International Journal of Scientific & Engineering Research, Volume 4, Issue 12, December-2013 1284

ISSN 2229-5518

An Improved Medical Image coding technique based on Seam Identification and SPIHT

M.Moorthi, Dr.R.Amutha

AbstractThis paper proposes an improved medical image compression based on seam identification using integer wavelet transform and near loss- less encoding techniques, image retargeting is generally required at the user end of the mobile multimedia communications. This work addresses the increasing demand of visual signal delivery to terminals with arbitrary resolutions, without heavy computational burden to the receiving end. The block based seam energy map is generated in the pixel domain for each input image and the integer wavelet transform (IWT) is performed on the retargeted image. IW T coefficients here are grouped and encoded according to the resultant seam energy map using SPIHT followed by arithmetic coding. At the decoder side, the end user has the ultimate choice for the spatial scalability without the need to examine the visual content; the received images with an arbitrary resolution preserve important content while achieving high coding efficiency for transmission.

Index TermsImage compression, Integer wavelet transform, Seam carving, SPIHT, PSNR

—————————— ——————————

1 INTRODUCTION

edical images are a special category of images in their char- acteristics and purposes. Medical images are generally ac- quired from special equipments, such as computed tomogra- phy (CT), magnetic resonance (MRI), ultrasound (US), X-ray diffraction, electrocardiogram (ECG), and positron emission tomography (PET). The increasing volume of data generated by these medical imaging modalities which justifies the use of different compression techniques to decrease the storage space and efficiency of transfer the images over network for access to electronic patient records. In practice, the compression of medical images must be lossless because a minor loss may result in a serious consequence. Figure 1a depicts the scenario of medical image storage and transmission in telemedicine which has been aiming at reducing bandwidth requirement. The compression methods are classified according to different medical images on the basis of compression ratio and com-
pression quality.

————————————————

M.Moorthi is currently pursuing Ph.D degree program in elec- tronic and communication engineering in Sankara University, Country, India. E-mail: msskm10@gmail.com

Dr.R.Amutha is currently working as professor in ECE in

SSN college, Country, India. E-mail: amuthar@ssn.edu.in

Figure 1a. Usage scenario of telemedicine
There are two types of compression.
1. Lossy compression
2. Lossless compression
Mostly lossless compression is appreciated only by quality, but compression ratio is restricted to this related compression methods. Lossy compression method gives high compression ratio with lesser quality. Researchers still are working to achieve better quality image with high compression rate for telemedicine. Sukhwinder Singh et al [1] have jointly pro- posed a novel technique for medical image compression called adaptive threshold-based block classification. As the result of this, CT, X-ray and ultrasound images are used to evaluate the

IJSER © 2013 http://www.ijser.org

International Journal of Scientific & Engineering Research, Volume 4, Issue 12, December-2013 1285

ISSN 2229-5518

performance and compared with JPEG. Rafeef Abugharbieh et al [2] presented a novel 3-D scalable compression method for medical images with optimized volume of interest (VOI) cod- ing. A new technique of 3D wavelet transform was proposed by Gregorio Bernabe et al [3] for medical videos. Here a lossy compression technique was used which was on the basis of the 3D fast wavelet transform especially for encoding the med- ical videos. Yen-Yu Chen et al [4] has designed a novel medi- cal image compression technique. In this method, high- frequency sub bands are used in good number for reducing the redundancy by promoting the algorithm with modified SPIHT. Srikanth et al [5] has put forth a method for medical image compression, this paper focused mainly on the lossless coding of the images, and then compares the performance of both the uniform and adaptive mesh-based methods. Shyam Sunder et al [6] have jointly framed a novel medical image compression technique by using 3-D Hartley transform. Aaron et al [7] have proposed a method called lossless image com- pression with projection-based and adaptive reversible integer wavelet transforms. Harjeetpal Singh et al [8] presented a hy- brid model which is the combination of several compression techniques. This paper presents DWT and DCT implementa- tion because these are the lossy techniques and also introduce Huffman encoding technique. The results show that the pro-
posed hybrid algorithm performs much better in term of peak-
with Huffman encoder for further compression. Avidan et al [21] presented a novel content-aware image resizing method based on wavelet analysis is to estimate the local energy map of an image by weighing its multi scale sub bands appropri- ately. Based on the energy map, the image is resized by re- peatedly carving out or inserting in a connected path of pixels, which is least significant in terms of the energy. It is an emerg- ing image resizing paradigm. For Seam carving, the im- portance of pixels is defined by an energy function and based upon this function; the image size can be changed by graceful- ly carving-out or inserting pixels in different parts of the im- age. Calculate forward and backward energy and then for- ward energy can achieve better retargeting performance. Here, the experiments conducted to evaluate the spatial-scalability and compression performance of the proposed seam energy based SPIHT codec. Gutierrez et al [22] proposed the compre- hensive perceptual study and analysis of image retargeting. First, create a benchmark of images and conduct a large scale user study to compare a representative number of state-of-the- art retargeting methods. In order to balance the visual quality of retargeted image and the coding efficiency, the wavelet de- composition scale is fixed as two, and, accordingly, each seam in the low-frequency sub band reflects four neighbouring col- umns or rows in the image domain. Hasegawa et al [23] pre-

