robust watermarking and compression for medical images based on genetic algorithms

Information Sciences 175 (2005) 200–216

www.elsevier.com/locate/ins

Robust watermarking and compressionfor medical images basedon genetic algorithms

Frank Y. Shih *, Yi-Ta Wu

Computer Vision Laboratory, College of Computing Sciences, New Jersey Institute

of Technology, Newark, NJ 07102, United States

Abstract

A ROI (region of interest) of a medical image is an area including important infor-

mation and must be stored without any distortion. In order to achieve optimal compres-

sion as well as satisfactory visualization of medical images, we compress the ROI by

lossless compression, and the rest by lossy compression. Furthermore, security is an

important issue in web-based medical information system. Watermarking skill is often

used for protecting medical images. In this paper, we present a robust technique embed-

ding the watermark of signature information or textual data around the ROI of a med-

ical image based on genetic algorithms. A fragile watermark is adopted to detect any

unauthorized modification. The embedding of watermark in the frequency domain is

more difficult to be pirated than in spatial domain.

� 2005 Elsevier Inc. All rights reserved.

Keywords:Watermarking; Medical imaging; ROI; SPIHT; Image compression; Genetic algorithms

0020-0255/$ - see front matter � 2005 Elsevier Inc. All rights reserved.

doi:10.1016/j.ins.2005.01.013

* Corresponding author. Tel.: +1 973 596 5654; fax: +1 973 596 5777.

E-mail address: [email protected] (F.Y. Shih).

mailto:[email protected]

F.Y. Shih, Y.-T. Wu / Information Sciences 175 (2005) 200–216 201

1. Introduction

In recent decades with the rapid development of biomedical engineering,

digital medical images have been becoming increasingly important in hospitals

and clinical environment. Concomitantly, traversing medical images between

hospitals exists complicated network protocol, image compression and securityproblems. Many techniques have been developed to resolve these problems.

For example, HIS (hospital information system) and PACS (picture arching

and communication system) are currently the two primary data communica-

tion systems used in hospitals. Although HIS may be slightly different between

hospitals, data can be exchanged based on the standard—HL7 (health level se-

ven). Similarly, PACS transmits medical images using the standard—DICOM

(digital imaging and communications in medicine). Furthermore, IEEE 1073

was published in order to set a standard for measured data and signals fromdifferent medical instruments.

Some techniques, such as HIS and PACS, were developed to provide an effi-

cient mechanism in dealing with the outburst medical images. In order to en-

hance the performance, we need to compress the image data for efficient

storage and transmission. Lossless compression is adopted for avoiding any

distortion when the images are needed for diagnosis. Otherwise, lossy compres-

sion can achieve higher compression rate. Since the quality of medical images is

crucial in diagnosis, lossless compression is often used. However, if we can uti-lize the domain knowledge of specific types of medical images, higher compres-

sion ratio could be achieved without losing any important diagnostic

information. In order to obtain the higher compression rate, a hybrid-compres-

sion method [1,2] was developed by compressing the ROI (region of interest)

with lossless compression, and the rest with lossy compression.

For the purpose of security, watermarking [3,4] is adopted for copyright

protection, broadcast monitoring, and data authentication. The embedded

data could be signature images or specific textual data. There are two waysof performing watermarking, one in spatial domain, and the other in frequency

domain. In the spatial domain [5,6], we can simply insert watermark into a host

image by changing the gray levels of some pixels in the host image, but the in-

serted information may be easily detected using computer analysis. In the fre-

quency domain [7–9], we can insert watermark into the coefficients of a

transformed image, for example, using the DFT (discrete Fourier transform),

DCT (discrete cosine transform) and DWT (discrete wavelet transform) and

that is difficult to detect. However, the embedded watermark in the coefficientsof the transformed image will be somewhat disturbed in the process of trans-

forming the image from its frequency domain to spatial domain because of

deviations in converting real numbers into integers. We developed a technique

to correct the errors by using genetic algorithm [10]. In this paper, we propose

an advanced efficient technique to correct the errors.

202 F.Y. Shih, Y.-T. Wu / Information Sciences 175 (2005) 200–216

In order to protect the copyright of medical images, the watermark is

embedded surrounding their ROI parts. Meanwhile, the embedded watermark

is pre-processed by SPIHT (set partitioning in hierarchical trees) [11] for

robustness. This paper is organized as follows. In Section 2, we present the

overview of our proposed technique. Section 3 introduces the technique for

rounding errors reduction using genetic algorithms. Experimental results areshown in Section 4. Finally, conclusions are made in Section 5.

