scholarly journals Region of Interest-based Tamper Detection and Lossless Recovery Watermarking Scheme on MRI and X-ray Medical Images

2020 ◽  
Vol 769 ◽  
pp. 012068
Author(s):  
Jessie Ooi ◽  
Hui-Liang Khor ◽  
Siau-Chuin Liew
Keyword(s):  
Medical Images ◽  
Region Of Interest ◽  
Tamper Detection ◽  
X Ray ◽  
Lossless Recovery ◽  
Watermarking Scheme

scholarly journals Medical Image Watermarking Technique for Accurate Tamper Detection in ROI and Exact Recovery of ROI

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
R. Eswaraiah ◽  
E. Sreenivasa Reddy
Keyword(s):  
Medical Image ◽  
Medical Images ◽  
Image Watermarking ◽  
Region Of Interest ◽  
Tamper Detection ◽  
High Quality ◽  
Medical Image Watermarking ◽  
Block Based ◽  
Embedding Distortion

In telemedicine while transferring medical images tampers may be introduced. Before making any diagnostic decisions, the integrity of region of interest (ROI) of the received medical image must be verified to avoid misdiagnosis. In this paper, we propose a novel fragile block based medical image watermarking technique to avoid embedding distortion inside ROI, verify integrity of ROI, detect accurately the tampered blocks inside ROI, and recover the original ROI with zero loss. In this proposed method, the medical image is segmented into three sets of pixels: ROI pixels, region of noninterest (RONI) pixels, and border pixels. Then, authentication data and information of ROI are embedded in border pixels. Recovery data of ROI is embedded into RONI. Results of experiments conducted on a number of medical images reveal that the proposed method produces high quality watermarked medical images, identifies the presence of tampers inside ROI with 100% accuracy, and recovers the original ROI without any loss.

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.

Efficient Region of Interest Extraction Methods for Multiple Medical Images

2019 ◽  
Vol 9 (1) ◽  
pp. 1554-1559
Keyword(s):  
Medical Images ◽  
Region Of Interest ◽  
Extraction Methods ◽  
Brain Images ◽  
X Ray ◽  
Different Types ◽  
Wrong Diagnosis ◽  
Mr Brain Images ◽  
Significant Region ◽  
Mr Brain

Medical images do contain important and unimportant spatial regions. Compression methods which are capable of reconstructing the image with high quality are required to compress the medical images. For these images, only a portion of it is useful for diagnosis hence a region based coding techniques are significant for compressing and transmission. Extracting a significant region is of great demand since a slighter mistake may leads to wrong diagnosis. This paper is focused on investigating multiple image processing algorithms for medical images. All the images may not contain the same region of interest, so different approaches are supposed to apply for different images. In this three types of medical images were considered like magnetic resonance (MR) brain images, computer tomography (CT) abdomen images and X-ray lung images. In this paper three automatic region of interest extraction algorithms were proposed for different types of images.

scholarly journals Protection of Health Imagery by Region Based Lossless Reversible Watermarking Scheme

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
R. Lakshmi Priya ◽  
V. Sadasivam
Keyword(s):  
Digital Signature ◽  
Medical Images ◽  
Region Of Interest ◽  
Reversible Watermarking ◽  
Least Significant Bit ◽  
Watermarking Scheme

Providing authentication and integrity in medical images is a problem and this work proposes a new blind fragile region based lossless reversible watermarking technique to improve trustworthiness of medical images. The proposed technique embeds the watermark using a reversible least significant bit embedding scheme. The scheme combines hashing, compression, and digital signature techniques to create a content dependent watermark making use of compressed region of interest (ROI) for recovery of ROI as reported in literature. The experiments were carried out to prove the performance of the scheme and its assessment reveals that ROI is extracted in an intact manner and PSNR values obtained lead to realization that the presented scheme offers greater protection for health imageries.

A FRT - SVD Based Blind Medical Watermarking Technique for Telemedicine Applications

2019 ◽  
Vol 11 (2) ◽  
pp. 13-33 ◽  
Author(s):  
Surekah Borra ◽  
Rohit Thanki
Keyword(s):  
Medical Image ◽  
Medical Images ◽  
Image Watermarking ◽  
Region Of Interest ◽  
Singular Value ◽  
Ridgelet Transform ◽  
X Ray ◽  
Simulation Results ◽  
The Individual ◽  
Value Decomposition

In this article, a blind and robust medical image watermarking technique based on Finite Ridgelet Transform (FRT) and Singular Value Decomposition (SVD) is proposed. A host medical image is first transformed into 16 × 16 non-overlapping blocks and then ridgelet transform is applied on the individual blocks to obtain sets of ridgelet coefficients. SVD is then applied on these sets, to obtain the corresponding U, S and V matrix. The watermark information is embedded into the host medical image by modification of the value of the significant elements of U matrix. This proposed technique is tested on various types of medical images such as X-ray and CT scan. The simulation results revealed that this technique provides better imperceptibility, with an average PSNR being 42.95 dB for all test medical images. This technique also overcomes the limitation of the existing technique which is applicable on only the Region of Interest (ROI) of the medical image.

OSTEOPOROSIS AND RISK FACTORS IN THE PATIENTS ON CHRONIC HEMODIALYSIS

2015 ◽  
pp. 50-58
Author(s):  
Thi Dung Nguyen ◽  
Tam Vo
Keyword(s):  
Quality Of Life ◽  
Risk Factors ◽  
Bone Density ◽  
Region Of Interest ◽  
Cross Sectional Study ◽  
Chronic Hemodialysis ◽  
Cross Sectional ◽  
X Ray ◽  
Density Reduction

Background: The patients on hemodialysis have a significantly decreased quality of life. One of many problems which reduce the quality of life and increase the mortality in these patients is osteoporosis and osteoporosis associated fractures. Objectives: To assess the bone density of those on hemodialysis by dual energy X ray absorptiometry and to examine the risk factors of bone density reduction in these patients. Patients and Method: This is a cross-sectional study, including 93 patients on chronic hemodialysis at the department of Hemodialysis at Cho Ray Hospital. Results: Mean bone densities at the region of interest (ROI) neck, trochanter, Ward triangle, intertrochanter and total neck are 0.603 ± 0.105; 0.583 ± 0.121; 0.811 ± 0.166; 0.489 ± 0.146; 0.723 ± 0.138 g/cm2 respectively. The prevalences of osteoporosis at those ROI are 39.8%, 15.1%; 28%; 38.7%; and 26.9% respectively. The prevalences of osteopenia at those ROI are 54.8%; 46.3%; 60.2%; 45.2% and 62.7% respectively. The prevalence of osteopososis in at least one ROI is 52.7% and the prevalence of osteopenia in at least one ROI is 47.3%. There are relations between the bone density at the neck and the gender of the patient and the albuminemia. Bone density at the trochanter is influenced by gender, albuminemia, calcemia and phosphoremia. Bone density at the intertrochanter is affected by the gender. Bone density at the Ward triangle is influenced by age and albuminemia. Total neck bone density is influenced by gender, albuminemia and phosphoremia. Conclusion: Osteoporosis in patients on chronic hemodialysis is an issue that requires our attention. There are many interventionable risk factors of bone density decrease in these patients. Key words: Osteoporosis, DEXA, chronic renal failure, chronic hemodialysis

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