sented image resizing technique which shrinks an image size

IJSER

signal-to-noise ratio with a higher compression ratio com-
pared to standalone DCT and DWT algorithms. David Wu et
al [9] proposed a coder which outperformed the LOCO coder
while preserving the visual fidelity of the image the heart of
the proposed coder. Sivanantha Raja et al [10] described a
novel approach to medical image compression using the
Curvelet Transform. This method gives higher compression
ratio compared other compression schemes proposed earlier.
Marykutty Cyriac et al [11] proposed a method in which, the
run length is stored in the pixel value itself for single run pix-
els thus reducing the size of the encoded vector. Visually loss- less compression with high PSNR value is obtained. M.Ferni Ukrit et al [12] performed a survey on various lossless com- pressing techniques. Aleksej Avramovic et al [13] described
predictive lossless image compression process. Mrs.S.Sridevi et al [14] reviewed various medical image compression tech- niques such as JPEG2000, JPEG2000 scaling based ROI coding, JPEG2000 MAXSHIFT ROI coding, Shape Adaptive wavelet transform and scaling based ROI, Discrete cosine transform, Discrete wavelet transform, Mesh based coding scheme, Sub band block hierarchical partitioning. Balpreet Kaur et al [15] proposed the ROI is compressed with lossless and lossy tech- niques. Pasumpon Pandian et al [16] proposed a new hybrid image coding algorithm based on a sequencing that is simple to cast and encode the bit planes. Zixiang Xiong et al [17] have jointly proposed a technique called lossy to lossless compres- sion using 3D wavelet transforms. Ramakrishna et al [18] has developed a medical image compression technique called in- ternet transmission of DICOM images with effective low bandwidth utilization. 3-D medical image compression using
3-D wavelet coders was developed by Sriraam et al [19], Jana- ki et al [20] proposed a technique for image compression which uses the Wavelet-based Image Coding in combination

but does not change important region(s) in the original image,

whereas image dilution is the reverse process to image con-

centration. An image retargeting method called seam carving

is used for image concentration. The image concentration (di-

lution) is a pre (post)- processing of image encoder (decoder).

In the experimental results, JPEG/SPIHT with image concen-

tration/dilution presents significant bit rate savings compared

with the original JPEG/SPIHT alone and reconstructed image

qualities are very similar to each other.The objective of this

system is to provide high compression ratio with better quali-
ty in medicines. This paper aims to integrate lossy and lossless compression method for reducing redundancy in medical im- ages.The remainder of this paper is organized as follows. Sec- tion 2 introduces the proposed image coding scheme in detail,
including block-based seam energy map generation, Integer wavelet transform, SPIHT and arithmetic encoding, Section 3 demonstrates the simulation results compared with other rel- evant state-of-the-art image coding schemes, while Section 4 concludes this paper.

2 PROPOSED METHOD

An approach has been made to build the integrated system for medical image compression. The block diagram of proposed method is shown in figure 1b.The system consists of two ma- jor parts. The first part is the removal of noisy pixel in an im- age using seam carving and energy mapping. The second part of the system is to perform compression using integer wavelet transform followed by SPIHT, arithmetic coding. Furthermore, the proposed method is analyzed with PSNR and compression rate. It also significantly reduces the computational time for

IJSER © 2013 http://www.ijser.org

International Journal of Scientific & Engineering Research, Volume 4, Issue 12, December-2013 1286

ISSN 2229-5518


compression and decompression
Medical image
Seem carving
entropy, visual saliency, eye-gaze movement. Figure 2 is gra-

dient image of an input image. The sobel operator was chosen for calculation of the gradient image, but other gradient opera- tors may be used.
Figure 2: Gradient Image

Step 3: Once the gradient image is calculated, the next step is to calculate the energy map image


Energy Map
Integer wavelet transform
Figure 3: Seam Energy map
It is shown in Figure 4.12b, the energy map image needs to be

IJSER

SPIHT coding
Arithmetic coding

Decoding and inverse trans- form

calculated separately for either vertical or horizontal seams, Seams are ranked by energy, with low energy seams being of least importance to the content of the image
Dynamic programming is used in seam carving for computing

seams. If attempting to compute a vertical seam (path) of low- est energy, for each pixel in a row we compute the energy of the current pixel plus the energy of one of the three possible pixels above it. This is better described by figure 4.

