Multiple Watermarks Algorithm for Medical Image Based on Arnold Scrambling and DFT

2013 ◽  
Vol 380-384 ◽  
pp. 4116-4119
Author(s):  
Miao Sui ◽  
Jing Bing Li ◽  
Yu Cong Duan
Keyword(s):  
Medical Image ◽  
Feature Vector ◽  
Region Of Interest ◽  
Third Party ◽  
Geometric Attacks ◽  
Arnold Scrambling ◽  
Network Transmission ◽  
Multiple Watermarks ◽  
Medical Accident ◽  
Accident Case

In the process of network transmission, when an exception occurs (such as forgery, tampering, information confusion), digital medical image, as a diagnostic basis, can not serve as the evidence of medical accident case. And the ROI of medical image is unable to tolerate significant changes. In order to deal these problems, we have proposed a multiple watermarks algorithm that uses Arnold scrambling to preprocess the original multiple watermarks, improving the security of watermarking, and combining the visual feature vector of image with the encryption technology and the concept of third-party. Moreover, the sophisticated process is needless to find the Region of Interest (ROI) of medical images. So compared with the existing medical watermarking techniques, it can embed much more data, with less complexity. The experimental results show that the scheme has strong robustness against common attacks and geometric attacks.

scholarly journals A Robust Multi-Watermarking Algorithm for Medical Images Based on DTCWT-DCT and Henon Map

2019 ◽  
Vol 9 (4) ◽  
pp. 700 ◽  
Author(s):  
Jing Liu ◽  
Jingbing Li ◽  
Jixin Ma ◽  
Naveed Sadiq ◽  
Uzair Bhatti ◽  
...  
Keyword(s):  
Medical Image ◽  
Feature Vector ◽  
Medical Images ◽  
Complex Wavelet Transform ◽  
Hénon Map ◽  
Henon Map ◽  
Geometric Attacks ◽  
Watermark Embedding ◽  
Zero Watermarking ◽  
Blind Extraction

To resolve the contradiction between existing watermarking methods—which are not compatible with the watermark’s ability to resist geometric attacks—and robustness, a robust multi-watermarking algorithm suitable for medical images is proposed. First, the visual feature vector of the medical image was obtained by dual-tree complex wavelet transform and discrete cosine transform (DTCWT-DCT) to perform multi-watermark embedding and extraction. Then, the multi-watermark was preprocessed using the henon map chaotic encryption technology to strengthen the security of watermark information, and combined with the concept of zero watermark to make the watermark able to resist both conventional and geometric attacks. Experimental results show that the proposed algorithm can effectively extract watermark information; it implements zero watermarking and blind extraction. Compared with existing watermark technology, it has good performance in terms of its robustness and resistance to geometric attacks and conventional attacks, especially in geometric attacks.

scholarly journals Steganography Technique Based on WPT and ElGamal Encryption with Confusion for Robust Medical Image

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2239-2248
Keyword(s):  
Medical Image ◽  
Medical Images ◽  
Wavelet Packet ◽  
Region Of Interest ◽  
Image Data ◽  
Third Party ◽  
Medicine Service ◽  
Health Related ◽  
Elgamal Encryption ◽  
Embedding Rate

For delivering effective health medical images and Electronic Patient Record (EPR) play an important role and these are stored in cloud, remote medical care and tele medicine service. For health care system, all the medical image data are stored in third party a server that is cloud. So, there is more chance to process or change the medical images as well as patient’s records which leads to health-related issues. To prevent the medical details from the hackers, many techniques are proposed and analyzed by the researchers. Anyway, data corruption is done by the attackers till now. In order to improve the security for data, this paper proposes a steganography technique which embed the important details into the medical image by using Wavelet Packet Transform (WPT) without affecting Region of Interest (ROI) which is useful for further diagnosis. Before embedding the patient’s record, these data are encrypted by using ElGamal Encryption technique which provides more security to the data. It is observed from the simulation results that the proposed technique produces better performance in terms of MSE, PSNR and WPSNR values. The PSNR value of the proposed system can increase 8.8%, 6.2%, 12.5%, 9.6%, 6.7% and 6.9% for embedding rate 5%, 10%, 20%, 25%, 30% and 40% respectively from the existing (DWT-ElGamal) technique.

scholarly journals An Image Encryption Technique Based on Logistic Sine Map and an Encrypted Image Retrieval Using DCT Frequency

