Authentication System for Medical Images Using Hilbert Numbering

Syifak Izhar Hisham Faculty of Computer Science and Software Eng.

Universiti Malaysia Pahang Gambang, Malaysia

penawar85@gmail.com

Jasni Mohamad Zain Faculty of Computer Science and Software Eng.

Universiti Malaysia Pahang Gambang, Malaysia jasni@ump.edu.my

Nurul Wahidah Arshad Faculty of Electric and Electronics Engineering

Universiti Malaysia Pahang Gambang, Malaysia

wahidah@ump.edu.my

Liew Siau-Chuin Faculty of Computer Science and Software Eng.

Universiti Malaysia Pahang Gambang, Malaysia liewsc@ump.edu.my

Nasrul Hadi Johari Faculty of Mechanical Engineering

Universiti Malaysia Pahang Pekan, Malaysia

nhadi@ump.edu.my

Gran Badshah Faculty of Computer Science and Software Eng.

Universiti Malaysia Pahang Gambang, Malaysia

gran16178@gmail.com

Abstract— Medical image is seen as one of crucial data that

demand for authentication method as it is highly confidential and used in insurance claim, evidence of jurisdiction and personal identification. Nowadays, Hospital Information System (HIS) is used widely at hospitals and clinical departments and it handles thousands of crucial electronic data in medical. We have introduced a fragile watermarking method using spiral manner numbering which showed a good numbering system and excellent embedding, but due to the technique, it only embedded in square shape. We enhanced the scheme to the Hilbert numbering scheme, which is more compatible with medical image modalities, which is not only specific to square shape of image but applicable to all kinds of image.

Keywords-Authentication; Hilbert; localization; security; recovery

I. INTRODUCTION Nowadays in a hospital, medical images are scanned by a

radiographer and stored in digital version in the Hospital Information System (HIS). The image will be transmitted to the doctor for recording and for diagnosing. If the specialist doctor is not from the same hospital, telemedicine is done by transmitting to the doctor to get the opinion from the experts. Thus, there is a concern about the security of the image since it is exposed to any attack once it is in the Internet [1-5].

A new way of giving a copy of medical report to the patient is by giving the softcopy of the image, not the printed

version. By this way, it saves the printing cost and also eases the process of storing. Thus, there is also a concern about the image security since the patient can edit the image to use as evidence for insurance claim or pressing false charge to the hospital.

Mistakes, alterations and errors in transmitted digital images may arise accidentally from the transmitting activity or purposely by the patients or hackers. Authentication method is seen as very important to answer this concern. Various techniques have been researched and various schemes have been developed in this field. It is a very active research area in recent years [1-5]. Among the criterion that is focused in medical image watermarking is the importance of not changing even a slight pixel to ensure no misdiagnosis happens. The watermarking scheme should also compatible to all modalities of medical images.

A robust reversible watermarking scheme focused on medical image has been proposed by [1] based on wavelet-like transform. It utilizes the Slantlet transform (SLT) to transform the image blocks and embed the watermark bits. The scheme is reversible as [1] claimed a doctor should diagnose based on original image as a slight change in the original image can lead to a significant difference in diagnosing and deciding.

Another new method has been proposed by [2] which presenting a fragile and reversible watermarking based on chaotic key. In this method, the region-of-interest (ROI) and region-of-non-interest (RONI) are distinguished and separated. The watermarking data is embedded in RONI and it makes the

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method is claimed imperceptible after embedding.

In addition, another reversible method is from [3], which presents an improved modification of their existing histogram bin shifting technique. By the technique, an optimal selection scheme is developed for the grey-scale value of the pixels which functions as the embedding point. In the embedding process, the system uses zero frequency values of the image produced in the histogram.

Another interesting method has been proposed by [4] using the technique of Cryptographic Message Syntax, which protects the corresponding access keys as watermarks. This method is built specific for medical image in DICOM format and able to endure the JPEG2000 compression. This method also utilizes the RONI and does not disturb the ROI of the image.

Weber’s law is also used in fragile watermarking technique, as presented by [5]. The proposed technique able to detect and locate a slight change made to the watermarked image and is tolerant to image compression. The method is by selecting dark pixels and modifying the intensity using Weber’s law. The method is very good, though it does not have the recovery or reversible function after detection phase. The peak-signal-to-noise-ratio (PSNR) value is very high between host and watermarked images, which is in the range of 50 dB and 70 dB.