Reconstructed image and

Quality measures

Figure1b: Block Diagram of Proposed method

2.1 Seam Carving For Image Retargeting

A seam is defined as a continuous path of pixels running from the top to the bottom of an image in the case of a vertical seam, while a horizontal seam is a continuous line of pixels spanning from left to right in an image. The process allows the user to resize an image by removing a continuous path of pixels (a seam) vertically or horizontally from an image.

2.1.1 Algorithm implementation

Step 1: Read the input image

Step 2: Calculate the weight / density / energy of each pixel.

This can be done by various algorithms: gradient magnitude,
Figure: 4 Computation of seams
Each square represents a pixel, with the top-left value in red representing the energy value of that said pixel. The value in black represents the cumulative sum of energies leading up to and including that pixel. The first row has no rows above it, so the sum (black) is just the energy value of the current pixel (red).The second row, if we look at the second pixel for exam- ple, we see its energy value is 2 (red). If we look above it, it has a choice of 1, 4, or 3 (black). Since 1 is the minimum number of the three values, we ignore the other two and set the sum of the pixel to its energy value which is 2 (red) plus 1 (black).After the above operation is carried out for every pixel in the second row, we go to the third row. We repeat the pro- cess to trace the seam/path. A cost function with a Lagrange multiplier is utilized, and it is formulized as follows:

IJSER © 2013 http://www.ijser.org

International Journal of Scientific & Engineering Research, Volume 4, Issue 12, December-2013 1287

ISSN 2229-5518

CF=Argmin {EB (SB ) +λRB (SB )} (1)
where SB = 2L x2L denotes the width and height in seam unit of the block-based vertical seam, is the optimized block size, and EB and RB are the average forward seam energy induced by removing a block based vertical seam and the required bit stream to transmit the pixel value (or transformed coefficients) and side information of the seams. For an N X M original im- age, EB and RB can be calculated as
EB (S B )=Σ J MB (N/2L,j)/(M/2L) (2) RB (SB )=Rc(SB )+Rs(SB ) (3)

Input Sj

Evenj-1

Split Predict

Oddj-1

Approximations part

Sj-1

+

Update

Details part dj-1

Where MB (N/2 L,J) represents the last row of the cost matrix MB in, J is the column index and J €[0,M/2L]; (Rc(SB )) and Rs(SB ) are the coded bits generated by encoding the wavelet coefficients and the position information of the corresponding seam paths, respectively. For the vertical seams, each entry in MB is updated by
MB (i/2L,j/2L)=W(i/2L,j/2L)+min{MB ((i-2L)/2L,(j-2L)/2L)+CL B{MB ((i-
Figure 5-Block diagram of forward transform
The lifting approach [24] allows fast, efficient, and in-place calculation of the wavelet transform. Applying wavelet trans- form to Sj divides it into coarse Sj-1 values and detail dj-1 val- ues. Two operations are used for performing the lifting trans- form. They are Predict and Update.
Lifting step is the operation of obtaining the differences from
the prediction. After the prediction step, the update step is

IJSER

2L)/2L,(j)/2L)+CU B{MB ((i-2L)/2L,(j+2L)/2L)+CR B (4)
where CL B ,CU B, CR B are the costs for the three possible connec- tion paths of a block-based verticalseam. W (.) is a weighting parameter that can be used to balance seam removal/insertion
CL B= (5)

Step 4: Then remove the seams from the image, reducing the size of the image.

followed. In the update step, the even values are updated and

the odd samples become the scaling coefficients. These scaling coefficients pass on to the next stage of transform. Finally the odd elements are replaced by the difference and the even ele- ments by the averages. The computations in the lifting scheme are done in place which saves lot of memory and computation time. The lifting scheme provides integer coefficients and so it is exactly reversible.

