Adaptive Prediction Methods for Medical Image/Video compression for Telemedicine Application

2021 ◽  
pp. 319-344
Author(s):  
Ketki C. Pathak ◽  
Jignesh N. Sarvaiya ◽  
Anand D. Darji
Keyword(s):  
Video Compression ◽  
Medical Images ◽  
Rapid Development ◽  
Predictive Coding ◽  
Available Bandwidth ◽  
High Data ◽  
Telemedicine Application ◽  
Aerial Imaging ◽  
Magnetic Resonance Imaging Mri ◽  
Spatial Redundancy

Due to rapid development of multimedia communication and advancement of image acquisition process, there is a crucial requirement of high storage and compression techniques to mitigate high data rate with limited bandwidth scenario for telemedicine application. Lossless compression is one of the challenging tasks in applications like medical, space, and aerial imaging field. Apart from achieving high compression ratio, in these mentioned applications there is a need to maintain the original imaging quality along with fast and adequate processing. Predictive coding was introduced to remove spatial redundancy. The accuracy of predictive coding is based on the choice of effective and adaptive predictor which is responsible for removing spatial redundancy. Medical images like computed tomography (CT) and magnetic resonance imaging (MRI) consume huge storage and utilize maximum available bandwidth. To overcome these inherent challenges, the authors have reviewed various adaptive predictors and it has been compared with existing JPEG and JPEG LS-based linear prediction technique for medical images.

Adaptive Prediction Methods for Medical Image/Video compression for Telemedicine Application

2019 ◽  
pp. 244-275 ◽  
Author(s):  
Ketki C. Pathak ◽  
Jignesh N. Sarvaiya ◽  
Anand D. Darji
Keyword(s):  
Video Compression ◽  
Medical Images ◽  
Rapid Development ◽  
Predictive Coding ◽  
Available Bandwidth ◽  
High Data ◽  
Telemedicine Application ◽  
Aerial Imaging ◽  
Magnetic Resonance Imaging Mri ◽  
Spatial Redundancy

Due to rapid development of multimedia communication and advancement of image acquisition process, there is a crucial requirement of high storage and compression techniques to mitigate high data rate with limited bandwidth scenario for telemedicine application. Lossless compression is one of the challenging tasks in applications like medical, space, and aerial imaging field. Apart from achieving high compression ratio, in these mentioned applications there is a need to maintain the original imaging quality along with fast and adequate processing. Predictive coding was introduced to remove spatial redundancy. The accuracy of predictive coding is based on the choice of effective and adaptive predictor which is responsible for removing spatial redundancy. Medical images like computed tomography (CT) and magnetic resonance imaging (MRI) consume huge storage and utilize maximum available bandwidth. To overcome these inherent challenges, the authors have reviewed various adaptive predictors and it has been compared with existing JPEG and JPEG LS-based linear prediction technique for medical images.

A Novel Secure and Robust encryption scheme for medical images

2020 ◽  
Vol 16 ◽  
Author(s):  
Siyamol Chirakkarottu ◽  
Sheena Mathew
Keyword(s):  
Medical Images ◽  
Technological Development ◽  
Imaging Techniques ◽  
Chaotic Map ◽  
Two Phase ◽  
Positron Emission ◽  
Medical Diagnostic ◽  
Wireless Medium ◽  
Health Care Applications ◽  
Magnetic Resonance Imaging Mri

Background: Medical imaging encloses different imaging techniques and processes to image the human body for medical diagnostic and treatment purposes. Hence it plays an important role to improve public health. The technological development in biomedical imaging specifically in X-ray, Computed Tomography (CT), nuclear ultrasound including Positron Emission Tomography (PET), optical and Magnetic Resonance Imaging (MRI) can provide valuable information unique to a person. Objective: In health care applications, the images are needed to be exchanged mostly over wireless medium. The diagnostic images with confidential information of a patient need to be protected from unauthorized access during transmission. In this paper, a novel encryption method is proposed to improve the security and integrity of medical images. Methods: Chaotic map along with DNA cryptography is used for encryption. The proposed method describes a two phase encryption of medical images. Results: Performance of the proposed method is also tested by various analysis metrics. Robustness of the method against different noises and attacks is analyzed. Conclusion: The results show that the method is efficient and well suitable to medical images.

