RESEARCH ARTICLE


Shape Adaptable Medical Multimedia Processing



Ali J. Abboud*
Department of Computer Engineering, College of Engineering, University of Diyala, Diyala Governorate, Iraq


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© 2019 Ali J. Abboud.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Department of Computer Engineering, College of Engineering, University of Diyala, Diyala Governorate, Iraq, E-mail: ali.j.abboud@gmail.com


Abstract

Introduction:

Electronic medical healthcare systems are becoming the backbone of health organizations over the world. The huge amounts of medical multimedia produced by these systems especially images and videos are transmitted by the computer networks that connect these systems. The variability in the shape and texture of transferred medical multimedia data needs adaptable procedures to process these data efficiently. In other words, these procedures must adjust automatically based on the shape of region of interests in the medical multimedia images to cope with fast changes in the healthcare environments. In this paper, we have proposed shape adaptable watermarking approaches for medical multimedia processing systems. The medical images generated by X-rays, MRI and CT modalities are used in our experiments to test proposed approaches. In addition, these approaches were tested under different kinds of signal processing and geometric attacks. The comparative comparison of our proposed approaches with state-of-art approaches proved the superiority and capability of our approaches to adjust the number of selected subands of medical cover image to embed and extract the hospital watermark logos.

Background and Objective:

The development of an adjustable approach to process medical multimedia signals for healthcare system. The aim of this research is to select adaptably the number of subands of cover image to hide the information of hospital logo watermark inside them such that embedded watermarks can resist different kinds of attacks.

Method:

The proposed adjustable approach consists of suband selection method, criterion, embedding and extraction procedures, DWT transform, attacks, evaluation metrics, etc.

Results & Conclusion:

It provides robust and adjustable method to embed and extract watermark logo at different resolution levels of cover medical image and uses with images of different sizes and modalities.

Keywords: Shape adaptable, Medical imaging, Multiple level embedding, Watermarking, Region size.



1. INTRODUCTION

Medical healthcare is one of the most important sectors in the daily life activities of human beings. The traditional healthcare practices used paper based medical reports and diagnoses to help doctors and specialists to make suitable decisions to treat their patients [1]. Nowadays, telemedicine revolutionize the traditional practices and procedures by exchanging medical images and patient’s records electronically across all world hospitals. Indeed, telemedicine is becoming the imperative tool in the infrastructure of all healthcare systems [2]. This tool uses modern computer technologies to provide electronic healthcare services to the people living in rural and far areas. Those people in the past, need to travel for long distances to obtain necessary diagnosis and treatment, however currently, telemedicine made this task very easy by contacting physicians and doctors remotely and giving right consultation to those patients [3].

Hence, we can say that telemedicine is an effective apparatus to serve patients accurately and quickly. However, interchanging medical images and patient records electronically by this tool through the computer networks especially global internet makes these sensitive data vulnerable to different kinds of intentional attacks. These attacks can modify, insert or delete the contents of medical data [4]. In addition, the accidental attacks such as noise, compression, rotation and scaling translation have adverse effects on the performance of medical healthcare systems. To make the electronic health system robust to all these attacks, there are certain information security approaches to deal effectively with all these attacks. These security services should be available in all healthcare systems to protect information of patients and medical staff. Confidentiality, authentication and integrity are the main security characteristics that should be available in any telemedicine system in order to be secure enough to provide medical services robustly. Confidentiality guarantees that only intended parties will use medical multimedia information while authentication assures that the medical multimedia information comes from authorized source. On the other hand, the integrity service find out whether the medical data modified or not through transmission from source to destination [5, 6].

Cryptography, digital signatures, hashes and watermarking hiding algorithms are used to satisfy earlier explained information security characteristics in the healthcare systems. Cryptography is one of strongest tools to provide confidentiality for different kinds of multimedia data including text, images, voice and videos while digital signatures and hashes are used extensively to check the integrity of multimedia files. In addition, watermarking algorithms are used to provide authentication and copyright of data and recently used to recover tampered areas of multimedia file .The rest of paper is organized as follows: Section 2 is devoted to explain background material and Section 3 is used to present developed method. In addition, Section 4 is used to explain our experimental results. Finally, conclusion is presented in Section 5.