Approximations

2.2 Integer wavelet Transform

Recently, wavelets [23] have been used frequently in medical image processing. The biorthogonal wavelet transform is taken in this method because it is symmetric, almost orthogonal and gives the best results for medical images. The image is decom- posed into different frequency components. It is performed by applying 1-D DWT along the rows of the image first, and, then, the results are decomposed along the columns. The fol- lowing sequence of operations is done for performing Lifting:
1. Split Sj into Evenj-1 and Oddj-1

part Sj-1

Details part dj-1

Up- d

Pre- di

+

Evenj-1

Oddj-1

Merge

Output

O j

2. dj-1 = Oddj-1 – Predict (Evenj-1 )
3. Sj-1 = Evenj-1 + Update (dj-1 )
The Fig 5 shows the forward transform visually.
Figure 6-Block diagram of inverse transform
The Fig 6 shows the inverse transform visually. The inverse operation is done by performing the following sequence of operations:
1. Evenj-1= Sj-1- U ( dj-1 )
2. Oddj-1 = dj-1 + Predict (Evenj-1 )
3. Oj = Sj = Merge (Evenj-1 , Oddj-1 )

IJSER © 2013 http://www.ijser.org

International Journal of Scientific & Engineering Research, Volume 4, Issue 12, December-2013 1288

ISSN 2229-5518

The original signal is retrieved back by the inverse transform
by reversing the operations of the forward transform and re- placing the split operation with a merge operation. The lifting process provides spatial de-correlation of image data.

2.3 Set Partitioning In hierarchal Trees

SPIHT (Singh et al 2012) is a medical image coder that ex- ploits the inherent similarities across the sub bands in an inte- ger wavelet decomposition of an image. The SPIHT (Set Parti- tioning in Hierarchical Trees) algorithm is a fast and efficient technique for image compression and encryption. SPIHT gen- erally operates on an entire image at once. The method de- serves special attention because it provides the advantages like highest image quality, progressive image transmission, fully embedded code file, simple quantization algorithm, fast coding/decoding, completely adaptive, lossless compression, exact bit rate coding and error protection. In the SPIHT algo- rithm, the image is first decomposed into a number of sub bands by means of hierarchical wavelet decomposition. The sub band coefficients are then grouped into sets known as spa- tial-orientation trees, which efficiently exploit the correlation
between the frequency bands. The coefficients in each spatial
• LSP: list of significant pixels (significant coef-
ficients)
SPIHT defines 2 types of trees
• Type M: check all descendants for signifi- cance
• Type P: check all descendants except imme-
diate children

Step 1: Initialization

Compute initial threshold
LIP: all root nodes (in low pass sub band)
LIS: all trees (type M)
LSP: empty

Step 2: Sorting Pass

Check significance of all coefficients in LIP:
If significant, output 1 followed by a sign bit
& move it to LSP.
If insignificant, output 0.

Step 3: Check significance of all trees in LIS

For type-M tree:
If significant, output 1 & proceed to code its
children
If a child is significant, output 1, sign bit, &

IJSER

orientation tree are then progressively coded from the most
significant bit-planes (MSB) to the least significant bit-planes
(LSB), starting with the coefficients with the highest magni- tude and at the lowest pyramid levels.
add it to LSP.
If a child is insignificant, output 0 and add it to the end of LIP.
If the child has descendants, move the tree to the end of LIS as type L, otherwise remove it from LIS.

If insignificant, output 0. For type-P tree :
If significant, output 1, add each of the chil- dren to the end of LIS as type M and remove the par- ent tree from LIS.
If insignificant, output 0.

Step 4: Refinement pass

For each element in LSP – except those just added above, output the nth most significant bit of coeffi- cient.
Figure 7: Structure of SPIHT encoding method
The original image is decomposed into sixteen sub bands which are shown in Fig 7. The SPIHT multistage encoding process employs three lists and sets: SPIHT has 3 lists
• LIP: list of insignificant pixels (individual in- significant coefficients)
• LIS: list of insignificant lists (insignificant
trees)
End loop over LSP.

Step 5: Decrease the threshold by a factor of 2. Go to Step 2.

The rate can be controlled by using this algorithm. Till the de- sired distortion value is reached, the encoder continues esti- mating the progressive distortion reduction. The same number of lists is replicated by the SPIHT decoder algorithm and also the ordering information is recovered easily.