2. Overview of our proposed technique

In order to achieve higher compression rate without distorting the impor-

tant data in a medical image, we select the ROI of a medical image and com-

press it by lossless compression and the rest by lossy compression. For thepurpose of protecting medical images and maintaining their integrity, we

embed information watermark (signature image or textual data) and fragile

watermark into the frequency domain surrounding the ROI part. Meanwhile,

the embedded information watermark is pre-processed into a bitstream

depending on different types of watermark.

2.1. The signature image

2.1.1. Encoding procedure

Fig. 1 shows the encoding procedure when the watermark is a signature

image. The host image (medical image) is seperated into two parts, ROI

and non-ROI. We embed the signature image and fragile watermark into

the non-ROI part by genetic algorithms. Note that we pre-process the signa-

ture image by SPIHT in order for its reconstruction to perceptual satisfaction.

Last, we obtain the watermarked image by combining the embedded non-ROI

and ROI.

Fig. 1. The encoding procedure of embedding a signature image.


2.1.2. Decoding procedure

Fig. 2 shows the decoding procedure. The non-ROI part is selected from the

watermarked image, and is transformed by DCT. Last, we obtain the fragile

watermark and a set of bitstreams by extracting data from the specific positions

of the coefficients in the frequency domain. We can decide the integrity of the

medical image by checking the fragile watermark, and obtain the signature im-age by reconstructing the bitstream using SPIHT.

2.1.3. SPIHT (set partitioning in hierarchical trees) compression

The SPIHT is a zerotree structure based on DWT (discrete wavelet trans-

form). The first zerotree structure, EZW (embedded zerotree wavelet), was

published by Shapiro [12] in 1993. SPIHT uses a bit allocation strategy in

essential and produces a progressively embedded scalable bitstream.

Fig. 3 shows an example of SPIHT. Fig. 3(a) is the original 8 · 8 image. Weobtain the transformed image as Fig. 3(b) by DWT. Fig. 3(c) lists the bit-

streams generated by applying SPIHT three times. We reconstruct the image

by using these bitstreams and the result is shown in Fig. 3(d).

2.2. The textual data

2.2.1. Encoding and decoding procedures

Fig. 4 shows the encoding procedure when the watermark is textual data.The procedure is similar to the signature image encoding except the encryption

of textual data is used. Its decoding procedure is shown in Fig. 5.

2.2.2. The encryption scheme in textual data

There are many techniques for encrypting textual data. Generally, the main

idea is translating the textual data of plain text into secret codes of cipher text.

There are two types of encryption: asymmetric and symmetric encryptions.

An easy way to achieve encryption is bit shifting. That is, we consider acharacter as a byte value and shift its bit position. For example, the byte value

Fig. 2. The decoding procedure of embedding a signature image.

Fig. 3. The example of SPIHT.

Fig. 4. The encoding procedure of embedding textual data.

Fig. 5. The decoding procedure of embedding textual data.



of a letter �F� is �01000110�. We can change it to be �d� as �01100100� by shiftingthe left-most 4 bits to the right side.

An encryption method by taking the logarithm of ASCII codes was devel-

oped in [16]. Their encryption algorithm can be mathematically stated as

T e ¼ ðlogðT o � 2Þ � 100Þ � 300;

where Te denotes the encrypted text and To denotes the ASCII code of the ori-

ginal text. The decrypted text can be obtained by

T o ¼ expT e þ 300100

� log 2� �

:

Note that, the encrypted information (Te) is stored as an integer.

2.3. The algorithm for our proposed technique

Let H be the original host image with size K · K, which is separated intoHROI (ROI) and HNROI (non-ROI) with sizes of N · M and K · K � N · M,

respectively. Let S and T denote a signature image with size ofW · W and tex-

tual data, respectively. SB is a bitstream obtained by compressing S in SPIHT

compression or encoding To by the encryption technique. WF is the fragile

watermark. HWROI is obtained by adjusting the pixel values of HNROI in which

we can extract SB and W F from some specific positions of coefficients of the

frequency domain in HWROI. HFinal is the final image by combining HROI

and HWROI.

Algorithm

1. Separate the host image, H, into HROI and HNROI.

H = {h(i, j), 0 6 i, j < K}, where h(i, j) 2 {0,1,2, . . ., 2L � 1} and L is the

number of bits used in the gray level of pixels.

