Medical Image Watermarking using a Perceptual Similarity Metric

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1 MIT International Journal Electrical and Instrumentation Engineering Vol. 1, No. 1, Jan. 2011, pp Medical Image Watermarking using a Perceptual Similarity Metric Janani Natarajan Engineering Department, St.Xavier s Technical Institute, Mumbai, India janani_natarajan@yahoo.co.in Vijay R. Rathod Electronics Engineering Department, St.Xavier s Technical Institute, Mumbai, India gmail.com. Abstract Medical image data hiding has strict constraints such as high imperceptibility, high capacity and high robustness. This paper presents a novel model whereby medical image regions are watermarked with the payload differently so that perceptual degradation due to watermarking is limited. Here we describe an approach to ensure that impact on the image quality is well below the threshold visual perceptibility. The principle on which this approach rests is the choice a suitably light payload, and the use different watermarking methods and parameters for different medical image types. The model partitions images into regions and characterizes each region according to some feature(s). Each region is then watermarked with a particular watermark method and payload capacity such that perceptual degradation is limited. Results on MR and CT images demonstrate that less visually sensitive areas on images can be watermarked using more robust techniques and more sensitive areas can be watermarked using lighter or no embedding. Keywords imperceptibility, payload, perceptual degradation, robustness, watermarking. I. INTRODUCTION The exchange databases between hospitals requires efficient transmission and sto to cut down the cost healthcare. This exchange involves a large amount vital patient information such as bio-signals, word documents, and medical images. Image watermarking has become an increasingly popular research area with applications in data security, content verification and image integrity. Medical image watermarking is a particular subset image watermarking whereby medical images are embedded with hidden information that may be used to assert ownership, increase the security, and verify the numerical integrity medical images [1]. Watermarking techniques are divided into two basic categories, which are: (1) spatial-domain watermarking, in which the lower order bits the image pixels are replaced with that the watermark or adding some fixed intensity value to a picture and (2) frequency-domain watermarking, in which the image is first transformed to the frequency domain discrete fourier transform (DFT) or discrete cosine transform (DCT), and then the low-frequency components are modified to obtain watermarked images[4]. Unlike most images, medical images require particular care when embedding additional information within them because the additional information must not affect the reading the images. This paper presents a model that addresses this issue, by carefully analyzing regions medical images, categorizing each region according to some feature and then watermarking each region differently such that only a limited amount visual degradation occurs. II. BACKGROUND Medical image watermarking has been proposed as an appropriate method for enhancing data security, content verification and numerical image fidelity. Due to the sensitive nature the data, medical image watermarking requires that any additional information that is stored within an image must not affect the perceptual integrity the image[2]. In typical approaches, additional information is generally hidden in the entire image, or in the background regions an image (so as not to affect the medical data). Using this algorithm medical image regions are watermarked differently so that perceptual degradation due to watermarking is limited. It partitions images into regions and characterizes each region according to some feature(s). Each region is then watermarked with a particular watermark method and payload capacity such that perceptual degradation is limited. It can be shown that on MR and CT images less visually sensitive areas on images can be watermarked using more robust techniques and more sensitive areas can be watermarked using lighter or no embedding. Image Payload Encoding system Fig. 1. Embedding a payload Watermarked Image Image watermarking consists two processes: encoding and decoding. During encoding, a payload is embedded into an image, as shown in Fig. 1. Typical payloads may be text (such as patient or image capture

2 MIT International Journal Electrical and Instrumentation Engineering Vol. 1, No. 1, Jan. 2011, pp details), another image (such as a logo) or an identification number (such as patient or hospital identifiers). Fig. 2 shows the decoding process for a blind watermarking system. In this type system only the embedded image is supplied from which the payload is to be extracted. Nonblind systems may have additional data such as the original image or payload. Water marked Image Decoding system Payload Fig. 2. Recovering a payload Fig. 3. Method & Capacity selection III. PROPOSED DATA HIDING METHOD The aim the watermarking system presented in this paper is to demonstrate that specific regions in medical images can be watermarked with specific watermarking methods and payload capacities, so that perceptual damage caused by watermarking is minimized. The experiment relies on a number important parameters: 1. Image region size and shape. 2. Method region characterizations. 3. Selection appropriate watermarks and associated capacities (i.e. number bits that are embedded per region). 4. Method for comparing the visual similarity (or degradation) between raw and watermarked regions. Fig. 4. Encoder The objective is to use the above to specify a set regions, with specific characteristic values. Using this information, the most appropriate watermark method and capacity are selected for each region that falls within a specific range characteristic values. The most appropriate refers to the most robust watermark method and highest capacity that can be embedded in the region such that the watermarked area falls within a preset allowable perceptual damage threshold. The watermarking system consists two phases. Phase I determines region characteristics, and appropriate watermarks for each region type, as shown in Fig. 3. The output Phase I is a table that specifies the most appropriate watermark (WM) method and payload capacity for each range region characteristic values. Phase II encodes (Fig. 4) and decodes (Fig. 5) watermark payloads into and from medical images, using the watermark selection table. Fig. 5. Decoder IV. METHOD AND CAPACITY SPECIFICATION In the first phase the watermarking system, the most appropriate watermark method and payload capacity are determined for each region type. During this phase, the parameters and thresholds outlined above must be appropriately selected. Table 1 presents the selections used in the current implementation the watermarking system.