2.4 Adaptive Arithmetic Encoder

It is the method of representing frequently occurring pixel values into fewer bits. In arithmetic coding (suneetha et al

2007), single codes are used to represent a string of character thereby reducing the file size. Arithmetic coding is a form

IJSER © 2013 http://www.ijser.org

International Journal of Scientific & Engineering Research, Volume 4, Issue 12, December-2013 1289

ISSN 2229-5518

of variable-length entropy encoding used in lossless data
compression. Normally, a string of characters such as the words "hello there" is represented using a fixed number of bits per character, as in the ASCII code. When a string is converted to arithmetic encoding, frequently used characters will be stored with fewer bits and not-so-frequently occurring charac- ters will be stored with more bits, resulting in fewer bits used in total. Arithmetic coding differs from other forms of entropy encoding such as Huffman coding in that rather than separat- ing the input into component symbols and replacing each with a code, arithmetic coding encodes the entire message into a single number, a fraction n where (0.0 ≤ n < 1.0).In arithmetic coding, a message is encoded as a real number in an interval from one to zero. Arithmetic coding typically has a better compression ratio than Huffman coding, as it produces a sin- gle symbol rather than several separate codeword’s. Arithme- tic coding is a lossless coding technique. There are a few dis- advantages of arithmetic coding. One is that the whole code- word must be received to start decoding the symbols, and if there is a corrupt bit in the codeword, the entire message could become corrupt. Another is that there is a limit to the precision of the number which can be encoded, thus limiting

Symbol

New "a" Interval

a

[0.0, 0.04)

b

[0.04, 0.1)

c

[0.1, 0.102)

d

[0.102, 0.2)

Step 3: Repeat the process until the maximum precision of the machine is reached, or all symbols are encoded. To encode the next character "b", we use the "a" interval created before, and zoom into the subinterval "b", and use that for the next step.

Table 3 calculation of new cumulative probability after symbol

"b" and "d" has occurred

the number of symbols to eIncode Jwithin a codSeword. There ER
also exist many patents upon arithmetic coding, so the use of
some of the algorithms also call upon royalty fees. Here is the
arithmetic coding algorithm, with an example to aid under- standing.

Step 1: Start with an interval [0, 1), divided into subintervals of all possible symbols to appear within a message. Make the size of each subinterval proportional to the frequency at which it appears in the message. Eg:

Table 1 calculation of cumulative probability

Symbol

Probability

Interval

a

0.2

[0.0, 0.2)

b

0.3

[0.2, 0.5)

c

0.1

[0.5, 0.6)

d

0.4

[0.6, 1.0)

Step 2: When encoding a symbol, "zoom" into the current in- terval, and divide it into subintervals like in step one with the new range. Example: suppose we want to encode "abd". We "zoom" into the interval corresponding to "a", and divide up that interval into smaller subintervals like before. We now use this new interval as the basis of the next symbol encoding step.

Table 2 calculation of new cumulative probability after symbol

"a" has occurred

And lastly, the final result is:

Symbol

New "d" Interval

a

[0.1608, 0.16864)

b

[0.16864, 0.1804)

c

[0.1804, 0.18432)

d

[0.18432, 0.2)

Step 4: Transmit some number within the latest interval to send the codeword. The number of symbols encoded will be stated in the protocol of the image format, so any number within [0.1608, 0.2) will be acceptable. To decode the message, a similar algorithm is followed, except that the final number is given, and the symbols are decoded sequentially from that order.

3. Results and Discussions

The three experimental works are reported to evaluate the performance of the proposed algorithm. The proposed algo- rithm was tested on the real images acquired from patients. Each image had a size of 256x256 pixels. All the algorithms were implemented in MATLAB 9.0 on a Pentium IV PC (with CPU 2.8G and 512M memory).

3.1 Quality Measures

The quality of the image or video data [24] to be measured at the output of the decoder, entropy, mean square error (MSE)

IJSER © 2013 http://www.ijser.org

International Journal of Scientific & Engineering Research, Volume 4, Issue 12, December-2013 1290

ISSN 2229-5518


and peak to signal to noise ratio (PSNR), time period and compression ratio are often used.

3.1.1 Entropy

An entropy-based cost function is used to measure the com- pactness of signal (image) representation. The entropy (E) is defined as

(6) where s is the set of processed coefficients and p (e) is the probability of processed coefficients.

3.1.2 Peak signal noise ratio (PSNR)

The peak signal-to-noise ratio is defined as the ratio between
signal variance and reconstruction error variance. MSE is cal- culated as:
MSE=σq 2= (7) Where the sum over i,j denotes the sum over all pixels in the image and M*N is the number of pixels in each image. Xij - original image,Yij -Reconstructed image. The PSNR in terms of
decibels (dBs) is given by:

Figure 7: CR value for different medical images using various methods

Table 4: Quality measures for different medical images using proposed method


PSNR= IJS(8) ER

3.1.3 Time period

The time period is defined as the time taken by each method to undergo compression and decompression stages involved in the algorithm.

