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Evaluation of medical image watermarking with tamper detection and recovery (AW-TDR)

Zain JM, Fauzi AR

Annu Int Conf IEEE Eng Med Biol Soc, 2007;2007:5662-5.
PMID: 18003297

This paper will study and evaluate watermarking technique by Zain and Fauzi [1]. Recommendations will then be made to enhance the technique especially in the aspect of recovery or reconstruction rate for medical images. A proposal will also be made for a better distribution of watermark to minimize the distortion of the Region of Interest (ROI). The final proposal will enhance AW-TDR in three aspects; firstly the image quality in the ROI will be improved as the maximum change is only 2 bits in every 4 pixels, or embedding rate of 0.5 bits/pixel. Secondly the recovery rate will also be better since the recovery bits are located outside the region of interest. The disadvantage in this is that, only manipulation done in the ROI will be detected. Thirdly the quality of the reconstructed image will be enhanced since the average of 2 x 2 pixels would be used to reconstruct the tampered image.
Medical image watermarking with tamper detection and recovery

Zain JM, Fauzi AM

Conf Proc IEEE Eng Med Biol Soc, 2007 10 20;2006:3270-3.
PMID: 17945763

This paper discussed security of medical images and reviewed some work done regarding them. A fragile watermarking scheme was then proposed that could detect tamper and subsequently recover the image. Our scheme required a secret key and a public chaotic mixing algorithm to embed and recover a tampered image. The scheme was also resilient to VQ attack. The purposes were to verify the integrity and authenticity of medical images. We used 800 x 600 x 8 bits ultrasound (US) greyscale images in our experiment. We tested our algorithm for up to 50% tampered block and obtained 100% recovery for spread-tampered block.
Fulltext Tamper localization and lossless recovery watermarking scheme with ROI segmentation and multilevel authentication

Liew SC, Liew SW, Zain JM

J Digit Imaging, 2013 Apr;26(2):316-25.
PMID: 22555905 DOI: 10.1007/s10278-012-9484-4

Tamper localization and recovery watermarking scheme can be used to detect manipulation and recover tampered images. In this paper, a tamper localization and lossless recovery scheme that used region of interest (ROI) segmentation and multilevel authentication was proposed. The watermarked images had a high average peak signal-to-noise ratio of 48.7 dB and the results showed that tampering was successfully localized and tampered area was exactly recovered. The usage of ROI segmentation and multilevel authentication had significantly reduced the time taken by approximately 50 % for the tamper localization and recovery processing.
Clinical evaluation of watermarked medical images

Zain JM, Fauzi AM, Aziz AA

Conf Proc IEEE Eng Med Biol Soc, 2007 10 20;2006:5459-62.
PMID: 17946306

Digital watermarking medical images provides security to the images. The purpose of this study was to see whether digitally watermarked images changed clinical diagnoses when assessed by radiologists. We embedded 256 bits watermark to various medical images in the region of non-interest (RONI) and 480K bits in both region of interest (ROI) and RONI. Our results showed that watermarking medical images did not alter clinical diagnoses. In addition, there was no difference in image quality when visually assessed by the medical radiologists. We therefore concluded that digital watermarking medical images were safe in terms of preserving image quality for clinical purposes.
Fulltext Parallel Digital Watermarking Process on Ultrasound Medical Images in Multicores Environment

Khor HL, Liew SC, Zain JM

Int J Biomed Imaging, 2016;2016:9583727.
PMID: 26981111 DOI: 10.1155/2016/9583727

With the advancement of technology in communication network, it facilitated digital medical images transmitted to healthcare professionals via internal network or public network (e.g., Internet), but it also exposes the transmitted digital medical images to the security threats, such as images tampering or inserting false data in the images, which may cause an inaccurate diagnosis and treatment. Medical image distortion is not to be tolerated for diagnosis purposes; thus a digital watermarking on medical image is introduced. So far most of the watermarking research has been done on single frame medical image which is impractical in the real environment. In this paper, a digital watermarking on multiframes medical images is proposed. In order to speed up multiframes watermarking processing time, a parallel watermarking processing on medical images processing by utilizing multicores technology is introduced. An experiment result has shown that elapsed time on parallel watermarking processing is much shorter than sequential watermarking processing.
Fulltext Region of Interest-Based Tamper Detection and Lossless Recovery Watermarking Scheme (ROI-DR) on Ultrasound Medical Images

