Watermark Compression in Medical Image Watermarking Using Lempel-Ziv-Welch (LZW) Lossless Compression Technique

Badshah, Gran; Liew, Siau-Chuin; Zain, Jasni Mohamad; Ali, Mushtaq

doi:10.1007/s10278-015-9822-4

Cited by 75 publications

(42 citation statements)

References 24 publications

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“…The performance of the proposed compression methods for 3.0 and 5.0 mm ISD values are chosen to represent the efficiency of the compression methods as these values are the most frequent ISD values used in clinical routine. Six datasets (i.e., datasets [14][15][16][17][18][19] were acquired using the 320-row detector CT scanner with 0.8 mm ISD. In total, 3649 DICOM images, which have 512 Â 512 pixel resolution, are segmented manually to extract aorta and the main vessels departing from it.…”

Section: A the Datasets And Referencesmentioning

confidence: 99%

“…Consequently, due to the fact that any loss of information is critical with medical images, compression must reconstruct an image with no loss of informationthis mode of compression called lossless, that is, reversible, compression. 15 As a result, lossless compression techniques are more commonly preferred for compression of medical images. In a similar perspective, only lossless techniques are examined in this manuscript.…”

Section: Introductionmentioning

confidence: 99%

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Reversible 3D compression of segmented medical volumes: usability analysis for teleradiology and storage

et al. 2020

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Background: DICOM standard does not have modules that provide the possibilities of two-dimensional Presentation States to three-dimensional (3D). Once the final 3D rendering is obtained, only video/image exporting or snapshots can be used. To increase the utility of 3D Presentation States in clinical practice and teleradiology, the storing and transferring the segmentation results, obtained after tedious procedures, can be very effective. Purpose: To propose a strategy for preserving interaction and mobility of visualizations for teleradiology by storing and transferring only binary segmented data, which is effectively compressed by modern adaptive and context-based reversible methods. Material and Methods: A diverse set of segmented data, which include four abdominal organs (liver, spleen, right, and left kidneys) from 20 T1-DUAL and 20 T2-SPIR MRI, liver from 20 CT, and abdominal aorta with aneurysms (AAA) from 19 computed tomography-angiography datasets, are collected. Each organ is segmented manually by expert physicians, and binary volumes are created. The well-established reversible binary compression methods PNG, JPEG-LS, JPEG-XR, CCITT-G4, LZW, JBIG2, and ZIP are applied to medical datasets. Recently proposed context-based (3D-RLE) and adaptive (ABIC) algorithms are also employed. The performance assessment has been presented in terms of the compression ratio that is a universal compression metric. Results: Reversible compression of binary volumes results with substantial decreases in file size such as 254 to 2.14 MB for CT-AAA, 56.7 to 0.3 MB for CT-liver. Moreover, compared to the performance of well-established methods (i.e., mean 76.14%), CR is observed to be increased significantly for all segmented organs from both CT and MRI datasets when ABIC (95.49%) and 3D-RLE (94.98%) are utilized. The hypothesis is that morphological coherence of scanning procedure and adaptation between the segmented organs, that is, bi-level images, contributes to compression performance. Although the performance of well-established techniques is satisfactory, the sensitivity of ABIC to modality type and the advantage of 3D-RLE when the spatial coherence between the adjacent slices are high results with up to 10 times more CR performance. Conclusion: Adaptive and context-based compression strategies allow effective storage and transfer of segmented binary data, which can be used to re-produce visualizations for better teleradiology practices preserving all interaction mechanisms.

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Section: A the Datasets And Referencesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reversible 3D compression of segmented medical volumes: usability analysis for teleradiology and storage

et al. 2020

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“…4. Hidden Watermarking [10] In [2] Al-Husainy discussed a new approach for image security by using two simpler and efficient methods of confusion and diffusion, both are Boolean operations, the first is XOR operation which is performed on bits of digital image pixels and the former is to rotate pixel bits right circularly. The procedure is applied many times so the plain image becomes cipher image due to increasing demands of high speed networks.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A lossless compression watermarking technique was presented by Badshah et al [10] to secure sensitive images like medical images for example ultrasound, X-ray, CT scan, ECG, MRI images because the physicians have to take a decision depending on these medical reports for treatment. This LZW technique recovers alteration in images if changed due to noisy channel or intruder.…”

Section: Literature Reviewmentioning

confidence: 99%

Digital Image Security: Fusion of Encryption, Steganography and Watermarking

Abdur¹,

Ahmed²,

Adnan³

et al. 2017

ijacsa

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Abstract-Digital images are widely communicated over the internet. The security of digital images is an essential and challenging task on shared communication channel. Various techniques are used to secure the digital image, such as encryption, steganography and watermarking. These are the methods for the security of digital images to achieve security goals, i.e. confidentiality, integrity and availability (CIA). Individually, these procedures are not quite sufficient for the security of digital images. This paper presents a blended security technique using encryption, steganography and watermarking. It comprises of three key components: (1) the original image has been encrypted using large secret key by rotating pixel bits to right through XOR operation, (2) for steganography, encrypted image has been altered by least significant bits (LSBs) of the cover image and obtained stego image, then (3) stego image has been watermarked in the time domain and frequency domain to ensure the ownership. The proposed approach is efficient, simpler and secured; it provides significant security against threats and attacks.

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“…The watermark is compressed using the LZW lossless compression technique [14]. LZW compression reduces the watermark bits to a lower number in such a way that can be easily encapsulated into an image at LSBs [15]. At destination, the extracted watermark is used for image authentication, tamper localization and lossless recovery.…”

Section: Proposed Methodologymentioning

confidence: 99%

Secured Telemedicine Using Whole Image as Watermark with Tamper Localization and Recovery Capabilities

et al. 2015

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Region of interest (ROI) is the most informative part of a medical image and mostly has been used as a major part of watermark. Various shapes ROIs selection have been reported in region-based watermarking techniques. In region-based watermarking schemes an image region of non-interest (RONI) is the second important part of the image and is used mostly for watermark encapsulation. In online healthcare systems the ROI wrong selection by missing some important portions of the image to be part of ROI can create problem at the destination. This paper discusses the complete medical image availability in original at destination using the whole image as a watermark for authentication, tamper localization and lossless recovery (WITALLOR). The WITALLOR watermarking scheme ensures the complete image security without of ROI selection at the source point as compared to the other region-based watermarking techniques. The complete image is compressed using the Lempel-Ziv-Welch (LZW) lossless compression technique to get the watermark in reduced number of bits. Bits reduction occurs to a number that can be completely encapsulated into image. The watermark is randomly encapsulated at the least significant bits (LSBs) of the image without caring of the ROI and RONI to keep the image perceptual degradation negligible. After communication, the watermark is retrieved, decompressed and used for authentication of the whole image, tamper detection, localization and lossless recovery. WITALLOR scheme is capable of any number of tampers detection and recovery at any part of the image. The complete authentic image gives the opportunity to conduct an image based analysis of medical problem without restriction to a fixed ROI.

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Watermark Compression in Medical Image Watermarking Using Lempel-Ziv-Welch (LZW) Lossless Compression Technique

Cited by 75 publications

References 24 publications

Reversible 3D compression of segmented medical volumes: usability analysis for teleradiology and storage

Reversible 3D compression of segmented medical volumes: usability analysis for teleradiology and storage

Digital Image Security: Fusion of Encryption, Steganography and Watermarking

Secured Telemedicine Using Whole Image as Watermark with Tamper Localization and Recovery Capabilities

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