A technique which integrates the embedding, localizing and recovering functions have been proposed by [6] as an authentication watermarking using spiral manner numbering for grey-scale and colour medical images. The spiral numbering shows a good numbering system of embedding. The PSNR value is high, up to 70 dB and the operating time is very fast. It is able to recover the valid digital image from various types of attacks such as changes in the pixels, changes of colours, block removal and various filter attacks. However, the images to be watermarked are limited to square shape image. Fig. 1 (a - c) shows the difference of embedded watermarking data using spiral manner numbering between rectangle and square shape of several modalities.

The main motivation of these issues focuses on the importance of the validity, integrity and security of the medical images of all types, grey-scale and colour image, also square and rectangle shape image. In this paper, we propose a novel watermarking technique as an improved modification of the existing [6] special manner numbering technique. We develop Hilbert manner numbering to fully cover the watermarking area in the image. The purpose of fragile watermarking is to verify the integrity and authenticity of all digital medical images, regardless what are the modalities and the size.

Figure 1(a). Grey-scale MRI and the watermarking data

Figure 1(b). Grey-scale mammogram and the watermarking data

Figure 1(c). Color MRI and the watermarking data

II. THE PROPOSED METHOD

A. Scanning and Numbering Patterns Before embedding, the blocks in the image should be

numbered and mapped to decide the location of blocks as the watermark data. A unique style of mapping or scanning of pixels can guarantee the top performance of authentication system by spreading the numbered data as far as possible from the original location. As investigated by [7], whether different scanning pattern affect the image quality reconstructed in image processing, it supports the idea stating that space filling curves can be used effectively as a scanning method in order to improve the reconstructed image quality when compared to a conventional raster scan method. Hilbert is one of space filling curve and a unique style of scanning and numbering, such as shown in Fig. 2.

Hilbert curve is a mapping scheme is proposed by David Hilbert, a German mathematician in 1891 [8]. The size of pixel scanned in the scheme is 2 × 2. Fig. 3 shows the close up of the scheme by the self-copy scheme.

While in spiral numbering technique, the numbering start from the center and the path is in spiral, as shown in Fig. 3. Although it is a unique way to number the pixels, which can provide an excellent sequence in mapping, it is not a totally space filling curve as it follows the spiral path only [6].

Figure 2. Hilbert Numbering and Scanning

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Figure 3. Spiral manner numbering

B. Proposed Scheme Based on the issue related to this research indicated in the

previous chapters, the following research diagram in Fig. 4(a - b) has been planned for this research based on a model from [9]. The objective in this research is to develop a new technique for authentication which appropriate for all medical image modalities, grey-scale and color images. Due to that, the samples in this research are from various modalities, CT, MRI, PET, ultrasound, mammogram, DTI and simple X-ray images, as shown in Table 1. According to the review done, popular samples of watermarking research are grey-scale ultrasound, grey-scale CT and MRI [9-14].

TABLE I. RESEARCH SAMPLES OF GREY-SCALES AND COLOR IMAGES

Modality Grey-scale image Colour image

Ultrasound

MRI

PET (Not available)

Mammogram

(Not available)

CT scan

DTI (Not available)

X-ray

(Not available)

Figure 4(a). The flow diagrams for proposed numbering and mapping technique

Figure 4(b). The flow diagrams for proposed embedding technique

All these samples have different qualities, sizes, height and width. Some of them are 8 bits and some are 16 bits. The type of image is bitmap (BMP) since most medical scanners can save the scanned image in bitmap. Showing here, there are many scanners that move forward to produce color images, and due to that, a watermarking that can work with grey-scale images and also color images is competent to be integrated with current HIS, PACS or any clinical database.

This scheme embeds the watermark in spatial domain, which is in the LSB of the image pixels. It is imperceptible as it does not produce serious distortion to the original image [15]. For authentication, this scheme uses parity check approach and comparison between average intensities approach. It is effective because we set it to double inspection. The scheme inspects the image twice with the inspection view increasing to a bigger block so that the accuracy of tamper localization can be ensured.