2. BACKGROUND

Medical multimedia processing refers to the creating and manipulating the visual information of medical data to diagnosis and treat patients accurately. These medical multimedia include image, voice or/and video signal of the diagnosed organs inside the body of the patient. The shape of patient’s organs may be changed according to the health status of the sick person. Sometimes, the organ is healthy and has normal shape while in other states, the size and shape of this organ is changed significantly for its sickness by infections, microbes, germs or bacteria. Hence, we can conclude that the size and shape of organs will change according to the condition of the patient. For earlier reasons, this research is aimed at developing dynamic algorithms that can adapt to the shape, size and health of patient organs. These developed algorithms process medical images dynamically. We have to mention that medical multimedia images may have one or more regions of specific importance to the doctor. These regions are called Regions Of Interest (ROI) and irrelevant regions for medical staff and doctors are called Regions Of No Interest (RONI). The details of the ROI are very important for diagnosing and treating the people, hence, this region should be protected from any intentional or accidental changes such as tampering, rotation, shear, translation, scaling, compression or tampering. Several algorithms were used in this research study as described below.

2.1. Discrete Wavelet Transform

Many approaches have been proposed in the literature to analyse and extract useful information from digital signals and images either in the spatial or transform domain [7, 8, 9]. However, transform domain based approaches look more robust to any geometric modifications and attacks on the medical images [10]. Such of these popular transforms are discrete wavelet transform (DWT), Discrete Cosine Transform (DCT), and Discrete Fourier Transform (DFT) [11, 12]. Wavelet transform is the set of basis functions copied from base mother wavelet and they are orthogonal to each other. It characterizes by decomposing the image into several successive levels depending on the decomposition level and the size of the image. In this research, DWT is used in our research as image transform tool with aim to obtain information about medical image simultaneously in the spatial and frequency domains as in Fig. (1).

Fig. (1). Discrete wavelet transform decomposition of medical multimedia images.

2.2. Watermarking Algorithms

Image watermarking is the state of art technology to provide several security services simultaneously. These services are the source authentication, patient identity checking and digital multimedia integrity verification [13]. These services are satisfied by embedding robust, semi fragile and fragile watermarks in the medical multimedia images. These watermarks may be visible or invisible in the multimedia signal depending on the application. Furthermore, these watermarks signals may be hidden in the spatial or/and transform domain(s) of the multimedia signal. For medical multimedia images of the patients, the watermarking algorithm should be reversible (i.e. we can recover watermarks without causing any distortion to the medical image). In addition, the watermarking algorithms should embed watermarks such that be robust to different kinds of intentional or accidental attacks.

2.3. Singular Value Decomposition

Singular Value Decomposition (SVD) is the mathematical tool to factorize matrix W into three matrices X, Y and Z [14]. If a matrix W of dimensions m x m, therefore the SVD of matrix W is:

(1)

Where X and Y are left and right orthonormal vectors and Y is the diagonal matrix of singular values. The singular values of Y are arranged in the diagonal in the descending order. The SVD is used in the signal and image processing for several reasons, which are [1]: (1) there is a need to use less singular values to reproduce big part of signal power (2) we can use SVD with different sizes matrices (3) SVD singular values have good robustness to the attacks on the signal and multimedia images.

2.4. Performance Measures

The performance measurement of the biomedical multimedia processing system is evaluated using three main measures described below as follows:

2.4.1. Peak-To-Signal-Noise-Ratio (Psnr) Measure

PSNR is the most used measure to quantify the quality of multimedia signals including image, voice and video. This measure is comparing the peak power between original and corrupted signals as shown below:

(2)
(3)

Where MSE is the mean square error between the original image (A) and noisy image (B).

2.4.2. Structural Similarity Index (SSIM)

SSIM is a novel measure to quantify the degree of similarity between referenced and corrupted signals. The concept of measurement for this metric is based on the structure human vision system [15]. It measures the structure, luminance and contrast using following equation:

(4)

The kernel sizes for images A and B are the variables a and b.

(5)

MISSIM represents the mean SSIM over number of kernels referred to by variable n.

2.4.3. Normalized Cross Correlation Coefficient (Nc)

The normalized cross correlation coefficient (NC) measures the similarity between original and referenced images such that the higher value for this measure is one. It can be calculated as follows:

(6)

Where A and B matrices represent the original and referenced signals (or images).