3.1.4 Compression ratio

The capability of compression system is characterized by compression ratio which is calculated as

(9)
The compression ratio values calculated for the set of simulat- ed medical images. Thus it could be seen from the simulated data that the proposed algorithm has the best compression ratio than the existing algorithms. The simulated output of proposed method are discussed below, Fig 9 shows the input medical images for the compression. Fig 10 shows the Edge detected image. Fig 11 shows the Energy mapped image. Fig
12 shows the Retargeted (Resize) image. Fig 13 shows the out-
puts of the integer wavelet transform of an image and finally fig 14 shows the reconstructed images using the proposed method.

Table 5: PSNR value for Brain medical images using various methodologies

IJSER © 2013 http://www.ijser.org

International Journal of Scientific & Engineering Research, Volume 4, Issue 12, December-2013 1291


ISSN 2229-5518

Fig 11: Energy mapped image

Fig 12: Retargeted (Resize) image

Figure 8: PSNR value for different medical images using various methodologies

It could be observed from the table that the PSNR for different medical images using the proposed method is very high as com-

pared to the existing method [31] and also the time involved is

Fig 13: Integer wavelet transform of image

just 7.39 seconds for the proposed algorithm while the existing algorithm takes 17 seconds. Hereby it is concluded that an existing coding[31] takes more time for executing the entire process while the proposed algorithm takes very less time comparatively.

The quality measures such as compression ratio (CR), peak signal to noise ratio (PSNR) are plotted for the existing and the proposed method as shown in Figure 7 – 8. Figure 7 shows the CR for the five input medical images from which it is clearly evident that the CR is greater in the proposed method than the existing method. It is clearly evident from table 5, that the PSNR value is 46.2 dB for the proposed method using the SC+IWT+SPIHT+AC method, while it is around 45 dB for the existing method. This parameter needs to be close to 50dB which is best achieved by the proposed method.

Fig 9: Input medical images

Fig 10: Edge detected image

Fig 14: Reconstructed image

4. Conclusion

This method provide better compression ratio with good quality and reduce the processing time based on seam carving technique followed by integer wavelet transform and set parti- tioning in hierarchical tree, arithmetic coding. Also, the seam carving process was presented to retarget the image correspond- ing to display set size. Here near lossless embedded coding used to reduce the redundancy, performance was analyzed through determining the image quality after decompression, compression ratio and execution time. This method can be further enhanced by modifying the transformation technique for improving the effi- ciency of the technique.

REFERENCES

[1] Sukhwinder Singh, Vinod Kumar, H.K.Verma, “Adaptive threshold based block classification in medical image compression for teleradiology

”,Computers in Biology and Medicine ,Vol.37, pp. 811 – 819,2007

IJSER © 2013 http://www.ijser.org

International Journal of Scientific & Engineering Research, Volume 4, Issue 12, December-2013 1292

ISSN 2229-5518

[2] Rafeef Abugharbieh, Victor Sanchez, and PanosNasiopoulos,“3-D Scal- able Medical Image Compression With Optimized Volume of Interest Coding”, IEEE Transactions On Medical Imaging, Vol. 29, No. 10, October 2010

[3] Gregorio Bernab , Jose M. García , José González ,”A lossy 3D wavelet transform for high-quality compression of medical video”, The Journal of Systems and Software, Vol. 82, pp.526–534,2009

[4] Yen-Yu Chen,” Medical image compression using DCT-based sub band

decomposition and modified SPIHT data organization”, International journal of medical informatics, Vol.76, pp. 717–725, 2007

[5] R. Srikanth, A.G. Ramakrishnan, “Contextual encoding in uniform and adaptive mesh based lossless compression of MR images”, IEEE Transac- tions on Medical Imaging,Vol. 24,pp.1199–1206, 2005.

[6] R. Shyam Sunder, C. Eswaran, N. Sriraam, “Medical image compres- sion using 3-D Hartley transform”, Comput. Biol. Med, Vol.36, pp.958–973,

2006.

[7] A.T. Deever, S.S. Hemami , ”Lossless image compression with projec- tion-based and adaptive reversible integer wavelet transforms”, IEEE

Transactions on ImageProcessing. vol.12 , pp.489–499. 2003.