HROI = {hROI(i, j), 06 i <N, 06 j <M}, where hROI(i, j) 2 {0,1,2, . . .,2L � 1}.HNROI = {hNROI(i, j)}, where hNROI(i, j) 2 {0,1,2, . . ., 2L � 1},

2. For signature image:

Transform S to SB by SPIHT.

S = {s(i, j), 0 6 i, j <W}, where s(i, j) 2 {0,1,2, . . ., 2L � 1}.SB is the bitstream consisting of bits 0 and 1.

For textual data:

Encode T to SB by the encryption technique.

3. Adjust HNROI by genetic algorithms to obtain HWROI in which we can

extract SB and WF from the coefficients of its frequency domain.

4. Combine HROI and HWROI to obtain the final image HFinal.


Note that we can embed not only the information image, but also the fragile

watermark into the host image. If there exist more than one regular ROIs, we

can record the following information for each ROI: the top-left corner coordi-nates, the width, and the height. The watermark image can be placed in a group

of 8 by 8 blocks that are extracted from the outer layer of each ROI. For an

irregular polygon or circular shape of ROI, we need to record the starting

and ending positions of each row in ROI in order to extract the data from

the outer layer of its boundary.

3. The novel scheme based on genetic algorithms

3.1. The traditional scheme

Several approaches used watermarking in the frequency domain, for exam-

ple, JPEG-based [13], spread spectrum [14,15], and content-based approaches

[9]. How can we embed data into the frequency domain of a host image to ap-

pear unperceivable? The transformation functions often-used are DCT, DWT,

and DFT. Generally, we can insert data into the coefficients of the transformedimage. As shown in Fig. 6, we embed watermark into the coefficients of the

transformed host image.

Let H denote a host image with size N · N and W denote a watermark im-

age with sizeM · M. Let Hm andWn be the subdivided images from H andW,

respectively,Hm_DCT be the image transformed from Hm by DCT,Hm_F be the

image combined by Hm_DCT andWn in the frequency domain, and HF_IDCT be

the image transformed from Hm_F by IDCT. Let � denote the operation thatsubstitutes bits of watermark for LSBs (least significant bits) of the host image.The algorithm is described as follows.

Algorithm

1. Divide the host image into sets of 8 · 8 blocks.H = {h(i, j), 0 6 i, j < N}.

Hm = {hm(i, j), 0 6 i, j < 8}, where hm(i, j) 2 {0,1,2, . . . , 2L � 1} and m is the

total number of the 8 · 8 blocks.

Fig. 6. The traditional scheme in frequency domains.


2. Divide the watermark image into sets of 2 · 2 blocks.W = {w(i, j), 0 6 i, j < M}.

Wn = {wn(i, j), 0 6 i, j < 2}, where wn(i, j) 2 {0,1} and n is the total number

of the 2 · 2 blocks.3. Transform Hm to Hm_DCT by DCT.

4. Insert Wm into the coefficients of Hm_DCT.Hm_F = {hm_F(i, j) = hm_DCT(i, j) � wm(i, j), 0 6 i, j < 8}, hm_DCT(i, j) 2 {0,1,. . . , 2L � 1}.

5. Transform Hm_F to HF_IDCT by IDCT.

Note that all elements of HF_IDCT are real numbers. Therefore, we need to

convert real numbers into integers. Rounding method is often-used, but it will

cause errors that we can not precisely extract watermark from the watermarked

image.

3.2. Genetic algorithms

Genetic algorithms (GAs), introduced by Hollard [17] in his seminal work,

are commonly used as adaptive approaches that provide a randomized, paral-

lel, and global search method based on the mechanics of natural selection and

genetics in order to find solutions. GAs are different from the traditional opti-

mization and searching procedures in four ways [18]: (a) GAs work with acoded parameter set, not the parameters themselves, (b) GAs search from ran-

domly selected points, not from a single point, (c) GAs use objective function

information, and (d) GAs use probabilistic transition rules, not deterministic

ones.

Although there are many possible variants of genetic algorithms [19,20], the

fundamental is based on the SGA (simple genetic algorithm) [21]. In general,

genetic algorithms start with some randomly selected genes, i.e., the first gen-

eration, called population. Each individual in the population corresponding to asolution in the problem domain is called chromosome. An objective, called fit-

ness function, is used to evaluate the quality of each chromosome. The chromo-

somes with high quality will survive and form the population of the next

generation. By using the following three operators, reproduction, crossover,

and mutation, a new generation can be recombined in order to find the best

solution. The process will repeat until a predefined condition is satisfied, or a

constant number of iterations are reached.