3. PROPOSED METHOD

We have proposed in this paper an adjustable (i.e. adaptable) method to embed watermarks in the frequency domain of transformed medical image. The adaptability is achieved by dynamically select one or more wavelet decomposition levels of medical cover image. The selection of type and level of wavelet decomposition is depending on the size and shape of medical image region. The procedures of embedding and extracting the secure watermarks are as follow:

3.1. Shape Adaptable Watermark Embedding Procedure

The procedure to embed watermarks in the medical multimedia image as follows:

  1. Start
  2. Segment the original multimedia medical image I into the ROI and RONI regions.
  3. Determine the size and shape (S) of ROI in the medical image.
  4. Based on the output (S) of step 3 specify the number (N: 1, 2, 3,4,5,6 or 7) and type (T: LL, LH, HL or HH) of decomposition levels of wavelet transform for medical cover image I. We will refer to the number of subands for embedding hospital watermark logo by (NS).
  5. Apply wavelet transform of kind (Haar) on the cover medical image I using (T and N) from step 4.
  6. Apply SVD on the selected subands (NS) of medical image I as follows:
    (7)
  7. Compute SVD for the watermark logo W:
    (8)
  8. Replace the diagonal singular values (YS(n)) of selected subands of medical image I by the diagonal singular values (Yw) of W image.
  9. Do inverse SVD to obtain changed selected subands (NSw):
    (9)
  10. Do inverse DWT on the changed selected subands to obtain final watermarked cover image Iw.
  11. End

3.2. Shape Adaptable Watermarking Extracting Procedure

The procedure to extract watermarks from the medical multimedia image as follows:

  1. Start
  2. Segment the watermarked medical cover image Iw into the ROI and RONI regions.
  3. Determine the size and shape (S) of ROI in the medical image.
  4. Based on the output (S) of step 3, specify the number (N: 1, 2, 3,4,5,6 or 7) and type (T: LL, LH, HL or HH) of decomposition levels of wavelet transform for watermarked medical cover image Iw. We will refer to the number of subands for embedding hospital watermark logo by (NS).
  5. Apply wavelet transform of kind (Haar) on the watermarked cover medical image Iw using (T and N) from step 4.
  6. Apply SVD on the selected subands (NS) of medical image Iw as follow:
    (10)
  7. Extract the embedded singular values in the selected subands of cover image (YS(n)).
  8. Compute SVD for the watermark logo W:
    (11)
  9. Re building watermarks using the extracted singular values in step 7 and the left and right orthonormal vectors of matrix W of step 8.
    (12)
  10. End

4. EXPERIMENTAL RESULTS AND DISCUSSION

We have conducted experiments to testify the possibility of our proposed method to adjust the location of hiding watermarks according to the shape of ROI in the medical image. The proposed method was tested in these experiments using images of CT and MRI and X-ray medical modalities. Also, these experiments were performed in three groups according to the type of wavelet decomposition level (LH, HL and HH). Several kinds of attacks have been applied to the watermarked medical image to test the robustness of the proposed method to resist such kind of attacks. We have chosen mean, median, noise, rotate, shear, compression and tampering as the potential attacks on the shape adjustable medical processing system. Figs. (2, 3 and 4) presented to show examples of these attacks on the images of three different imaging modalities.

Fig. (2). An example of attacks on the X-rays modality of knee medical image.

Fig. (3). An example of attacks on the CT scan modality of brain medical image.