[15] Pasumpon Pandian, A. and S.N. Sivanandam, “Hybrid Algorithm for Lossless Image Compression using Simple Selective Scan order with Bit Plane Slicing”, Journal of Computer Science 8 (8): 1338-1345, 2012 ISSN 1549-

3636

[16] Harjeetpal singh and Sakhi Sharma, “Hybrid Image Compression Using DWT, DCT & Huffman Encoding Techniques”, International Journal of Emerging Technology and Advanced Engineering, ISSN 2250-2459, Vol- ume 2, Issue 10, October 2012

[17] Z. Xiong, X. Wu, S. Cheng, J. Hua, “Lossy to lossless compression of

medical volumetric data using 3D integer wavelet transforms”, IEEE Transactions on Medical Imaging ,vol.22, pp.459–470, 2003.

[18] B. Ramakrishnan, N. Sriraam, “Internet transmission of DICOM im- ages with effective low bandwidth utilization”, Journal of Digital Signal Process, Vol.16, pp. 825–831,2006.

[19] N. Sriraam, R. Shyamsunder.” 3-D medical image compression using

3-D wavelet coders”, Elsevier on Digital Image Processing,Vol.21,pp.100-

109,2010.

[20] Janaki. R and Dr.Tamilarasi, “A,Still Image Compression by Combining

IJSER

[8] David Wu, Damian M. Tan, Marilyn Baird, John DeCampo, Chris

White, and Hong Ren Wu, “Perceptually Lossless Medical Image Coding”, IEEE Transactions On Medical Imaging, Vol. 25, No. 3, March 2006

[9] A. Sivanantha Raja, D. Venugopal and S. Navaneethan, “An Efficient

Coloured Medical Image Compression Scheme using Curvelet Transform”, Eu- ropean Journal of Scientific Research ISSN 1450-216X Vol.80 No.3 (2012), pp.416-422

[10] Marykutty Cyriac and Chellamuthu C., “A Novel Visually Lossless Spatial Domain Approach for Medical Image Compression”, European Journal of Scientific Research ISSN 1450-216X Vol.71 No.3 (2012), pp. 347-351

[11] M.Ferni Ukrit ,A.Umamageswari ,Dr.G.R.Suresh , ―A Survey on

Lossless Compression for Medical Images ‖ International Journal of Com- puter Applications (0975 – 8887) Volume 31– No.8, October 2011

[12] Aleksej Avramovic and Slavica Savic “Lossless Predictive Compression of Medical Images”, Serbian Journal Of Electrical Engineering Vol. 8, No. 1, February 2011, 27-36

[13]Mrs.S.Sridevi ,Dr.V.R.Vijayakuymar and Ms.R.Anuja, “A Survey on Various Compression Methods for Medical Images”, I.J. Intelligent Systems and Applications, 2012, 3, 13-19 Published Online April 2012 in MECS

[14] Balpreet Kaur,Deepak Aggarwal, and Gurpreet Kaur, 4Amandeep

Kaur’ “Efficient Image Compression based on Region of Interest”, IJCST Vol. 2, Issue 1, March 2011 ISSN:2229-4333( Print )|ISSN:0976- 8 491

EZW Encoding with Huffman Encoder”, International Journal of Computer

Applications (0975 – 8887) Volume 13– No.7, January 2011

[21] Avidan S. and Shamir A.’A seam carving for content-aware image resizing’ (2007) ACM Trans. Graphics, vol. 26, no. 3, pp. 10–19.

[22] Gutierrez D Rubinstein.M Shamir and A. Sorkine O.(2010) ‘A compar- ative study of image retargeting ‘ACM Trans. Graphics, vol. 29, no. 6,pp.

160–168

[23] Hasegawa M Kato S. and Tanaka Y. (2010) ‘Image coding using con- centration and dilution based on seam carving with hierarchical search’ in Proc. IEEE Int. Conf.Acoust., Speech. Signal Process.pp. 1322–1325

[24] E.Salma,J.P. Josh Kumar , ‘Efficient Image Compression based on

Seam Carving for Arbitrary Resolution Display Devices’, International

Journal of Computer Applications Volume 68– No.4, April 2013.pp.37-40

[25] M. Moorthi, R. Amutha , “Medical Image Compression using Con- tourlet with SVD Transform and Huffman coder”, 2013, viruksha Jour- nal.(Article in press)

[26] Heikkila M., Pietikainen M. and Heikkila J., “A Texture-Based Method for Detecting Moving Objects,” The 15th British Machine Vision Confer- ence I: pp. 187-196, 2004.