Khor HL, Liew SC, Zain JM

J Digit Imaging, 2017 Jun;30(3):328-349.
PMID: 28050716 DOI: 10.1007/s10278-016-9930-9

Tampering on medical image will lead to wrong diagnosis and treatment, which is life-threatening; therefore, digital watermarking on medical image was introduced to protect medical image from tampering. Medical images are divided into region of interest (ROI) and region of non-interest (RONI). ROI is an area that has a significant impact on diagnosis, whereas RONI has less or no significance in diagnosis. This paper has proposed ROI-based tamper detection and recovery watermarking scheme (ROI-DR) that embeds ROI bit information into RONI least significant bits, which will be extracted later for authentication and recovery process. The experiment result has shown that the ROI-DR has achieved a good result in imperceptibility with peak signal-to-noise ratio (PSNR) values approximately 48 dB, it is robust against various kinds of tampering, and the tampered ROI was able to recover to its original form. Lastly, a comparative table with the previous research (TALLOR and TALLOR-RS watermarking schemes) has been derived, where these three watermarking schemes were tested under the same testing conditions and environment. The experiment result has shown that ROI-DR has achieved speed-up factors of 22.55 and 26.65 in relative to TALLOR and TALLOR-RS watermarking schemes, respectively.
Fulltext Watermark Compression in Medical Image Watermarking Using Lempel-Ziv-Welch (LZW) Lossless Compression Technique

Badshah G, Liew SC, Zain JM, Ali M

J Digit Imaging, 2016 Apr;29(2):216-25.
PMID: 26429361 DOI: 10.1007/s10278-015-9822-4

In teleradiology, image contents may be altered due to noisy communication channels and hacker manipulation. Medical image data is very sensitive and can not tolerate any illegal change. Illegally changed image-based analysis could result in wrong medical decision. Digital watermarking technique can be used to authenticate images and detect as well as recover illegal changes made to teleradiology images. Watermarking of medical images with heavy payload watermarks causes image perceptual degradation. The image perceptual degradation directly affects medical diagnosis. To maintain the image perceptual and diagnostic qualities standard during watermarking, the watermark should be lossless compressed. This paper focuses on watermarking of ultrasound medical images with Lempel-Ziv-Welch (LZW) lossless-compressed watermarks. The watermark lossless compression reduces watermark payload without data loss. In this research work, watermark is the combination of defined region of interest (ROI) and image watermarking secret key. The performance of the LZW compression technique was compared with other conventional compression methods based on compression ratio. LZW was found better and used for watermark lossless compression in ultrasound medical images watermarking. Tabulated results show the watermark bits reduction, image watermarking with effective tamper detection and lossless recovery.
Fulltext Watermarking of ultrasound medical images in teleradiology using compressed watermark

Badshah G, Liew SC, Zain JM, Ali M

J Med Imaging (Bellingham), 2016 Jan;3(1):017001.
PMID: 26839914 DOI: 10.1117/1.JMI.3.1.017001

The open accessibility of Internet-based medical images in teleradialogy face security threats due to the nonsecured communication media. This paper discusses the spatial domain watermarking of ultrasound medical images for content authentication, tamper detection, and lossless recovery. For this purpose, the image is divided into two main parts, the region of interest (ROI) and region of noninterest (RONI). The defined ROI and its hash value are combined as watermark, lossless compressed, and embedded into the RONI part of images at pixel's least significant bits (LSBs). The watermark lossless compression and embedding at pixel's LSBs preserve image diagnostic and perceptual qualities. Different lossless compression techniques including Lempel-Ziv-Welch (LZW) were tested for watermark compression. The performances of these techniques were compared based on more bit reduction and compression ratio. LZW was found better than others and used in tamper detection and recovery watermarking of medical images (TDARWMI) scheme development to be used for ROI authentication, tamper detection, localization, and lossless recovery. TDARWMI performance was compared and found to be better than other watermarking schemes.
RERS-CC: Robotic facial recognition system for improving the accuracy of human face identification using HRI

Jing W, Tao H, Rahman MA, Kabir MN, Yafeng L, Zhang R, et al.