3.3. The scheme based on genetic algorithms

As mentioned in Section 3.1, the simple rounding method will distort the

embedded watermark. We proposed the rounding errors correction by using


genetic algorithms to decide the strategy when real numbers are converted into

integers in [10]. The scheme is shown in Fig. 7.

Let HDCT be the image where H is transformed into the frequency domain

by DCT. HWF is the watermarked image where HDCT and W are combined in

the frequency domain. HWRS is the watermarked real-number image where

HDCT is transformed into the spatial domain by IDCT (inverse discrete cosinetransform). HGA is the watermarked integer image where all real numbers in

HWRS are translated into integers by genetic algorithms.

Algorithm

1. Transform the host image H by DCT to obtain HDCT.

H = {h(i, j), 0 6 i, j < N}, where h(i, j) 2 {0,1,2, . . ., 2L � 1} and L is the

number of bits used to represent gray level of pixels.HDCT = {hDCT(i, j), 0 6 i, j < N}, where hDCT(i, j) 2 R.

2. Insert W into the coefficients ofHDCT to obtain HWF

W = {w(i, j), 0 6 i, j < M}, where w(i, j) 2 {0,1}.HWF = {hWF(i, j) = hDCT(i, j) � w(i, j), 0 6 i, j < N}, where hWF(i, j) 2 R.

3. Transform HWF by IDCT to obtain HWRS.

HWRS = {hWRS(i, j), 0 6 i, j < N}, where hWRS(i, j) 2 R.

4. Find the suitable solution to translate all real numbers in HWRS into inte-

gers, and obtainHGA.HGA = {hGA(i, j), 0 6 i, j < N}, where hGA(i, j) 2 {0,1,2, . . ., 2L � 1}.

Fig. 8 shows the example of correcting the rounding errors based on genetic

algorithms. Fig. 8(a) is the original host image, and Fig. 8(b) is the transformed

image by DCT. Fig. 8(c) is a binary watermark, in which ‘‘0’’ and ‘‘1’’ denote

the embedded value in its location. Note that the minus sign ‘‘�’’ indicates nochange in its position. We obtain Fig. 8(d) by embedding Fig. 8(c) into Fig.

8(b) based on LSB substitution. After transforming Fig. 8(d) into the spatialdomain by IDCT, we obtain Fig. 8(e), where all pixels are real numbers. The

rounding errors are corrected by only adjusting three values outlined by bold

rectangles from (41,5,165) in Fig. 8(a) to (40,6,166) in Fig. 8(f). That is, we

can extract the embedded watermark correctly from the specific positions of

Fig. 7. The scheme based on genetic algorithms.

Fig. 8. The rounding errors are corrected by genetic algorithms.


Fig. 8(g) which is transformed from Fig. 8(f) by DCT. Fig. 8(h) is the extracted

watermark which is exactly the same as Fig. 8(c).

3.4. The improved scheme based on genetic algorithms

In this section, we present an improved scheme for embedding watermarkinto the frequency domain of a host image. The new scheme not only reduces

the cost for obtaining the solution, but also offers more applications in water-

marking. The main idea is to adjust the pixels in the host image based on ge-

netic algorithms, and make sure the extracted data from the specific positions

in the frequency domain of the host image are the same as the watermark. Figs.

9 and 10 show the encoding and decoding procedures of the improved scheme,

respectively.

The improved algorithm for embedding watermark based on genetic algo-rithms is presented below. Its flowchart is shown in Fig. 11.

Fig. 9. The encoding procedure of the improved scheme.

Fig. 10. The decoding procedure of the improved scheme.

Fig. 11. Basic steps in finding the best solutions by the improved genetic algorithms.


Algorithm

1. Define the fitness function, number of genes, size of population, crossover

rate, and mutation rate.

2. Generate the first generation by random selection.

3. Adjust the pixel values based on each chromosome of the generation.


4. Evaluate the fitness value for each corresponding chromosome.

5. Obtain the better chromosomes.

6. Recombine new chromosomes by using crossover.

7. Recombine new chromosomes by using mutation.

8. Repeat Steps 3–8 until a predefined condition is satisfied, or a constant num-

ber of iterations is reached.

