scholarly journals Parallel Digital Watermarking Process on Ultrasound Medical Images in Multicores Environment

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Hui Liang Khor ◽  
Siau-Chuin Liew ◽  
Jasni Mohd. Zain
Keyword(s):  
Digital Watermarking ◽  
Medical Image ◽  
Medical Images ◽  
Security Threats ◽  
Public Network ◽  
Elapsed Time ◽  
Single Frame ◽  
Internal Network ◽  
Speed Up ◽  
Images Processing

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.

An Imperceptible Watermarking Scheme for Medical Images Tamper Detection

2022 ◽  
Vol 16 (1) ◽  
pp. 0-0
Keyword(s):  
Secure Communication ◽  
Medical Image ◽  
Medical Images ◽  
Image Watermarking ◽  
Chaotic Map ◽  
Region Of Interest ◽  
Digital Image Watermarking ◽  
Watermark Image ◽  
Speed Up ◽  
Watermarking Scheme

One of the important issues in telemedicine field refers to an advanced secure communication. Digital image watermarking is an ideal solution since it protects the electronic patient information’s from unauthorized access. This paper presents a novel blind fragile-based image watermarking scheme in spatial domain that merges Speed Up Robust Features (SURF) descriptor with the well-known Weber Descriptors (WDs) and Arnold algorithm. It provides a good way for enhancing the image quality and time complexity for medical data integrity. Firstly, the watermark image is shuffled using Arnold chaotic map. Secondly, the SURF technique is practiced to Region of Interest (ROI) of the medical image and then the blocks around the SURF points are selected to insert the watermark. Finally, the watermark is encrusted and extracted using WDs. Experimental results show good image fidelity with the shortest execution time to ensure medical images integrity.

3D-Visualization for DICOM Series Based on ITK and VTK

2012 ◽  
Vol 263-266 ◽  
pp. 2530-2533 ◽  
Author(s):  
Hui Dong ◽  
Ling Xia ◽  
Zhi Peng ◽  
Jin Zhang
Keyword(s):  
Medical Image ◽  
Medical Images ◽  
3D Visualization ◽  
Three Dimensional ◽  
Large Data ◽  
Volume Data ◽  
Practical Applications ◽  
Novel Technology ◽  
Speed Up ◽  
Filter Algorithms

Nowadays, medical image processing and three-dimensional visualization have become a very important support for doctor diagnosis and treatment planning. It’s a novel technology that using ITK and VTK to process and display medical images in VC++. In this paper, the Curvature FlowImageFilter class of ITK libraries is used to denoise and smooth the medical images. The MC algorithms is used to reconstruct the volume data in the VS2008. Immediate Mode algorithms and Stripper filter algorithms are adopted to speed up large data processing. The experiment results demonstrate that using the MC algorithms and the acceleration algorithms base on the VTK can implement 3-D visualization efficiently and satisfy practical applications.

scholarly journals Joint Digital Water Marking for Medical Images for Improving Security

2018 ◽  
Vol 11 (2) ◽  
pp. 863-870 ◽  
Author(s):  
Kavitha K. J ◽  
Priestly B. Shan
Keyword(s):  
Digital Watermarking ◽  
Medical Image ◽  
Medical Images ◽  
Spatial Domain ◽  
Discrete Wavelet ◽  
System Efficiency ◽  
Simple Method ◽  
Incorrect Diagnosis ◽  
Small Change ◽  
The Military

Digital watermarking is one of the most efficient techniques to provide the highest secureness to the transmission of data like images or videos over the internet. Quite over the medical data which incorporates the EHR (Electronic Health Record) and medical images and conjointly the military data are crucial whose protection and privacy is extremely a lot of essential issues. To secure this data, the Digital watermarking plays a major role so that it will guarantee authentication, integrity, confidentiality and reliability. In the case of medical images, even a small change or modifications are strictly prohibited as it might lead to the incorrect diagnosis of the disease. Therefore, securing medical image is extremely essential. So as to provide high security for each patient’s data and also the various medical scanning images, we can employ the Digital Water Marking (DWM) technique. The DWM technique may be implemented in two ways: Spatial domain technique and Frequency domain technique. Although the spatial implementation is extremely straightforward and a very simple method, most of the implementations are done using frequency or transform/remodel domain strategies since it provides additional details and high effectiveness. The DWM may be implemented using numerous transform/remodel techniques like Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), also with the combination of these remodel techniques. Nowadays the work is also extended using a combination of transforming/remodel and spatial domain techniques. In this article the Digital Water Marking is being implemented by employing a combination of a transform technique DWT and a spatial domain technique SVD to provide security to the medical images and also the system efficiency is checked for numerous attacks.