4.1. LH Decomposition Level Experiments

LH subband is used in this group of experiments to hide the hospital watermark logo. The medical image is decomposed from level one until level seven (1-7). It means that there are seven decomposition levels for each tested medical image. Where LHn (i.e. LH1, i.e. LH2, i.e. LH3, i.e. LH4, LH5, LH6, LH7) on the horizontal axis in the Fig. (5) below refers to the groups of LH subands and does not mean the LH level number as conventionally known. For example, LH3 is the group of three LH subands {LHlevel 1 + LHlevel 2 + LHlevel 3}. We have noticed from the experimental results presented in the Fig. (5), that all the applied attacks have worse effect on the watermark logo image. However, only the contrast attack has less or no influence on the reconstructed watermark and has the same metrics values similar to the case of no attack on the medical image. For the rest of the attacks, we can say that the noise is the most powerful attack on the watermarked medical image. The big impact of this attack is apparent in the experimental results by making SSIM measure value (< 0.1) and NC measure value (< 0.6) and the PSNR measure value (< 20 dB). In addition, we have noticed that the noise attack effect becomes worse when goes deeper in the decomposition levels. Mean and median are the next influential attacks that have apparent effect on the reconstructed watermark logo. We can observe from the presented results below that these attacks made the SSIM metric value (0.2) and NC metric value (<0.5) and PSNR metric value (< 30 dB). However, we have noticed that the mean attack has consistent worse effect among all decomposition levels while the median attack starts to have less influence on the performance of the system from level (LH3) onwards. The rotate and shear geometric attacks are the next group in this sequence of attacks. The deteriorated effect of geometric attacks begins to appear from third decomposition level onwards, hence, we advise to use third level to embed watermark logos. Now, let analyze the influence of the last two attacks (i.e. tampering and compression). The tampering attack has less influence on the performance of the system than compression attack as proved in the terms of SSIM and PSNR metrics values in the Fig. (5). However, the bad effect of those two attacks generally increases gradually from the lowest decomposition band to the highest decomposition subband as shown in the Fig. (5). To sum up, the worst attack on LH subband across all levels is the noise attack and the least effect (or no effect) attack on the shape adjustable system in this subband is the contrast attack.

Fig. (4). An example of attacks on the MRI modality of legs medical image.

4.2. HL Decomposition Level Experiments

HL subband is used in this group of experiments to embed the hospital watermark logo. The medical image is decomposed into seven levels to embed watermark inside them. Where HLn (i.e. HL1, HL2, HL3, HL4, HL5, HL6, HL7) on the horizontal axis in the Fig. (6) below refers to the groups of HL subands and does not mean the HL level number as conventionally known. For example, HL3 is the group of three HL subands {HLlevel 1 + HLlevel 2 + HLlevel 3}. We have noticed from the experimental results presented in the Fig. (6), that the all applied attacks have worse effect on the watermark logo image. This result add another evidence that the attacks have adverse effects on the performance of healthcare systems. However, only contrast attack has less or no influence on the reconstructed watermark and does not affect on medical processing system (i.e. similar to the case of no attack). For the remaining attacks, we can say that the noise is the most powerful attack on the watermarked medical image. The big impact of this attack is apparent in the experimental results by making SSIM measure value (< 0.1) and NC measure value (< 0.5) and the PSNR measure value (< 20 dB). In addition, we have noticed the same observation that the effect of noise attack becomes worst when goes deeper in the decomposition levels. Mean and median are the next influential attacks that have apparent effect on the reconstructed watermark logo. We can observe from the presented results below that these attacks made the SSIM metric value (0.2) and NC metric value (<0.5) and PSNR metric value (< 20 dB). However, we have noticed that the mean attack has consistent worse effect among all decomposition levels while the median attack starts to be less influence on the performance of the system from level (LH3) onwards. Hence, we advise to embed the watermarks from level three onwards. The rotate and shear geometric attacks are the next group in this sequence of attacks. The deteriorated effect of geometric attacks begins to appear from third decomposition level onwards, hence, we advise to use third level to embed watermark logos. Now, let analyze the influence of the last two attacks (i.e. tampering and compression). The tampering attack has less influence on the performance of the system than compression attack as proved in the terms of SSIM and PSNR metrics values in the (Fig. 6). In general, the bad effect of those two attacks increases gradually from the lowest decomposition band to the highest decomposition subband as shown in the (Fig. 6). Overall, the worst attack on HL subband across all levels is the noise attack and the least effect (or no effect) attack on the shape adjustable system in this subband is the contrast attack. Finally, we have noticed that the HL3, HL4 and HL5 are the best levels to embed watermark logos in the CT scan modality.

Fig. (5). The results of embedding hospital logo across seven level in the LH Subband for X-rays, MRI and CT scan modalities.

