A Novel Robust Watermarking Algorithm for Encrypted Medical Image Based on Bandelet-DCT

Fang, Yangxiu; Liu, Jing; Li, Jingbing; Dan, Yi; Cui, Wenfeng; Xiao, Xinping; Han, Baoru; Bhatti, Uzair Aslam

doi:10.1007/978-981-16-3013-2_6

Cited by 12 publications

(3 citation statements)

References 12 publications

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“…The testing of the system suggests that the methodology used can differentiate the liver as having no damage, minimal damage, significant damage, severe damage, and cirrhosis. Additionally, the most widely used approach in this disease diagnosis is image processing [63][64][65][66]. But, this approach fails to detect it until or unless any physical symptoms have appeared in the liver.…”

Section: Defuzzifiermentioning

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: Defuzzifiermentioning

confidence: 99%

Fuzzy Logic-Based System for Liver Fibrosis Disease

Alkhalifah¹,

Singla²,

Alurise³

2023

CSSE

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“…Fang et al [ 51 ] illustrated a new encryption algorithm to secure the host medical image to protect private information; the author’s algorithm is based on Bandelet Transform and Discrete Cosine Transform. The medical image is first encrypted using a Logistic chaotic map, and then the features are extracted using Bandelet Transform.…”

Section: Cryptographymentioning

confidence: 99%

Security of medical images for telemedicine: a systematic review

Magdy

Hosny

Ghali

et al. 2022

Multimed Tools Appl

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Recently, there has been a rapid growth in the utilization of medical images in telemedicine applications. The authors in this paper presented a detailed discussion of different types of medical images and the attacks that may affect medical image transmission. This survey paper summarizes existing medical data security approaches and the different challenges associated with them. An in-depth overview of security techniques, such as cryptography, steganography, and watermarking are introduced with a full survey of recent research. The objective of the paper is to summarize and assess the different algorithms of each approach based on different parameters such as PSNR, MSE, BER, and NC.

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“…When a convolutional neural network (CNN) is used to extract the rain trace information, it is easy to accidentally extract and remove the background information. It is worth to mention that although generative adversarial networks (GANs) can reconstruct a more realistic image after rain removal [9,10], performance evaluation reveals that their performance is actually worse than the CNN-based reconstruction method. It can be inferred that many of the detail textures reconstructed by GANs are false and unreal.…”

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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A Novel Robust Watermarking Algorithm for Encrypted Medical Image Based on Bandelet-DCT

Cited by 12 publications

References 12 publications

Fuzzy Logic-Based System for Liver Fibrosis Disease

Fuzzy Logic-Based System for Liver Fibrosis Disease

Security of medical images for telemedicine: a systematic review

Towards Robust Rain Removal with Unet++

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