2021 ◽  
Author(s):  
Rajana Kanakaraju ◽  
Lakshmi V ◽  
Shanmuk Srinivas Amiripalli ◽  
Sai Prasad Potharaju ◽  
R Chandrasekhar
Keyword(s):  
Feature Vector ◽  
Cloud Service ◽  
Third Party ◽  
Discrete Cosine Transformation ◽  
Geometric Attacks ◽  
Encrypted Image ◽  
Digital Era ◽  
Similar Images ◽  
Sine Map ◽  
Encrypted Images

In this digital era, most of the hospitals and medical labs are storing and sharing their medical data using third party cloud platforms for saving maintenance cost and storage and also to access data from anywhere. The cloud platform is not entirely a trusted party as the data is under the control of cloud service providers, which results in privacy leaks so that the data is to be encrypted while uploading into the cloud. The data can be used for diagnosis and analysis, for that the similar images to be retrieved as per the need of the doctor. In this paper, we propose an algorithm that uses discrete cosine transformation frequency and logistic sine map to encrypt an image, and the feature vector is computed on the encrypted image. The encrypted images are transferred to the cloud picture database, and feature vectors are uploaded to the feature database. Pearson’s Correlation Coefficient is calculated on the feature vector and is used as a measure to retrieve similar images. From the investigation outcomes, we can get an inference that this algorithm can resist against predictable attacks and geometric attacks with strong robustness.

scholarly journals Automatic Hip Detection in Anteroposterior Pelvic Radiographs—A Labelless Practical Framework

2021 ◽  
Vol 11 (6) ◽  
pp. 522
Author(s):  
Feng-Yu Liu ◽  
Chih-Chi Chen ◽  
Chi-Tung Cheng ◽  
Cheng-Ta Wu ◽  
Chih-Po Hsu ◽  
...  
Keyword(s):  
Medical Image ◽  
Image Annotation ◽  
Region Of Interest ◽  
Confidence Score ◽  
Heterogeneous Data ◽  
Single Shot ◽  
Hip Joints ◽  
Pelvic Radiographs ◽  
Wide Range ◽  
Average Confidence

Automated detection of the region of interest (ROI) is a critical step in the two-step classification system in several medical image applications. However, key information such as model parameter selection, image annotation rules, and ROI confidence score are essential but usually not reported. In this study, we proposed a practical framework of ROI detection by analyzing hip joints seen on 7399 anteroposterior pelvic radiographs (PXR) from three diverse sources. We presented a deep learning-based ROI detection framework utilizing a single-shot multi-box detector with a customized head structure based on the characteristics of the obtained datasets. Our method achieved average intersection over union (IoU) = 0.8115, average confidence = 0.9812, and average precision with threshold IoU = 0.5 (AP50) = 0.9901 in the independent testing set, suggesting that the detected hip regions appropriately covered the main features of the hip joints. The proposed approach featured flexible loose-fitting labeling, customized model design, and heterogeneous data testing. We demonstrated the feasibility of training a robust hip region detector for PXRs. This practical framework has a promising potential for a wide range of medical image applications.

Pedestrian Detection Based on Histograms of Oriented Gradients in ROI

2012 ◽  
Vol 542-543 ◽  
pp. 937-940
Author(s):  
Ping Shu Ge ◽  
Guo Kai Xu ◽  
Xiu Chun Zhao ◽  
Peng Song ◽  
Lie Guo
Keyword(s):  
Traditional Method ◽  
Detection Method ◽  
Feature Vector ◽  
Pedestrian Detection ◽  
Region Of Interest ◽  
Experimental Results ◽  
Process Time ◽  
Vertical Edge ◽  
Histograms Of Oriented Gradients

To locate pedestrian faster and more accurately, a pedestrian detection method based on histograms of oriented gradients (HOG) in region of interest (ROI) is introduced. The features are extracted in the ROI where the pedestrian's legs may exist, which is helpful to decrease the dimension of feature vector and simplify the calculation. Then the vertical edge symmetry of pedestrian's legs is fused to confirm the detection. Experimental results indicate that this method can achieve an ideal accuracy with lower process time compared to traditional method.

Secured Medical Image Hashing Based on Frequency Domain with Chaotic Map

2021 ◽  
Vol 39 (5A) ◽  
pp. 711-722
Author(s):  
Amira K. Jabbar ◽  
Ashwaq T. Hashim ◽  
Qusay F. Al-Doori
Keyword(s):  
Medical Image ◽  
Personal Information ◽  
Chaotic Map ◽  
Region Of Interest ◽  
Image Hashing ◽  
Medical Field ◽  
Hash Algorithm ◽  
Frequency Domains ◽  
Hash Code ◽  
Shift Keying