scholarly journals TransMed: Transformers Advance Multi-Modal Medical Image Classification

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1384
Author(s):  
Yin Dai ◽  
Yifan Gao ◽  
Fayu Liu
Keyword(s):  
Image Analysis ◽  
Image Classification ◽  
Long Range ◽  
Medical Image ◽  
Large Scale ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Lesion Detection ◽  
Tumor Segmentation ◽  
Medical Image Classification

Over the past decade, convolutional neural networks (CNN) have shown very competitive performance in medical image analysis tasks, such as disease classification, tumor segmentation, and lesion detection. CNN has great advantages in extracting local features of images. However, due to the locality of convolution operation, it cannot deal with long-range relationships well. Recently, transformers have been applied to computer vision and achieved remarkable success in large-scale datasets. Compared with natural images, multi-modal medical images have explicit and important long-range dependencies, and effective multi-modal fusion strategies can greatly improve the performance of deep models. This prompts us to study transformer-based structures and apply them to multi-modal medical images. Existing transformer-based network architectures require large-scale datasets to achieve better performance. However, medical imaging datasets are relatively small, which makes it difficult to apply pure transformers to medical image analysis. Therefore, we propose TransMed for multi-modal medical image classification. TransMed combines the advantages of CNN and transformer to efficiently extract low-level features of images and establish long-range dependencies between modalities. We evaluated our model on two datasets, parotid gland tumors classification and knee injury classification. Combining our contributions, we achieve an improvement of 10.1% and 1.9% in average accuracy, respectively, outperforming other state-of-the-art CNN-based models. The results of the proposed method are promising and have tremendous potential to be applied to a large number of medical image analysis tasks. To our best knowledge, this is the first work to apply transformers to multi-modal medical image classification.

Cisco Secured Virtual Private Networks: A Review

2018 ◽  
Vol 7 (2) ◽  
pp. 30-33
Author(s):  
Yusera Farooq Khan
Keyword(s):  
Review Paper ◽  
Virtual Private Network ◽  
Virtual Private Networks ◽  
Public Network ◽  
The Public ◽  
Internal Network ◽  
Private Network ◽  
A Site ◽  
Destination Address

Now-a-days the significance of security has been greater than before because of the fact that data has been accessed and transferred through public network. The data which has been transferred could be sniffed which may be a loss for us. When data is transferred in to public network we need confidentiality, integration and authentication. In this review paper we will discuss all these factors that keep our data safe enough. In order to provide this factor a site-to-site virtual private network has been designed which provide more security to data and made the public network into private network. The virtual private network hides the source and destination address as well as it also hides the internal network so that our network would be safe enough.

DAUBECHIES COMPLEX WAVELET TRANSFORM BASED TECHNIQUE FOR DENOISING OF MEDICAL IMAGES

2007 ◽  
Vol 07 (04) ◽  
pp. 663-687 ◽  
Author(s):  
ASHISH KHARE ◽  
UMA SHANKER TIWARY
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
Medical Image ◽  
Medical Images ◽  
Universal Method ◽  
Complex Wavelet Transform ◽  
Shrinkage Function ◽  
Daubechies Complex Wavelet Transform ◽  
Complex Wavelet