Fig. 12 shows the example of the improved genetic algorithms. Fig. 12(a)

and (b) are the original image and the signature data, respectively. Using the

embedded order shown in Fig. 12(c), we obtain Fig. 12(d) by separating the sig-

nature data into these 12 parts. Fig. 12(e) is the fragile watermark which is de-

fined by the user. Our purpose is to adjust the pixel values of Fig. 12(a) in order

to obtain its frequency domain in which we can extract signature data and frag-

ile watermarks from the specific positions. Here, the signature data and thefragile watermark are extracted from the positions of bits (3,4,5), and bit 1,

respectively. Fig. 12(f) shows the result of the adjusted image. We can extract

the signature data and fragile watermark from the coefficients of frequency do-

main of the watermarked image, as shown in Fig. 12(g). Table 1 shows the

Fig. 12. The example of the improved genetic algorithms.

Table 1

The watermark extracted from the specific positions of the binary form

Decimal Binary Decimal Binary Decimal Binary

90 01011010 88 01011000 3 00000011

84 01010100 27 00011011 78 01001110

38 00100110 55 00110111 12 00001100

32 00100000 26 00011010 28 00011100


watermark extracted from the specific positions of the coefficients of the fre-

quency domain of the original image.

4. Experimental results

Fig. 13 shows the example of embedding a signature image into a MRI brain

image. Fig. 13(a) and (b) show the original medical image with size of 230 · 230and the signature image with size of 64 · 64, respectively. Fig. 13(c) is the trans-formed image by DWT. The ROI part is marked as a rectangle with size of

91 · 112, as shown in Fig. 13(d). We encode Fig. 13(c) by SPIHT into a setof bitstreams which is embedded around the ROI part. In Fig. 13(e), the areabetween two rectangles, 91 · 112 and 117 · 138, is the clipped watermarkedarea. The signature image is extracted and the reconstructed result is shown

in Fig. 13(f). Table 2 shows PSNR (power signal-to-noise ratio) of the original

and reconstructed signature images, and of the non-watermarked and water-

marked parts. The error measures, PSNR, is defined as follows:

PSNR ¼ 10� log10

PNi¼1PN

j¼1½hGAði; jÞ2PN

i¼1PN

j¼1½hði; jÞ � hGAði; jÞ2

!:

Fig. 14 shows the example of embedding textual data into a CT brain image.

Fig. 14(a) and (b) show the original medical image with size of 260 · 260 andthe textual data, respectively. Fig. 14(c) is the encrypted data. The ROI part is

marked as a rectangle with size of 179 · 109, as shown in Fig. 14(d). We obtaina set of bitstreams by shifting the right-most 4 bits to the left side. In Fig. 14(e),

the area between two rectangles is the clipped watermarked area. Note that, the

original and extracted textual data are exactly the same.

Indeed, the genetic algorithm consumes more time. Our techniques are fo-cused on the following two goals: first, the embedded watermarks should be

as robust as possible and can be used to detect any unauthorized modification;

second, the compression rate of an image should be as high as possible. Fur-

thermore, the embedded watermarks will be disturbed when we convert real

numbers into integers. Genetic algorithms are adopted to be the best way to

achieve these goals.

Fig. 13. The example of embedding signature image.

Table 2

The PSNR of signature image and of medical image

Original and reconstructed

signature images

Non-watermarked and

watermarked medical images

PSNR 24.08 38.28


In general, the computational time is closely related to the amounts of the

required embedded data. That is, the more embedded data we have, the more

computational time it takes. For example, it takes about 4 min on a Pentium

III PC with 600 MHz for obtaining the results in Fig. 12 since there are 36 dig-

its of a bitstream and 16 digits of the fragile watermark. Note that in the liter-ature, usually only 4 digits of a bitstream and none of fragile watermark were

used.

Fig. 14. The example of embedding textual data.


5. Conclusions

Digital medical imaging technologies have become increasingly important in

medical practice and health care for providing assistant tools for diagnosis,

treatment, and surgery. Due to the volume of medical images is huge and

has grown rapidly, especially on CT (computer tomography) and MRI (mag-netic resonance imaging), the compression technique must be applied. For the

purpose of security, watermarks are embedded into medical images. Neverthe-

less, the embedded watermarks may be removed or distorted by attacks.

In this paper, we have presented the technique of embedding the signature

image and the fragile watermark into the frequency domain of non-ROI part

of a medical image by using the improved genetic algorithms. By compressing

the ROI part using lossless compression and the non-ROI part using lossy

compression, we can obtain a higher compression rate. Furthermore, the frag-ile watermark is embedded to detect any unauthorized modification. The pre-

sented method considers the watermarking techniques on 2-D still images. Our


concept can be extended to 3-D images by taking a 3-D cube as ROI. The re-

search issues such as efficiency of the compression rate in 3-D watermarking

will be studied in the future.

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robust watermarking and compression for medical images based on genetic algorithms

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