4.3. HH Decomposition Level Experiments

HH subband is used in this group of experiments to hide the hospital watermark logo. The medical image is decomposed into seven levels to embed watermark inside them. Where HHn (i.e. HH1, HH2, HH3, HH4, HH5, HH6, HH7) on the horizontal axis in the Fig. (7) below refers to the groups of HH subands and does not mean the HH level number as conventionally known. For example, HH3 is the group of three HH subands {HHlevel 1 + HHlevel 2 + HHlevel 3}. We have noticed from the experimental results presented in the Fig. (7), that the all applied attacks have worse effect on the watermark logo image. These results add another evidence that the attacks have adverse effects on the performance of healthcare systems. However, only contrast attack has no influence on the reconstructed watermark and does not disturb medical processing system (i.e. similar to the case of no attack). For the remaining attacks, we can say that the noise is the most powerful attack on the watermarked medical image. The big impact of this attack is apparent in the experimental results by making SSIM measure value (< 0.1) and NC measure value (< 0.2) and the PSNR measure value (< 20 dB). We have also noticed that the effect of noise attack is consistent among all the HH decomposition levels of medical image. In addition, we have observed interesting result that the noise influence becomes the least in the seventh decomposition level in contrast to the presented results in the earlier two subsections. Mean and median are the next influential attacks that have apparent effect on the reconstructed watermark logo. We can observe from the presented results below that these attacks made the SSIM metric value (0.1) and NC metric value (<0.2) and PSNR metric value (< 20 dB). However, we have noticed that the mean attack has consistent worse effect among all decomposition levels while the median attack starts to be less influence on the performance of the system from level (HH3) onwards. Hence, we advise to embed the watermarks from level three onwards especially in level (HH4). The rotate and shear geometric attacks are the next group in this sequence of attacks. The deteriorated effect of geometric attacks begins to appear from third decomposition level onwards, hence, we advise to use third level to embed watermark logos. Now, let analyze the influence of the last two attacks (i.e. tampering and compression). The tampering attack has less influence on the performance of the system than compression attack as proved in the terms of SSIM and PSNR metrics values in the (Fig. 7). In general, the bad effect of those two attacks increases gradually from the lowest decomposition band to the highest decomposition subband as shown in the (Fig. 7). Overall, the worst attack on HH subband across all levels is the noise attack and the least effect (or no effect) attack on the shape adjustable system in this subband is the contrast attack. Finally, we have noticed that the HH4, HL5 and HL6 are the best levels to embed watermark logos for all modalities.

4.4. Medical Cover Image Experiments

In this subsection, we presented the experimental results of analyzing the effects of attacks on the medical cover image for X-rays, MRI and CT scan modalities as shown below in the Tables 1, 2 and 3. We have observed from these results that the X-rays modality images have higher metrics values than other two modalities. The median attack has higher metric values than other attacks on the medical image for all modalities. Compression and noise attacks have the least values for PSNR, SSIM and NC metrics among all presented attacks. In addition, the HH subband has the higher metric values among tested decomposition subands. These experimental results are consistent with the earlier presented results of embedded and extracted watermarks in the previous subsections.

Tables 4, 5, 6 and 7 are presented to show the number of coefficients of watermark image that can be embedded in the pixels of the cover image. In these tables, the following terms mean that the coefficients of one suband (1 Sub.), two subands (2 Sub.), three subands (3 Sub.) or four subands (4 Sub.) are used to embed the coefficient of watermark logo. Hence, if the size of image and the number of subands increased then we can hide more coefficients in the image. Figs. (8 and 9) are presented to show an example of reconstructed watermark logos after disturbing the cover image by a number of different attacks that are described earlier.

