference frame. C.Motion Vector Motion estimation is using a reference frame in a video, dividing it in blocks and figuring out where theblocks have moved in the next frame using motion vectors pointing from the initial block location in the reference frame to the final block location in the next frame. For MV calculation we use Block matching

algorithm as it is simple and effective. It uses Mean Square Error (MSE) for finding the best possible match for the reference frame block in the target frame.Motion vector is the key element in motion estimation process. It is used to represent a macro block in a picture based on the position of this macro block in another picture called the reference picture. In video editing, motion vectors are used to compress video by storing the changes to an image from one frame to next. When motion vector is applied to an image, we can synthesize the next image called motion compensation [8],[13]. This is used to compress video by storing the changes to an image from one frame to next frame. To improve the quality of the compressed medical image sequence, motion vector sharing is used. [11]. D.Block Matching The block-matching process during the function MEMC taken from [1] takes much time hence we need a fast searching method and we have taken Diamond Search (DS) method [13] which is the best among methods both in accuracy and speed. This determines the displacement of a particular pixel „p‟ at frame at time t, a block of pixels centered at „p‟ is considered. The frame at time t + Δt is searched for the best matching block of the same size. In the matching process, it is assumed that pixels belonging to the block are displaced with the same amount. Matching is performed by either maximizing the cross correlation function or minimizing an error criterion. The most commonly used error criteria arethe Mean Square Error (MSE) as stated in equation (2) and the Minimum Absolute Difference (MAD) as stated in equation (3)

(2)

(3) The diamond search algorithm is basedon MV distribution of real world video sequences. It employs two search patterns in which thefirst pattern, called large diamond search pattern (LDSP) comprises nine checking points andform a diamond shape. The second pattern consists of five checking points make a small diamondpattern (SDSP). The search starts with the LDSP and is used repeatedly until the minimum Block Distortion Measure(BDM)point lies on the search center. The search pattern is then switched to SDSP. The position yielding minimum error point is taken as the final MV.DS is


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Proceeding of NCIEEE‟13 (ISBN: 978-81-924031-9-9) 21st – 22nd February 2013 anoutstanding algorithm adopted by MPEG-4 verification model (VM) due to its superiority toother methods in the class of fixed search pattern algorithms. E.Head Code Compression We also propose HCC scheme to further compress the code-words. The main concept is to utilize the nature of image sequence to perform compression. The first bit of each code-word is termed as the head code and the remaining bits are termed as tail codes that are identified to remove head codes. The head code is either 0 or 1, and this indicates that whether the current pixel is identical to the previous pixel. The code- word containing equal pixels has only the head code that is ready for compression. The first bit is a flag bit which contains all 0 or 1.The second will be 0 or 1 depending on the Best Run Length (BRL) codes which contains 0 or 1.Otherwise we get the original codes. Figure 4 shows the overall architecture. The first frame is decompressed using HCC decoder followed by JPEG-LS decoder. After the reproduction of the first frame the difference of the rest of the frames are decompressed. The first frame becomes the reference frame for the next frame. After the reproduction of the second frame it becomes the reference frame for the next frame and the process continues until all the frames are decompressed. Original Image

JPEG-LS Compression

Head code compression

Suspected Image Original Image

JPEG-LS Decompression on

Figure 7. The CR of JPEG-LS and the proposed algorithm are shown in Table I.

Figure 5. Image before compression

Figure 6. Image after compression

The results of the proposed algorithm were better. The purpose of this algorithm is to exploit the correlation among frames and to achieve a higher compression ratio. TABLE 1.CR OF SINGLE IMAGE Compression Ratio(CR) JPEG-LS JPEG-LS + HCC 2.34 3.25 2.86 3.95 3.54 4.45 3.75 4.57 3.92 4.88 2.50 3.02

Sample Images I1 I2 I3 I4 I5 I6

Head code Decompression

Figure 4. Overall architecture

III. RESULTS AND DISCUSSION To evaluate the performance of the proposed algorithm we have tested it on a single image as well as on a sequence of images. The sequence which we have used is grey-scale MRI images taken from MRTIP database.The images in these sequences are of dimension 512×512 with 8-bit grey-scale image. Figure 5 shows the image before compression and Figure 6 shows the image after compression. The image size was 40.4 KB before compression. By applying our proposed algorithm image is compressed to 7.7 KB. Hence the compression ratio is 5.24.The proposed algorithm was first tested on six individual images. The result of this algorithm was graphically compared with JPEG-LS as shown in

Figure 7.CR of JPEG-LS vs. Proposed algorithm for single image

IV. COMPARISON The proposed algorithm was tested for single image as well as sequence of medical images.MRI sequence was compressed using the proposed algorithm and


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Proceeding of NCIEEE‟13 (ISBN: 978-81-924031-9-9) 21st – 22nd February 2013 Algorithm: Principles and standardization into JPEG-LS,” IEEE Trans. Image Process., vol.9, no. 8, pp. 1309–1324, Aug. 2000.

the result was 20% more reduction than the previous algorithm. V. CONCLUSION

[7].