Using Tomographic Medical Images as Objects of Portrait Examination

2019 ◽  
pp. 142-145
Author(s):  
G. V. Cherepenko
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Medical Images ◽  
Resonance Imaging ◽  
Examination Technique ◽  
Magnetic Resonance Imaging Mri ◽  
Practical Recommendations ◽  
Mri Image ◽  
Expert Practice

The paper provides an example from expert practice, during which a head image obtained using magnetic resonance imaging (MRI) was used as a sample. It is proposed to include an MRI image in a number of objects and samples considered by the current portrait examination technique. The nature of the suitability of such an object for the production of portrait examination is determined. Practical recommendations are given for working with the appropriate software to get the most visual picture.

scholarly journals MIE-NSCT: Adaptive MRI Enhancement Based on Nonsubsampled Contourlet Transform

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Caiwei Liu ◽  
Guohua Zhao ◽  
Jiale Dong ◽  
Yusong Lin ◽  
Meiyun Wang
Keyword(s):  
Medical Images ◽  
Disease Diagnosis ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
The Public ◽  
Low Pass ◽  
Magnetic Resonance Imaging Mri ◽  
Noise Amplification ◽  
Enhancement Algorithm

Image enhancement technology is often used to improve the quality of medical images and helps doctors or expert systems identify and diagnose diseases. This paper aimed at the characteristics of magnetic resonance imaging (MRI) with complex and difficult-to-enhance details and to propose a nonsubsampled contourlet transform- (NSCT-) based enhancement algorithm called MIE-NSCT. NSCT was used for MRI sub-band decomposition. For high-pass sub-bands, four fuzzy rules were proposed to enhance multiscale and multidirectional edge contour details from adjacent eight directions, whilst for low-pass sub-bands, a new adaptive histogram enhancement algorithm was proposed. The problem of noise amplification and loss of details during the enhancement process was solved. The algorithm was verified on the public dataset BraTS2017 and compared with other advanced methods. Experimental results showed that MIE-NSCT had obvious advantages in improving the quality of medical images, and high-quality medical images showed enhanced performance in grading tumour. MIE-NSCT is suitable for integration into an interactive expert system to provide support for the visualization of disease diagnosis.

scholarly journals Application of the transfer learning to the medical images texture classification task

2020 ◽  
Vol 224 ◽  
pp. 01020
Author(s):  
M Privalov ◽  
M Stupina
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Medical Images ◽  
Texture Classification ◽  
Classification Task ◽  
Radiotherapy Planning ◽  
Digital Radiograph ◽  
Magnetic Resonance Imaging Mri ◽  
Images Processing ◽  
Brachy Therapy

This study is conducted to determine effectiveness and perspectives of application of the transfer learning approach to the medical images classification task. There are a lot of medical studies that involve image acquisition, such as XRay radiography, ultrasonic scanning, computer tomography (CT), magnetic resonance imaging (MRI) etc. Besides those medical procedures there are different operations that use medical images processing including but not limited to digital radiograph reconstruction (DRR), radiotherapy planning, brachy therapy planning. All those tasks could be effectively performed with help of software capable to perform segmentation, classification and object recognition. Those capabilities are naturally depend on neural classifiers. Presented work investigates different approaches to solving image classification task with neural networks, specifically, using pre-processing for feature extraction and end-to-end application of convolutional neural networks (CNN). Due to requirement of significantly big datasets and large computing power CNNs sometimes may appear difficult to train, so our results pay attention to application of transfer learning technique that can potentially relax requirements to classifier training. The conclusions of this study state that transfer learning can be effectively used for classification tasks, especially texture classification.

scholarly journals IBK – A NEW TOOL FOR MEDICAL IMAGE PROCESSING

2010 ◽  
Vol 13 (4) ◽  
pp. 20-27
Author(s):  
Linh Duy Tran ◽  
Linh Quang Huynh
Keyword(s):  
Image Processing ◽  
Diagnostic Imaging ◽  
3D Reconstruction ◽  
Programming Language ◽  
Medical Image ◽  
Medical Images ◽  
Rapid Development ◽  
Medical Image Processing ◽  
Matlab Programming

Along with the rapid development of diagnostic imaging equipment, software for medical image processing has played an important role in helping doctors and clinicians to reach accurate diagnoses. In this paper, methods to build a multipurpose tool based on Matlab programming language and its applications are presented. This new tool features enhancement, segmentation, registration and 3D reconstruction for medical images obtained from commonly used diagnostic imaging equipment.

scholarly journals Two-Bit Embedding Histogram-Prediction-Error Based Reversible Data Hiding for Medical Images with Smooth Area

Computers ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 152
Author(s):  
Ching-Yu Yang ◽  
Ja-Ling Wu
Keyword(s):  
Data Hiding ◽  
Prediction Error ◽  
Reversible Data Hiding ◽  
Medical Images ◽  
High Capacity ◽  
Personal Privacy ◽  
Hiding Capacity ◽  
X Ray ◽  
Magnetic Resonance Imaging Mri ◽  
Hidden Data