3 MIT International Journal Electrical and Instrumentation Engineering Vol. 1, No. 1, Jan. 2011, pp TABLE I. Parameter selections for watermarking system Parameter Region - Shape - Size Region Characterization Watermark Methods and Payload capacities Perceptual similarity -metric -threshold Current Implementation Square Blocks 8x8 pixels σ R standard deviation DCT2 -DCT with 2 bits per block DCT1 - DCT with 1 bit per block LSB2 - LSB with 2 bits per block LSB1 LSB with 1 bit per block SC-Structural Comparison SC.985 (quality 0) Or SC.98 (quality 1) Regions are constructed from 8 8 pixel blocks.one the watermark methods is based on DCT, which is robust against small levels lossy compression. Regions are characterized using ranges standard deviation (σ R is the standard deviation a region R). Standard deviation gives an indication the uniformity/non-uniformity a region. Each region on a 8-bit grey scale medical image falls into one the following four ranges, where σ min and σ max are the lower and upper bounds respectively. TABLE II. Ranges region characterization values Region σ min σ max Two watermark methods have been selected, LSB and DCT. LSB was selected for sensitive image regions, as it is known to cause minimal perceptual degradation. The DCT method was selected as a more robust watermarking choice and is based on an existing algorithm [8]. Very light capacities have been selected, one or two bits per region, again to minimize perceptual damage caused to a region. The perceptual metric that was selected for this operation is the structural comparison SC metric, which is an integral component the structural similarity index measure [12]. SC has been selected because it compares the structural similarity raw and watermarked regions. Structural similarity is an essential factor when considering differences in images that are detected by the human visual system [12]. SC is related to standard deviation, and is given by: SC(x, y) = (σ xy + K) / (σ x σ y + K) where σx and σy are the standard deviations regions x and y respectively, σxy is the estimated correlation coefficient and K is a small constant to map SC to a convenient range [ 1, 1]. Two alternative thresholds close to 1 have been specified to determine the amount visual similarity that must exist between image regions. The first allows for almost no visual degradation, SC that is quality 0.The second is SC 0.98, allowing for slightly more degradation, that is quality 1. The most appropriate watermark method and capacity can be determined for each region type using either threshold, depending on the level perceptual similarity required. The final outcome Phase I is a table that specifies the most appropriate watermark method and payload capacity for each standard deviation range. V. EMBEDDING AND EXTRACTION ALGORITHM The watermarking encoder performs the following: 1. Divide the grey scale raw medical image into 8 8 blocks. 2. For each block: (a) Compute the standard deviation σ R. (b) Classify the block into 4 regions based on σ R values asin Table 2. (c) Watermark each region with a section the payload by the 4 methods as mentioned in Table 1. (d) Compare the original and the watermarked region based on structural comparison (SC) metric. SC(x,y) = (σ xy + K) / (σ x σ y + K) (e) Use the WM selection table to obtain the most appropriate watermark method and capacity for that region. To extract the watermark, the following procedure is used: 1. Divide the watermarked image into 8 8 blocks. 2. For each block: (a) Compute σ R. (b) Use Phase I results (the WM selection table) to obtain the most appropriate watermark method and capacity for that region. (c) Extract section the payload from the region. A key issue that arises with this watermarking method is the possibility that regions may not be characterized in the same standard deviation range before and after watermarking. The decoding method is only acceptable if the number falsely decoded regions is small. Experimental results this and other issues are presented in the following section.