The algorithm given in this paper makes use of the lossless image compression technique and video compression to achieve higher CR. To achieve high GR the proposed method combines JPEG-LS with interframe coding along with HCC. The technique used in proposed algorithm gives better result than JPEG-LS.Fast block-matching algorithm is used here.Since the full search block matching was time consuming as proposed in [1] we have taken Diamond Search algorithm for block-matching process. Since this paper exploits interframe correlation in the form of MEMC the proposed is compared with [1] and Head Code Compression is used to compress the code-word which gives better result. From the Table 1 it is analyzed that proposed is much better than other existing algorithm. In future, to achieve better correlation we can use low cost correlation estimation method.

[8].

[9].

[10].

[11].

H uang-ChihKuo, Youn-Long Lin, “A Hybrid Algorithm for Effective Lossless Compression of Video Display Frames,” IEEE Trans. On Multimedia, vol.14, no.3, June 2012.

S [13].

S . Zhu and K. K. Ma, “A New Diamond Search Algorithm for Fast Block Matching Motion Estimation,” IEEE Trans. Image Process. vol. 9, no. 2, pp. 287–290, Feb. 2000.

R .Srikanth, A.G.Ramakrishnan, “Context-based Interframe Coding of MR Images”.

[14]. S

.Bhavani, Dr.K.Thanushkodi, “A Survey in Coding Algorithms in Medical Image Compression,”in International Journal on Computer Science and Engineering, vol.02, No.5, 2010, 1429-1434. [5].

M . F. Zhang, J. Hu, and L. M. Zhang, “Lossless Video Compression using Combination of Temporal and Spatial Prediction,” in Proc. IEEE Int. Conf.Neural Newt. Signal Process. Dec. 2003, vol. 2, pp. 1193–1196.

[12].

.E. Ghare, M.A.Mohd .Ali, K.Jumari and M.Ismail, “An Efficient Low Complexity Lossless Coding Algorithm for Medical Images,” in American Journal of Applied Sciences 6 (8): 1502-1508, 2009.

[4].

N .D.Memon and Khalid Sayood, “Lossless Compression of Video Sequences,”IEEE Trans.Commun., vol.44, no.10, pp.1340-1345.

S

[3].

D .Brunello, G.Calvagno, G. A. Mian, and R. Rinaldo, “Lossless Compression of Video using Temporal Information,” IEEE Trans. Image Process, vol. 12, no. 2, pp. 132–139, Feb. 2003.

haou-Gang Miaou, Fu-Sheng Ke, and Shu-Ching Chen, “A Lossless Compression Method for Medical Image Sequences Using JPEG-LS and Interframe Coding,” IEEE Transaction on Information Technology in Biomedicine, vol. 13,No.5,Sep 2009 . [2].

Y . D. Wang, “The Implementation of Undistorted Dynamic Compression Technique for Biomedical Image,” Master‟s thesis,Dept. Electr. Eng., Nat.Cheng Kung Univ., Taiwan, 2005.

REFERENCES [1].

M . Weinberger, G. Seroussi, and G. Sapiro, “LOCO-I: A Low Complexity, Context-based, Lossless Image Compression Algorithm,” in Proc. IEEE Data Compression Conf., Snowbird,UT, Mar./Apr. 1996, pp. 140–149.

M . FerniUkrit, A. Umamageswari and Dr.G.R.Suresh, “A Survey on Lossless Compression for Medical Images,” International Journal of Computer Applications, vol. 31,no.8, pp.0975-8887, October 2011.

G . Schaefer, R. Starosolski, and S. Y. Zhu, “An Evaluation of Lossless Compression Algorithms for Medical Infrared Images,” in Proc. IEEE Eng.Med. Biol. Conf., Sep. 2005, pp. 1673– 1676.

[6].

M . J. Weinberger, G. Seroussi, and G. Sapiro, “The LOCO-I Lossless Image Compression


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