Robust Zero Watermarking Algorithm for Medical Images Using Local Binary Pattern and Discrete Cosine Transform

Liu, Wenyi; Li, Jingbing; Shao, Chunyan; Ma, Jixin; Huang, Mengxing; Bhatti, Uzair Aslam

doi:10.1007/978-3-031-06764-8_28

Cited by 12 publications

(8 citation statements)

References 16 publications

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“…There are many methods used in the literature for the diagnosis of liver fibrosis disease. Among them, image processing is a widely and universally utilized approach that assists in the detection by using images [16][17][18][19]. The method of image processing, i.e., segmentation, was used for the detection of this illness in the early phase [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic-Based System for Liver Fibrosis Disease

Alkhalifah¹,

Singla²,

Alurise³

2023

CSSE

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The diagnosis of liver fibrosis (LF) is crucial as it is a deadly and life-threatening disease. Artificial intelligence techniques aid doctors by using the previous data on health and making a diagnostic system, which helps to take decisions about patients' health as experts can. The historical data of a patient's health can have vagueness, inaccurate, and can also have missing values. The fuzzy logic theory can deal with these issues in the dataset. In this paper, a multilayer fuzzy expert system is developed to diagnose LF. The model is created by using multiple layers of the fuzzy logic approach. This system aids in classifying the health of patients into different classes. The proposed method has two layers, i.e., layer 1 and layer 2. The input variables used in layer 1 for diagnosing liver fibrosis are Appetite, Jaundice, Ascites, Age, and Fatigue. Similarly, in layer 2, the input variables are Platelet count, White blood cell count, spleen, SGPT ALT (Serum Glutamic Pyruvic Transaminase Alanine Aminotransferase), SGOT ALT (Serum Glutamicoxalacetic Transaminase Alanine Aminotransferase), Serum bilirubin, and Serum albumin. The output variables for this developed system are no damage, minimal damage, significant damage, severe damage, and cirrhosis. This research work also presents the examination of results based on performance parameters. The proposed system achieves a classification accuracy of 95%. Moreover, other performance parameters such as sensitivity, specificity, and precision are calculated as 97.14%, 92%, and 94.44%, respectively.

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Section: Introductionmentioning

confidence: 99%

Fuzzy Logic-Based System for Liver Fibrosis Disease

Alkhalifah¹,

Singla²,

Alurise³

2023

CSSE

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“…Subsequently, we will organize and compare the experimental and analytical results obtained for each image under different attack types and intensities. In this section, we will not only conduct a comprehensive analysis and comparison of the experiments conducted with our designed framework but also extend our analysis to compare the results with other recent methods that utilize zero-watermarking, such as Xing et al [12], Huang et al [13], and Liu et al [14]. This comparative analysis aims to provide a more objective and impartial perspective on the effectiveness of our proposed method in the context of the latest approaches in the field of zero-watermarking.…”

Section: Experimental Analysis and Resultsmentioning

confidence: 99%

“…This sequence is employed to distinguish the zero-watermark from the feature sequence of the encrypted watermark image. Huang et al [13] employs a pre-trained DO-VGG model to extract deep abstract features from medical images and generates the zero-watermark using a perceptual hashing algorithm. Additionally, Liu et al [14] introduced a method that combines Local Binary Patterns (LBP) with Discrete Cosine Transform (DCT).…”

Section: Introductionmentioning

confidence: 99%

Robust Zero-Watermarking by Circular Features and 1-D NRDPWT Transformation

Hsiu-Chi Tseng,

Hsiu-Chi Tseng

2024

Journal of Computers

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<p>This paper introduces a secure and robust zero-watermarking framework that leverages the advantages of zero-watermarking, ensuring non-destructive modification of original images and unlimited capacity. The proposed method enables robust watermark embedding while preserving the original image. It employs a novel feature extraction approach using circular areas based on image radius, enhancing feature resilience. Additionally, applying one-dimensional non-recursive discrete periodized wavelet transform (1-D NRDPWT) converts feature values into phi, contributing to enhanced stability and robustness. Enhanced security is achieved through the use of Shuffle and Pseudo-Random Number Generator (PRNG). Experimental results, evaluated using metrics such as Bit Error Rate (BER) and Normalized Correlation (NC), validate the exceptional performance of this watermarking technique. These findings underscore the framework’s robustness, security, reliability, and integrity against both general and geometric noise attacks, making it a secure and robust solution for modern digital image copyright protection. In summary, our method offers an effective defense against various noise attacks while ensuring the highest watermark quality without compromising the original image. It is a significant advancement in copyright protection applications.</p> <p> </p>

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“…Image rain removal is an image preprocessing method that deals with the inverse problem of removing the rain effect from an image, while highlighting its details to meet application-specific requirements and make it more suitable for human-machine recognition [1,2]. It is common to find rain in videos and images taken under bad weather conditions [3,4]. The presence of rain not only negatively affects the visual quality of an image or video, but also reduces the performance of applicationspecific tasks, such as object segmentation, recognition, tracking, and autonomous driving [5,6].…”

Section: Introductionmentioning

confidence: 99%

Towards Robust Rain Removal with Unet++

Hu¹,

Sun²,

Yang³

et al. 2023

Computers, Materials &Amp; Continua

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Image deraining has become a hot topic in the field of computer vision. It is the process of removing rain streaks from an image to reconstruct a high-quality background. This study aims at improving the performance of image rain streak removal and reducing the disruptive effects caused by rain. To better fit the rain removal task, an innovative image deraining method is proposed, where a kernel prediction network with Unet++ is designed and used to filter rainy images, and rainy-day images are used to estimate the pixel-level kernel for rain removal. To minimize the gap between synthetic and real data and improve the performance in real rainy image handling, a loss function and an effective data optimization method are suggested. In contrast with other methods, the loss function consists of Structural Similarity Index loss, edge loss, and L1 loss, and it is adopted to improve performance. The proposed algorithm can improve the Peak Signal-to-Noise ratio by 1.3% when compared to conventional approaches. Experimental results indicate that the proposed method can achieve a better efficiency and preserve more image structure than several classical methods.

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Robust Zero Watermarking Algorithm for Medical Images Using Local Binary Pattern and Discrete Cosine Transform

Cited by 12 publications

References 16 publications

Fuzzy Logic-Based System for Liver Fibrosis Disease

Fuzzy Logic-Based System for Liver Fibrosis Disease

Robust Zero-Watermarking by Circular Features and 1-D NRDPWT Transformation

Towards Robust Rain Removal with Unet++

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