Table 1. Watermarked Medical Cover Image (Logo embedded in the LH subband).
Type of Attack X-rays Modality MRI Modality CT Scan Modality
PSNR SSIM NC PSNR SSIM NC PSNR SSIM NC
No attack 30.279 0.504 0.954 23.342 0.456 0.843 28.292 0.610 0.918
Mean 28.037 0.277 0.957 22.600 0.358 0.862 26.537 0.338 0.928
Median 29.720 0.390 0.956 24.076 0.460 0.858 28.204 0.523 0.927
Noise 09.212 0.054 0.497 09.629 0.088 0.383 13.645 0.259 0.362
Rotation 14.210 0.097 0.559 11.555 0.014 0.187 16.560 0.013 0.527
Shear 15.042 0.184 0.671 11.918 0.030 0.307 15.849 0.021 0.432
Contrast 11.978 0.315 0.954 09.573 0.240 0.843 10.426 0.165 0.918
Compression 03.807 0.047 0.506 02.618 0.006 0.393 02.225 0.006 0.369
Tampering 18.802 0.377 0.840 15.517 0.377 0.652 15.298 0.498 0.673
Table 2. Watermarked Medical Cover Image (Logo embedded in the HL subband).
Type of Attack X-rays Modality MRI Modality CT Scan Modality
PSNR SSIM NC PSNR SSIM NC PSNR SSIM NC
No attack 32.996 0.604 0.990 29.665 0.597 0.971 29.620 0.614 0.951
Mean 31.713 0.373 0.991 28.070 0.492 0.981 28.085 0.363 0.958
Median 33.656 0.510 0.992 30.978 0.615 0.981 30.122 0.533 0.959
Noise 09.212 0.090 0.545 09.630 0.101 0.481 13.645 0.246 0.375
Rotation 13.338 0.103 0.543 13.447 0.013 0.174 16.151 0.010 0.506
Shear 14.251 0.186 0.659 13.874 0.023 0.281 15.536 0.013 0.397
Contrast 11.642 0.337 0.990 11.507 0.321 0.971 10.395 0.179 0.951
Compression 04.086 0.069 0.553 02.937 0.006 0.491 02.299 0.005 0.382
Tampering 18.666 0.473 0.867 17.150 0.461 0.740 13.254 0.459 0.604
Table 3. Watermarked Medical Cover Image (Logo embedded in the HH subband).
Type of Attack X-rays Modality MRI Modality CT Scan Modality
PSNR SSIM NC PSNR SSIM NC PSNR SSIM NC
No attack 34.283 0.585 0.995 31.556 0.538 0.979 31.741 0.658 0.977
Mean 33.992 0.434 0.996 30.145 0.529 0.990 30.942 0.416 0.983
Median 36.439 0.538 0.997 32.418 0.629 0.992 33.246 0.607 0.986
Noise 09.212 0.091 0.549 09.630 0.099 0.491 13.645 0.268 0.401
Rotation 13.162 0.104 0.548 14.769 0.015 0.174 16.652 0.011 0.511
Shear 14.112 0.188 0.665 15.258 0.028 0.288 16.028 0.021 0.406
Contrast 11.515 0.319 0.995 12.541 0.284 0.979 10.852 0.185 0.977
Compression 04.099 0.071 0.557 02.988 0.008 0.501 02.470 0.007 0.408
Tampering 18.629 0.467 0.872 18.493 0.429 0.752 15.504 0.531 0.712
Table 4. Number of coefficients for images of size (2048, 1024 and 512) among different subands.
Level 2048 x 2048 1024 x1024 512 x512
1 Sub. 2 Sub. 3 Sub. 4 Sub. 1 Sub. 2 Sub. 3 Sub. 4 Sub. 1 Sub. 2 Sub. 3 Sub. 4 Sub.
1 1024 2048 3072 4096 512 1024 1536 2048 256 512 768 1024
2 1536 3072 4608 6144 768 1536 2304 3072 384 768 1152 1536
3 1792 3584 5376 7168 896 1792 2688 3584 448 896 1344 1792
4 1920 3840 5760 7680 960 1920 2880 3840 480 960 1440 1920
5 1984 3968 5952 7936 992 1984 2976 3968 496 992 1488 1984
6 2016 4032 6048 8064 1008 2016 3024 4032 504 1008 1512 2016
7 2032 4064 6096 8128 1016 2032 3048 4064 508 1016 1524 2032
8 2040 4080 6120 8160 1020 2040 3060 4080 510 1020 1530 2040
9 2044 4088 6132 8176 1022 2044 3066 4088 511 1022 1533 2044
10 2046 4092 6138 8184 1023 2046 3069 4092
11 2047 4094 6141 8188
Fig. (6). The results of embedding hospital logo across seven levels in the HL Subband for X-rays, MRI and CT scan modalities.