4 MIT International Journal Electrical and Instrumentation Engineering Vol. 1, No. 1, Jan. 2011, pp VI. RESULTS Different types medical images have been watermarked using the two phases the watermarking system. The outcomes the data gathering stage Phase I and the encoding/decoding stages Phase II are presented here. In the current implementation Phase I, test regions were generated that are typical those found on grey scale medical images. The test regions were categorized into the four standard deviation ranges outlined in Table 2, and the most appropriate watermark method and capacity for each range were determined. The test regions were 8 8 image blocks that are representative grey scale medical images. Blocks were created by randomly assigning pixel intensities with standard deviations that fall into the four standard deviation categories. 100 sample blocks were created for each standard deviation range, to cover a wide variety options. Each block was watermarked using the four preset methods and capacities: DCT2, DCT1, LSB2 and LSB1. For each watermarking method and capacity, the raw and watermarked regions were compared using the structural comparison metric. The minimum (worst) SC value for each standard deviation range is tabulated below. Table 3 was created using experimentally generated blocks random pixels. It was essential to determine whether the test regions are indicative actual medical image regions. That is, 8 8 medical image blocks must produce SC values that are greater than the minimum values outlined for Table 3 to be used as the WM selection table. Two specific types medical images were used for initial experimentation: MRI head scans and CT brain scan images. Table 3 is the resultant WM selection table Phase I, which can be used to select the optimal watermark method and capacity for specific image region types. For example, the dark shaded areas show which methods and capacities may be used if all image regions must have a perceptual similarity SC (when comparing the raw and watermarked regions). The light and dark shaded regions show which methods are permitted for a perceptual threshold SC If more than one method and capacity can be used per region, the one that can embed the greatest amount information, and is most robust is selected. For example, DCT with 2 changed bits per block is allowed for ranges >2 6 that have perceptual similarities SC TABLE III. Selection table for watermarking method and payload capacity for σ ranges and SC values. σ MIN σ MAX DCT DCT LSB LSB (a) (c) (b) (d) Fig. 6(a). Original MR head scan image, (b) Payload that is embedded, (c) Watermarked Image, (d) Difference between Original and Watermarked Image Payload extracted after Decoding: Quality 0 (SC 0.985) Error Analysis: q u a l i t y Block error regi on error Quality 1 (SC 0.98) Bit error

5 MIT International Journal Electrical and Instrumentation Engineering Vol. 1, No. 1, Jan. 2011, pp (a) (c) Fig. 7(a). Original CT brain scan image, (b) Payload that is embedded, (c) Watermarked Image, (d) Difference between Original and Watermarked Image Payload extracted after Decoding: (b) (d) Quality 0 Quality 1 Error Analysis: q u a l i t y Block errors region errors Bit errors VII. FACTORS ENHANCING RESISTANCE TO ATTACKS IN THE PROPOSED SYSTEM We will now analyze the approach which we have developed to establish watermarking at an acceptable level image degradation. The principles on which this approach rests are: (a) Use a light payload capacity with sufficient information to specify identifying details; (b) Use different watermark methods and embedding densities in different images, according to visual perceptibility measures. The design the payload must ideally satisfy several criteria. A fundamental need is to identify the patient, for which purpose full names, date birth and sex are usually the minimum requirement in medical applications. A unique patient identifying number may also be required in some systems. The total size this essential text information is the order 100 bytes. Other information relevant to the examination may also be included, such as the imaging site, imaging equipment and settings, clinician and technologist names, purpose examination, nature the episode care within which the examination is being made. These items are less important for security purposes, and may be included in the image header or other medical records rather than expanding payload capacity. Another payload component that may be important is a summary the image in the form a reduced resolution thumbnail, which provides a quick visual check that gross changes have not been made. A typical pixel reduction factor for this is 1000:1 (i.e blocks pixels encoded as one macro pixel) and in addition a binary macro pixel value may be adopted giving a further factor 10 reductions. For a medical image say 10 MB, a thumbnail may therefore be represented in approximately 1000 bytes. Further embellishment image information may be achieved by including checksums for the pixels covered in a macro pixel, or by including other content related information such as regions interest or locations diagnostically significant image features. For both text and image based payload content, the lightest useful payloads are therefore the order hundreds to thousands bytes information. This implies that the watermark information will only affect isolated pixels in large images multi-mega pixel size, when it is spread uniformly through the image. In order to provide further robustness, watermarks are ten embedded repeatedly several times throughout the image [14]. Within the constraints embedding density to be explored further below, it is therefore possible that watermarks the order thousands bytes information might be utilized. The choice watermarking methods and their embedding densities is the second consideration in our approach. Two popular families watermarking methods are spatial or pixel-based techniques, which modify pixel intensity values directly at particular positions in the image, and spectral or frequency-based techniques, which spread the modifications across a number adjacent pixels according to a decomposition the spectral properties those pixels [13]. In order to achieve both fragile and robust behavior in our watermarking, we may choose to apply both techniques with two different payloads. For example, the essential text information may be inserted with a spatial technique (such as modification the Least Significant Bit, or LSB), while the less critical thumbnail information may be inserted with a spectral technique (such as the Discrete Cosine Transformation, or DCT).