During medical treatment, personal privacy is involved and must be protected. Healthcare institutions have to keep medical images or health information secret unless they have permission from the data owner to disclose them. Reversible data hiding (RDH) is a technique that embeds metadata into an image and can be recovered without any distortion after the hidden data have been extracted. This work aims to develop a fully reversible two-bit embedding RDH algorithm with a large hiding capacity for medical images. Medical images can be partitioned into regions of interest (ROI) and regions of noninterest (RONI). ROI is informative with semantic meanings essential for clinical applications and diagnosis and cannot tolerate subtle changes. Therefore, we utilize histogram shifting and prediction error to embed metadata into RONI. In addition, our embedding algorithm minimizes the side effect to ROI as much as possible. To verify the effectiveness of the proposed approach, we benchmarked three types of medical images in DICOM format, namely X-ray photography (X-ray), computer tomography (CT), and magnetic resonance imaging (MRI). Experimental results show that most of the hidden data have been embedded in RONI, and the performance achieves high capacity and leaves less visible distortion to ROIs.

scholarly journals Video Smoothing of Aggregates of Streams with Bandwidth Constraints

2017 ◽  
Vol 1 (1) ◽  
pp. 57
Author(s):  
Pietro Camarda ◽  
Cataldo Guaragnella ◽  
Domenico Striccoli
Keyword(s):  
Video Transmission ◽  
Video Data ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Smoothing Algorithm ◽  
Compressed Video ◽  
Available Bandwidth ◽  
Video Broadcasting ◽  
High Data ◽  
Video Frames

Compressed variable bit rate (VBR) video transmission is acquiring a growing importance in the telecommunication world. High data rate variability of compressed video over multiple time scales makes an efficient bandwidth resource utilization difficult to obtain. One of the approaches developed to face this problem are smoothing techniques. Various smoothing algorithms that exploit client buffers have been proposed, thus reducing the peak rate and highrate variability by efficiently scheduling the video data to be transmitted over the network. The novel smoothing algorithm proposed in this paper, which represents a significant improvements over the existing methods,performs data scheduling both for a single stream and for stream aggregations, by taking into account available bandwidth constraints. It modifies, whenever possible, the smoothing schedule in such a way as to eliminate frame losses due to available bandwidth limitations. This technique can be applied to any smoothing algorithm already present in literature and can be usefully exploited to minimize losses in multiplexed stream scenarios, like Terrestrial Digital Video Broadcasting (DVB-T), where a specific known available bandwidth must be shared byseveral multimedia flows. The developed algorithm has been exploited for smoothing stored video, although it can also be quite easily adapted for real time smoothing. The obtained numerical results, compared with the MVBA, another smoothing algorithm that is already presented and discussed in literature, show the effectiveness of the proposed algorithm, in terms of lost video frames, for different multiplexed scenarios.

scholarly journals Tamper and Clone-Resistant Authentication Scheme for Medical Image Systems

Cryptography ◽  
2020 ◽  
Vol 4 (3) ◽  
pp. 19
Author(s):  
Mayssa Tayachi ◽  
Saleh Mulhem ◽  
Wael Adi ◽  
Laurent Nana ◽  
Anca Pascu ◽  
...  
Keyword(s):  
Information And Communication Technologies ◽  
Digital Watermarking ◽  
Medical Information ◽  
Medical Images ◽  
Rapid Development ◽  
Communication Technologies ◽  
Medical Environment ◽  
Secret Keys ◽  
Information And Communication ◽  
High Level

Telemedicine applications are more and more used due to the rapid development of digital imaging and information and communication technologies. Medical information which include digital medical images and patient’s information are extracted and transmitted over insecure networks for clinical diagnosis and treatments. Digital watermarking is one of the main approaches used to ensure the security of medical images. Nevertheless, in some cases, the only use of digital watermarking is not sufficient to reach a high level of security. Indeed, the watermark could carry essential patient information and needs to be protected. In such cases, cryptography may be used to protect the watermark and to improve the overall secured management in the medical environment. In this paper, we propose a clone-resistant watermarking approach combining a difference expansion watermarking technique with a cryptographic technique based on secret keys generated by a clone-resistant device called Secret Unknown Ciphers (SUCs). The use of SUCs to sign the watermark enforces the security of medical images during their transfer and storage. Experimental results show that the system provides a high level of security against various forms of attacks.

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