Table 5. Number of coefficients for images of size (256, 128, and 64) among different subands.
Level 256 x 256 128 x128 64 x 64
1 Sub. 2 Sub. 3 Sub. 4 Sub. 1 Sub. 2 Sub. 3 Sub. 4 Sub. 1 Sub. 2 Sub. 3 Sub. 4 Sub.
1 128 256 384 512 64 128 192 256 32 64 96 128
2 192 384 576 768 96 192 288 384 48 96 144 192
3 224 448 672 896 112 224 336 448 56 112 168 224
4 240 480 720 960 120 240 360 480 60 120 180 240
5 248 496 744 992 124 248 372 496 62 124 186 248
6 252 504 756 1008 126 252 378 504 63 126 189 252
7 254 508 762 1016 127 254 381 508
8 255 510 765 1020
9
10
11
Fig. (7). The results of embedding hospital logo across seven level in the HH Subband for X-rays, MRI and CT scan modalities.

Table 6. Number of coefficients for images of size (32, 16, and 8) among different subands.
Level 32 x 32 16 x 16 8 x8
1 Sub. 2 Sub. 3 Sub. 4 Sub. 1 Sub. 2 Sub. 3 Sub. 4 Sub. 1 Sub. 2 Sub. 3 Sub. 4 Sub.
1 16 32 48 64 8 16 24 32 4 8 12 16
2 24 48 72 96 12 24 36 48 6 12 18 24
3 28 56 84 112 14 28 42 56 7 14 21 28
4 30 60 90 120 15 30 45 60
5 31 62 93 124
6
7
8
9
10
11

4.5. Comparative Comparsion With Other Approaches

We have presented Table 1 to compare the performance of our approach with two approaches in the literature. We have noticed that in the LL suband, our approach outperforms other research works in the references [1, 16] for different listed attacks. In addition, we observed that the proposed approach exceeds other approaches for (LH, HL and HH) subands in terms of providing higher NC measure values. Hence, we can say that our approach is better those two approaches in all subands for various kinds of attacks listed in table below.

Table 7. Number of coefficients for images of size (4, 2, and 1) among different subands.
Level 4 x 4 2 x 2 1 x1
1 Sub. 2 Sub. 3 Sub. 4 Sub. 1 Sub. 2 Sub. 3 Sub. 4 Sub. 1 Sub. 2 Sub. 3 Sub. 4 Sub.
1 2 4 6 8 1 2 3 4
2 3 6 9 12
3
4
5
6
7
8
9
10
11
Fig. (8). Recovered hospital watermark logo from medical image after different kinds of attacks.

Fig. (9). Recovered hospital watermark logo from medical image after four kinds of attacks at seven DWT decomposition levels.

Table 8. Comparative comparison with other approaches using NC measure values for four DWT bands.
Attack Type LL Suband LH Suband HL Suband HH Suband
Proposed Ref [1] Ref [16] Proposed Ref [16] Proposed Ref [16] Proposed Ref [16]
1 Compression 0.512 0.5057 0.9014 0.830 - 0.0635 0.742 - 0.3440 0.749 - 0.5106
2 Median 0.931 0.6173 0.2422 0.702 - 0.6471 0.679 - 0.5230 0.633 0.0110
3 Noise 0.482 0.4772 0.1585 0.814 0.2997 0.728 0.2978 0.736 0.4773
4 Rotation 0.540 0.5207 0.0959 0.433 - 0.0157 0.437 0.3259 0.463 - 0.1543

CONCLUSION

We have presented in this research paper an approach to adjust automatically the number of selected subands to embed the diagonal coefficients of watermark logos in the cover medical image. The criteria of adjustment (or adaptation) depends on the size of cover image and watermark logo. The maximum number of decomposition levels for embedding is determined by seven levels. We concluded that the effects of applied attacks might be different for each modality and each suband of cover image. For instance, the images produced by CT modality have more resistance to the noise and compression attacks in the higher decomposition subands while the X-rays and MRI images are more resistant to the mean and median attacks. However, generally, the noise attack is the most influential attack on all images produced by all modalities, and the contrast attack is the least (or no effect) attack on the different kinds of images. Furthermore, the geometric attacks (i.e. rotate and shear) have an adverse effects on the performance of medical multimedia systems. In the future, we will use other transforms such as DCT and cryptographic algorithms to enhance the security of healthcare systems.

CONSENT FOR PUBLICATION

Not applicable.

CONFLICT OF INTEREST

The authors declare no conflict of interest, financial or otherwise.

ACKNOWLEDGEMENTS

Declared none.

REFERENCES

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