6 MIT International Journal Electrical and Instrumentation Engineering Vol. 1, No. 1, Jan. 2011, pp VIII. CONCLUSION AND FUTURE WORK This paper presented a medical image watermarking model whereby medical image data is used to determine the most appropriate watermark method and capacity for each region on a medical image. The model was invented to preserve the visual integrity medical images, which must not be compromised by watermarking. The model presented in this paper considers the most appropriate watermark method and capacity for each 8 8 block in a medical image. It was shown that using the structural comparison metric, less perceptual damage is caused to areas high standard deviation than low standard deviations. The watermarking system presented in this paper demonstrates that specific regions in medical images can be watermarked with specific watermarking methods and payload capacities, so that perceptual damage caused by watermarking is minimized. The medical images selected are size for CT images and MRI images and the maximum possible payload embedding capacity with minimum perceptual degradation was found to be Some future options that can be explored for the medical image watermarking model are: Alternative region definitions, other than 8 8 blocks. More sophisticated region characterizations, tailored to medical images (for example, segmentation into different tissue types). Other perceptual similarity, or error, metrics (for example, ones that take luminance and contrast into account). This type system is appropriate for medical images because the amount perceptual degradation caused by watermarking is limited. At the same time medical image data is protected by watermarking, which enhances the security the data. REFERENCES [1] Coatrieux, G., Main, H., Sankur, B., Rolland, Y. and Collorec, R. Relevance Watermarking in Medical Imaging. In IEEE-embs Information Technology Applications in Biomedicine, pp , Nov [2] A Study Block-based Medical Image Watermarking Using a Perceptual Similarity Metric - Birgit M. Planitz and Anthony J. Maeder Proceedings the Digital Imaging Computing: Techniques and Applications (DICTA 2005) 2005 IEEE. [3] Giakoumaki, A., Pavlopoulos, S., and Koutsouris, D. A Medical Image Watermarking Scheme Basedon Wavelet Transform. In Proc. the 25 th Annual Int. Conf. the IEEE-EMBS, pp , Sept [4] Jagadish, N., Subbanna Bhat, P., Acharya, R., and Niranjan, U. C. Simultaneous sto medical images in the spatial and frequency domain: a comparative study. Biomedical Engineering Online, Vol. 3, No. 1, June [5] Johnson, N.F., Duric, Z. and Jajodia, S. Information Hiding: Steganography and Watermarking Attacks and Countermeasures Kluwer Academic Press, [6] Kong, X. and Feng, R. Watermarking Medical Signals for Telemedicine. IEEE Trans on. Information Technology in Biomedicine, Vol. 5, No. 3, pp , Sept [7] Watermark Method Suitable for Medical Images with Error Correction. RSNA 88th Scientific Assembly and Annual Meeting, Dec (abstract). [8] Osborne, D., Abbott, S., Sorell, M. and Rogers, D. Multiple Embedding Using Robust Watermarks for Wireless Medical Images. In IEEE Symposium on Electronics and Telecommunications, pp. Section 13(34), Oct [9] Puech, W. and Rodrigues, J.M. A New Crypto- Watermarking Method for Medical Images Safe Transfer. In The 12th European Signal Processing Conference, pp , Sept [10] Tachibana, H., Harauchi, H., Ikeda, T., Iwata, Y., Takemura, A. and Umeda, T. Practical Use New Watermarking and VPN Techniques for Medical Image Communication and Archive. RSNA 88th Scientific Assembly and Annual Meeting, Dec [11] Wakatani, A. Digital watermarking for ROI medical images by using compressed signature image. In Annual Hawaii Int. Conf. on System Science, pp , Jan [12] Wang, Z., Bovik, A.C., Sheikh, H.R., and Simoncelli, E.P. Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Trans. on Image Processing, Vol. 13, No. 4, pp , Apr [13] Arnold, M., Schmucker, M., and Wolthusen, S.D. Digital Watermarking and Content Protection. Archtech House, Inc., [14] Woo, C.S., Du, J. and Pham, B. Multiple watermark method for privacy control and tamper detection in medical images. In APRS Workshop on Digital Image Computing, pp , 2005.

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