Work, 2021;68(3):923-934.
PMID: 33612534 DOI: 10.3233/WOR-203426

BACKGROUND: Human-Computer Interaction (HCI) is incorporated with a variety of applications for input processing and response actions. Facial recognition systems in workplaces and security systems help to improve the detection and classification of humans based on the vision experienced by the input system.
OBJECTIVES: In this manuscript, the Robotic Facial Recognition System using the Compound Classifier (RERS-CC) is introduced to improve the recognition rate of human faces. The process is differentiated into classification, detection, and recognition phases that employ principal component analysis based learning. In this learning process, the errors in image processing based on the extracted different features are used for error classification and accuracy improvements.
RESULTS: The performance of the proposed RERS-CC is validated experimentally using the input image dataset in MATLAB tool. The performance results show that the proposed method improves detection and recognition accuracy with fewer errors and processing time.
CONCLUSION: The input image is processed with the knowledge of the features and errors that are observed with different orientations and time instances. With the help of matching dataset and the similarity index verification, the proposed method identifies precise human face with augmented true positives and recognition rate.
Fulltext Acute Kidney Injury following Rhabdomyolysis in Critically Ill Patients

Saverymuthu A, Teo R, Zain JM, Cheah SK, Yusof AM, Rahman RA

J Crit Care Med (Targu Mures), 2021 Oct;7(4):267-271.
PMID: 34934816 DOI: 10.2478/jccm-2021-0025

Introduction: Rhabdomyolysis, which resulted from the rapid breakdown of damaged skeletal muscle, potentially leads to acute kidney injury.
Aim: To determine the incidence and associated risk of kidney injury following rhabdomyolysis in critically ill patients.
Methods: All critically ill patients admitted from January 2016 to December 2017 were screened. A creatinine kinase level of > 5 times the upper limit of normal (> 1000 U/L) was defined as rhabdomyolysis, and kidney injury was determined based on the Kidney Disease Improving Global Outcome (KDIGO) score. In addition, trauma, prolonged surgery, sepsis, antipsychotic drugs, hyperthermia were included as risk factors for kidney injury.
Results: Out of 1620 admissions, 149 (9.2%) were identified as having rhabdomyolysis and 54 (36.2%) developed kidney injury. Acute kidney injury, by and large, was related to rhabdomyolysis followed a prolonged surgery (18.7%), sepsis (50.0%) or trauma (31.5%). The reduction in the creatinine kinase levels following hydration treatment was statistically significant in the non- kidney injury group (Z= -3.948, p<0.05) compared to the kidney injury group (Z= -0.623, p=0.534). Significantly, odds of developing acute kidney injury were 1.040 (p<0.001) for mean BW >50kg, 1.372(p<0.001) for SOFA Score >2, 5.333 (p<0.001) for sepsis and the multivariate regression analysis showed that SOFA scores >2 (p<0.001), BW >50kg (p=0.016) and sepsis (p<0.05) were independent risk factors. The overall mortality due to rhabdomyolysis was 15.4% (23/149), with significantly higher incidences of mortality in the kidney injury group (35.2%) vs the non- kidney injury (3.5%) [ p<0.001].
Conclusions: One-third of rhabdomyolysis patients developed acute kidney injury with a significantly high mortality rate. Sepsis was a prominent cause of acute kidney injury. Both sepsis and a SOFA score >2 were significant independent risk factors.
Fulltext Accuracy Analysis of Deep Learning Methods in Breast Cancer Classification: A Structured Review

Yusoff M, Haryanto T, Suhartanto H, Mustafa WA, Zain JM, Kusmardi K

Diagnostics (Basel), 2023 Feb 11;13(4).
PMID: 36832171 DOI: 10.3390/diagnostics13040683