This scheme is a blind watermarking. It needs no original image to recover. Tamper detection is achieved through a block-based inspection with double checking. Recovery of a tampered block is achieved by retrieving the recovery bits embedded in the LSB. The proposed embedding technique starts with numbering by the Hilbert manner. It starts at random pixel and directly follows the generated iteration which is +L, -L, +R and –R as stated in Table 2. After numbered, each block is mapped (watermark embedding) using equation;

]mod)[( NbhkB ×= +1 (1)

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where B is the watermarked block, h is Hilbert, Nb is block numbers, and k is the secret key which is the highest prime number from the result of block numbers divided by 2.

The embedded watermark in each sub-block is a 3-tuple (v, p, r), where both v and p are 1-bit authentication watermark, and r is a 7-bit recovery watermark for the corresponding sub-block within the original block (block A) mapped to watermarked block (block C).

The scheme is to make sure the recovery bit of each original block will be embedded as far as possible to make sure although the image has been attacked with any vast attack, the recovery bit still can survive and the image is recoverable. While [9] numbered the pixel in raster scan, this research insists to show that numbering in Hilbert manner can make sure the recovery bit is far than the original location.

TABLE II. HILBERT PATTERN

Mapping Unit in iteration Unit generated

+L +R +L +L –R

-L -R –L –L +R

+R +L +R +R –L

-R -L –R –R +L

III. RESULTS AND ANALYSIS The metric used in this research is PSNR. The PSNR will

also be calculated by MATLAB to evaluate the quality of the watermarked image. PSNR is one of metrics to determine the degradation in the embedded image with respect to the host image. A general rule of PSNR is the values over 36 dB in PSNR are acceptable in terms of degradation, which means no significant degradation is observed by human eye [16].

We have tested 25 medical images using the proposed watermarking and the average PSNR value is satisfactory, 58.24 dB. As the PSNR value is high, there is no visual difference between the original image and the watermarked image. Table 3 shows the PSNR value for every sample with the comparison of Hilbert and spiral numbering method.

Table 4 shows the comparison between several latest methods in medical image watermarking with the types and functions of these schemes. PSNR value is stated in average. In a nutshell, comparing the average PSNR value with other methods, it is obvious Hilbert numbering method covers all the criteria needed for medical image watermarking.

IV. CONCLUSIONS The proposed watermarking scheme capacity is high. It

embeds all authentication data all over the image, regardless region-of-interest (ROI) or region-of-non-interest (RONI). This is to guarantee all data has authentication bits and recovery bits if one of the area is attacked or modified. The purpose is to ensure localization works at all data, as the fragility purpose is

not to protect the data like robust watermarking, but to be alert with the altered location in the image. The Hilbert numbering methods shows that it is compatible to various types of images, color and grey-scale. It is also equipped with detection, localization and recovery function. Its transparency is excellent and imperceptible as the PSNR value is high. The Hilbert and spiral numbering method recorded a very good distribution of embedded recovery bits, which support the investigation by [7] but as we stated earlier about the limitation of spiral numbering method that is only compatible for square shape image, Hilbert numbering method is claimed better than spiral numbering method.

ACKNOWLEDGMENT Our appreciation dedicated to RESEARCH AND

DEVELOPMENT DEPARTMENT OF UNIVERSITI MALAYSIA PAHANG for funding the research by PRG scheme.

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TABLE III. COMPARISON BETWEEN THE ORIGINAL AND WATERMARKED IMAGES WITH THE PSNR VALUE

Original Image Watermarked Image PSNR Value Using Hilbert Numbering

PSNR Value Using Spiral Numbering

58.9348 54.6770

58.3968 54.0736

58.4691 54.1651

TABLE IV. COMPARISON BETWEEN THE LATEST PROPOSED MEDICAL IMAGE WATERMARKING SCHEMES

Method Type Average PSNR Value

Detection Reversible / Recovery

Modalities / Image Type

Hilbert Manner Numbering Watermarking Fragile 58.24 X √ All Slantlet transform (SLT) Watermarking [1] Robust 39.25 √ MRI, CT

Chaotic Key Watermarking [2] Fragile 52.4 X √ Ultrasound Cryptographic Message Syntax Protected

Watermarking [4] Robust 25.2 √ X DICOM

Weber's Law based Watermarking [5] Fragile 61.94 √ X All Spiral Manner Numbering Watermarking [6] Fragile 62.06 √ √ Square Shaped

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