[27] Turtinen M. and Pietikainen M., “Visual Training and Classification of

Textured Scene Images,” 3rd International Workshop on Texture Analysis and Synthesis pp. 101-106, 2003.

[28] Ojala T., Pietikainen M., Maenpaa T., “Multiresolution Gray-Scale and

IJSER © 2013 http://www.ijser.org

International Journal of Scientific & Engineering Research, Volume 4, Issue 12, December-2013 1293

ISSN 2229-5518

Rotation Invariant Texture Classification with Local Binary Patterns,” IEEE Transactions on Pattern Analysis and Machine Intelligence 24 pp.

971-987, 2002.

[29] Ramana Reddy B.V., Radhika Mani M. Sujatha B., Vijaya Kumar V., “Texture Classification Based on Random Threshold Vector Technique,” International Journal of Multimedia and Ubiquitous Engineering Vol. 5, No. 1, January, 2010

[30]Mikael Rousson, Nikos Paragios, and Rachid Deriche. Implicit active shape models for 3d segmentation in mr imaging. Medical Image Compu- ting and Computer-Assisted Intervention –MICCAI 2004, pages 209–216, 2004. [31] Daniel Cremers,Mikael Rousson, and RachidDeriche. A review of statistical approaches to level set segmentation: Integrating color, texture, motion and shape. Int. J. Comput.Vision, 72(2):195–215, 2007.

[32]Rosin, P.. Training Cellular Automata for Image Processing. IEEE

Transactions on Image Processing, Vol. 15, No. 7, pp. 2076-2087. (2006). [33]Cheng, H. D., Shi, X.J., Min, R., Hu, L.M., Cai, X.P. & Du, H.N. Ap-

proaches for Automated Dectection and Classification of Masses in

He has published and presented papers in National and Inter- national Conference in the area of Image processing. He has been the reviewer for 2012 & 2013 IET image processing. His research interests are Image Segmentation, Image Compres- sion, Neural network, Fuzzy logic, microprocessor and micro- controller.

Dr.R.Amutha, Professor, ECE department graduated from Thiagarajar college of Engi- neering in the year 1987. She obtained her M.E degree from PSG college of Technology. She got her Ph.D from Anna University in
2006. She has 24 years of teaching and 10
years research experience. Her research area includes coding theory, Wireless communication network and Image pro- cessing. She published 3 International and 2 national journal papers. She has 20 International and national conference pa- pers to her credit. She reviewed three international journal papers. She is a recognized research supervisor of Anna Uni- versity and SCSVMV University for Ph.D and M.S (by re- search). She is supervising 9 Ph.D research scholars.

Mammograms. Pattern Recognition, Vol. 39, pp. 646-668,(2006).

[34] Wongthanavasu, S. & Tangvoraphongchai, V. CA-based Algorithms

and Its Application in Medical Image Processing. Proceedings of ICIP 2007

14th International Conference on Image Processing, pp. III-41-44, ISBN: 1-4244-

1437-7, ISSN: 1522-4880, San Antonio, Texas, U.S.A. September 16-19, 2007. [35] Chen, J.-C., Yeh, C.-M. & Tzeng, J.-E. ,Pattern differentiation of glan- dular cancerous cells and normal cells with cellular automata and evolu- tionary learning. Expert Systems with Applications, Vol. 34, Issue 1, , pp. 337-

346, 2008

[36] M. Moorthi, R. Amutha , “ Medical Image Compression using Adap- tive Neural Network”, IEEE-International Conference on Emerging Trends in Science, Engineering and Technology (INCOSET), pp 222- 227, 2012.

M.Moorthi pursuing his Ph.D program at Sri Chandrasekharendra Saraswathi Viswa Ma- havidyalaya University, Kanchipuram. He completed his B.E degree at Arulmigu Meenakshi Amman College of Engineering, Kanchipuram, in Electronics and Communi-

cation Engineering in the year 2001 and M.E - Medical Elec-
tronics in the year 2007 at Anna University, Gundy campus,
Chennai, India. He has 12 years of teaching experience and he is currently working as Assistant Professor in the department of Electronics and Communication Engineering at Prathyusha Institute of Technology and management, Chennai. He is a member of the Institute of Electrical and Electronics Engineers (IEEE), Indian Society for Technical Education (ISTE), IETE.

IJSER © 2013 http://www.ijser.org