Breast cancer is diagnosed using histopathological imaging. This task is extremely time-consuming due to high image complexity and volume. However, it is important to facilitate the early detection of breast cancer for medical intervention. Deep learning (DL) has become popular in medical imaging solutions and has demonstrated various levels of performance in diagnosing cancerous images. Nonetheless, achieving high precision while minimizing overfitting remains a significant challenge for classification solutions. The handling of imbalanced data and incorrect labeling is a further concern. Additional methods, such as pre-processing, ensemble, and normalization techniques, have been established to enhance image characteristics. These methods could influence classification solutions and be used to overcome overfitting and data balancing issues. Hence, developing a more sophisticated DL variant could improve classification accuracy while reducing overfitting. Technological advancements in DL have fueled automated breast cancer diagnosis growth in recent years. This paper reviewed studies on the capability of DL to classify histopathological breast cancer images, as the objective of this study was to systematically review and analyze current research on the classification of histopathological images. Additionally, literature from the Scopus and Web of Science (WOS) indexes was reviewed. This study assessed recent approaches for histopathological breast cancer image classification in DL applications for papers published up until November 2022. The findings of this study suggest that DL methods, especially convolution neural networks and their hybrids, are the most cutting-edge approaches currently in use. To find a new technique, it is necessary first to survey the landscape of existing DL approaches and their hybrid methods to conduct comparisons and case studies.
Security robot for the prevention of workplace violence using the Non-linear Adaptive Heuristic Mathematical Model

Tao H, Rahman MA, Al-Saffar A, Zhang R, Salih SQ, Zain JM, et al.

Work, 2021;68(3):853-861.
PMID: 33612528 DOI: 10.3233/WOR-203419

BACKGROUND: Nowadays, workplace violence is found to be a mental health hazard and considered a crucial topic. The collaboration between robots and humans is increasing with the growth of Industry 4.0. Therefore, the first problem that must be solved is human-machine security. Ensuring the safety of human beings is one of the main aspects of human-robotic interaction. This is not just about preventing collisions within a shared space among human beings and robots; it includes all possible means of harm for an individual, from physical contact to unpleasant or dangerous psychological effects.
OBJECTIVE: In this paper, Non-linear Adaptive Heuristic Mathematical Model (NAHMM) has been proposed for the prevention of workplace violence using security Human-Robot Collaboration (HRC). Human-Robot Collaboration (HRC) is an area of research with a wide range of up-demands, future scenarios, and potential economic influence. HRC is an interdisciplinary field of research that encompasses cognitive sciences, classical robotics, and psychology.
RESULTS: The robot can thus make the optimal decision between actions that expose its capabilities to the human being and take the best steps given the knowledge that is currently available to the human being. Further, the ideal policy can be measured carefully under certain observability assumptions.
CONCLUSION: The system is shown on a collaborative robot and is compared to a state of the art security system. The device is experimentally demonstrated. The new system is being evaluated qualitatively and quantitatively.
The comparison between propofol and dexmedetomidine infusion on perioperative anxiety during regional anesthesia

Lim YP, Yahya N, Izaham A, Kamaruzaman E, Zainuddin MZ, Wan Mat WR, et al.

Turk J Med Sci, 2018 Dec 12;48(6):1219-1227.
PMID: 30541250 DOI: 10.3906/sag-1802-126

Background/aim: Regional anesthesia for surgery is associated with increased anxiety for patients. This study aimed to compare the
effect of propofol and dexmedetomidine infusion on perioperative anxiety during regional anesthesia.
Materials and methods: Eighty-four patients were randomly divided into two groups receiving either study drug infusion. Anxiety
score, level of sedation using the Bispectral Index and Observer’s Assessment of Alertness and Sedation, hemodynamic stability, and
overall patient’s feedback on anxiolysis were assessed.
Results: Both groups showed a significant drop in mean anxiety score at 10 and 30 min after starting surgery. Difference in median
anxiety scores showed a significant reduction in anxiety score at the end of the surgery in the dexmedetomidine group compared to the
propofol group. Dexmedetomidine and propofol showed a significant drop in mean arterial pressure in the first 30 min and first 10 min
respectively. Both drugs demonstrated a significant drop in heart rate in the first 20 min from baseline after starting the drug infusion.
Patients in the dexmedetomidine group (76.20%) expressed statistically excellent feedback on anxiolysis compared to patients in the
propofol group (45.20%).
Conclusion: Dexmedetomidine infusion was found to significantly reduce anxiety levels at the end of surgery compared to propofol
during regional anesthesia.

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