Recently, online-medicine got increased global interest, particularly during COVID19 pandemic. Data protection is important in the medical field since when promoting telemedicine applications, it is necessary to protect the patient data and personal information. A secured process is needed to transmit medical images over the Internet. In this paper hash algorithm is employed to protect the data by using powerful features from the coupled frequency domains of the Slantlet Transformation (SLT) and the Discrete Cosine Transform (DCT). The Region of Interest (ROI) is localized from an MRI image then extraction of a feature set is performed for calculating the hash code. Then, hash code is enciphered to maintain security by employing a secure Chaotic Shift Keying (CSK). The suggested method of security is ensured by the strength of the CSK and the encryption key secrecy.  A detailed analysis was conducted using 1000 uncompressed images that were chosen randomly from a publicly available AANLIB database. The proposed methodology can be useful for JPEG compression. Also, this method could resist many attacks of image processing likes filtering, noise addition, and some geometric transforms.

A NEW METHOD OF MEDICAL IMAGE RETRIEVAL BASED ON COLOR–TEXTURE CORRELOGRAM AND GTI MODEL

2009 ◽  
Vol 08 (02) ◽  
pp. 239-248 ◽  
Author(s):  
XIAO-YING TAI ◽  
LI-DONG WANG ◽  
QIN CHEN ◽  
REN FUJI ◽  
KITA KENJI
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Feature Vector ◽  
Texture Feature ◽  
Image Feature ◽  
Similarity Metric ◽  
Medical Image Retrieval ◽  
Endoscopic Image ◽  
Color Correlogram ◽  
Color Texture

This paper presents a method for endoscopic image retrieval based on color–texture correlogram and Generalized Tversky's Index (GTI) model. First we define a new image feature named color–texture correlogram, which is the extension of color correlogram. The texture image extracted by texture spectrum algorithm is combined with color feature vector, and then we calculate the spatial correlation of color–texture feature vector. Similarity metric is also the key technology during domain of image retrieval, GTI model is used in medical image retrieval for similarity metric, and the technique of relevance feedback is used in the algorithm to enhance the efficiency of retrieval. Experimental results show that the method discussed in this paper is much more effective.

Region of Interest-Based Coding Technique of Medical Images Using Varying Grading of Compression

2020 ◽  
pp. 2150021
Author(s):  
Urvashi Sharma ◽  
Meenakshi Sood ◽  
Emjee Puthooran
Keyword(s):  
Medical Image ◽  
Region Of Interest ◽  
Block Size ◽  
Threshold Value ◽  
Hospital Performance ◽  
Jpeg 2000 ◽  
Compression Method ◽  
Compression Efficiency ◽  
Important Region

A region of interest (ROI)-based compression method for medical image datasets is a requirement to maintain the quality of the diagnostically important region of the image. It is always a better option to compress the diagnostic important region in a lossless manner and the remaining portion of the image with a near-lossless compression method to achieve high compression efficiency without any compromise of quality. The predictive ROI-based compression on volumetric CT medical image is proposed in this paper; resolution-independent gradient edge detection (RIGED) and block adaptive arithmetic encoding (BAAE) are employed to ROI part for prediction and encoding that reduce the interpixel and coding redundancy. For the non-ROI portion, RIGED with an optimal threshold value, quantizer with optimal [Formula: see text]-level and BAAE with optimal block size are utilized for compression. The volumetric 8-bit and 16-bit standard CT image dataset is utilized for the evaluation of the proposed technique, and results are validated on real-time CT images collected from the hospital. Performance of the proposed technique in terms of BPP outperforms existing techniques such as JPEG 2000, M-CALIC, JPEG-LS, CALIC and JP3D by 20.31%, 19.87%, 17.77%, 15.58% and 13.66%, respectively.

A New Method for Osteosarcoma Recognition Based on Bayesian Classifier

2014 ◽  
Vol 543-547 ◽  
pp. 2901-2904
Author(s):  
Wen Bo Huang ◽  
Yun Ji Wang
Keyword(s):  
Bayesian Network ◽  
Medical Image ◽  
Feature Vector ◽  
Probability Model ◽  
Image Features ◽  
New Method ◽  
Training Samples ◽  
Recognition Probability ◽  
Medical Image Diagnosis ◽  
Image Diagnosis

In order to deal with the complexity and uncertainty in medical image diagnosis of osteosarcoma, we proposed a new method based on Bayesian network, and first applied it to recognize osteosarcoma. A new multidimensional feature vector composed of both biochemical indicator and the quantized image features is defined and used as input to the Bayesian network, so as to establish a more accurate and reliable osteosarcoma recognition probability model. Experimental results demonstrate the effective of our method, there are 50 training samples and 30 testing samples, and the accuracy is up to 86.67%, which close to the expert diagnosis.

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