Wavelet based denoising is an effective way to improve the quality of images. Various methods have been proposed for denoising using real-valued wavelet transform. Complex valued wavelets exist but are rarely used. The complex wavelet transform provides phase information and it is shift invariant in nature. In medical image denoising, both removal of phase incoherency as well as maintaining the phase coherency are needed. This paper is an attempt to explore and apply the complex Daubechies wavelet transform for medical image denoising. We have proposed a method to compute a complex threshold, which does not depend on any assumed model of noise. In this sense this is a "universal" method. The proposed complex-domain shrinkage function depends on mean, variance and median of wavelet coefficients. To test the effectiveness of the proposed method, we have computed the input and output SNR and PSNR of various types of medical images. The method gives an improvement for Gaussian additive, Speckle and Salt-&-Pepper noise as well as for the mixture of these noise types for a range of noisy images with 15 db to 30 db noise levels and outperforms other real-valued wavelet transform based methods. The application of the proposed method to Ultrasound, X-ray and MRI images is demonstrated in the experiments.

scholarly journals Local Derivative Vector Pattern: Hybrid Pattern for Content-Based Medical Image Retrieval

2020 ◽  
Vol 7 (4) ◽  
pp. 79-86
Author(s):  
Nagadevi Darapureddy ◽  
Nagaprakash Karatapu ◽  
Tirumala Krishna Battula
Keyword(s):  
Image Retrieval ◽  
Texture Analysis ◽  
Medical Image ◽  
Local Binary Pattern ◽  
Medical Images ◽  
Texture Classification ◽  
Medical Image Retrieval ◽  
Local Vector ◽  
Local Derivative ◽  
Local Derivative Pattern

This paper examines a hybrid pattern i.e. Local derivative Vector pattern and comparasion of this pattern over other different patterns for content-based medical image retrieval. In recent years Pattern-based texture analysis has significant popularity for a variety of tasks like image recognition, image and texture classification, and object detection, etc. In literature, different patterns exist for texture analysis. This paper aims at forming a hybrid pattern compared in terms of precision, recall and F1-score with different patterns like Local Binary Pattern (LBP), Local Derivative Pattern (LDP), Completed Local Binary Pattern (CLBP), Local Tetra Pattern (LTrP), Local Vector Pattern (LVP) and Local Anisotropic Pattern (LAP) which were applied on medical images for image retrieval. The proposed method is evaluated on different modalities of medical images. The results of the proposed hybrid pattern show biased performance compared to the state-of-the-art. So this can further extended with other pattern to form a hybrid pattern.

scholarly journals Medical Image Denoising Algorithm Based on Sparse Nonlocal Regularized Weighted Coding and Low Rank Constraint

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Quan Yuan ◽  
Zhenyun Peng ◽  
Zhencheng Chen ◽  
Yanke Guo ◽  
Bin Yang ◽  
...  
Keyword(s):  
Image Denoising ◽  
Gaussian Noise ◽  
Medical Image ◽  
Impulse Noise ◽  
Medical Images ◽  
Noise Removal ◽  
White Gaussian Noise ◽  
Low Rank ◽  
Rank Constraint ◽  
Nonlocal Regularization

Medical image information may be polluted by noise in the process of generation and transmission, which will seriously hinder the follow-up image processing and medical diagnosis. In medical images, there is a typical mixed noise composed of additive white Gaussian noise (AWGN) and impulse noise. In the conventional denoising methods, impulse noise is first removed, followed by the elimination of white Gaussian noise (WGN). However, it is difficult to separate the two kinds of noises completely in practical application. The existing denoising algorithm of weight coding based on sparse nonlocal regularization, which can simultaneously remove AWGN and impulse noise, is plagued by the problems of incomplete noise removal and serious loss of details. The denoising algorithm based on sparse representation and low rank constraint can preserve image details better. Thus, a medical image denoising algorithm based on sparse nonlocal regularization weighted coding and low rank constraint is proposed. The denoising effect of the proposed method and the original algorithm on computed tomography (CT) image and magnetic resonance (MR) image are compared. It is revealed that, under different σ and ρ values, the PSNR and FSIM values of CT and MRI images are evidently superior to those of traditional algorithms, suggesting that the algorithm proposed in this work has better denoising effects on medical images than traditional denoising algorithms.

Sign in / Sign up

Export Citation Format

Share Document