A Hybrid Fusion of Multimodal Medical Images for the Enhancement of Visual Quality in Medical Diagnosis

2019 ◽  
pp. 61-70 ◽  
Author(s):  
S. Sandhya ◽  
M. Senthil Kumar ◽  
L. Karthikeyan
Keyword(s):  
Medical Diagnosis ◽  
Medical Images ◽  
Visual Quality ◽  
Hybrid Fusion

scholarly journals Blockchain-Based Reversible Data Hiding for Securing Medical Images

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Ji-Hwei Horng ◽  
Ching-Chun Chang ◽  
Guan-Long Li ◽  
Wai-Kong Lee ◽  
Seong Oun Hwang
Keyword(s):  
Data Hiding ◽  
Private Information ◽  
Medical Diagnosis ◽  
Reversible Data Hiding ◽  
Secure Communication ◽  
Medical Images ◽  
Personal Information ◽  
Embedding Capacity ◽  
Image Block ◽  
Encrypted Images

Medical images carry a lot of important information for making a medical diagnosis. Since the medical images need to be communicated frequently to allow timely and accurate diagnosis, it has become a target for malicious attacks. Hence, medical images are protected through encryption algorithms. Recently, reversible data hiding on the encrypted images (RDHEI) schemes are employed to embed private information into the medical images. This allows effective and secure communication, wherein the privately embedded information (e.g., medical records and personal information) is very useful to the medical diagnosis. However, existing RDHEI schemes still suffer from low embedding capacity, which limits their applicability. Besides, such solution still lacks a good mechanism to ensure its integrity and traceability. To resolve these issues, a novel approach based on image block-wise encryption and histogram shifting is proposed to provide more embedding capacity in the encrypted images. The embedding rate is over 0.8 bpp for typical medical images. On top of that, a blockchain-based system for RDHEI is proposed to resolve the traceability. The private information is stored on the blockchain together with the hash value of the original medical image. This allows traceability of all the medical images communicated over the proposed blockchain network.

scholarly journals An Optimal Method to Improve the Visual Quality of Medical Images

2015 ◽  
Vol 8 (2) ◽  
pp. 141-144
Author(s):  
Choong-ho Shin ◽  
Chai-yeoung Jung
Keyword(s):  
Medical Images ◽  
Visual Quality ◽  
Optimal Method

scholarly journals Multi-Task Learning for Medical Image Inpainting Based on Organ Boundary Awareness

2021 ◽  
Vol 11 (9) ◽  
pp. 4247
Author(s):  
Minh-Trieu Tran ◽  
Soo-Hyung Kim ◽  
Hyung-Jeong Yang ◽  
Guee-Sang Lee
Keyword(s):  
Image Analysis ◽  
Medical Diagnosis ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Image Inpainting ◽  
Ct Images ◽  
Task Learning ◽  
Specular Reflections ◽  
Simultaneous Training

Distorted medical images can significantly hamper medical diagnosis, notably in the analysis of Computer Tomography (CT) images and organ segmentation specifics. Therefore, improving diagnostic imagery accuracy and reconstructing damaged portions are important for medical diagnosis. Recently, these issues have been studied extensively in the field of medical image inpainting. Inpainting techniques are emerging in medical image analysis since local deformations in medical modalities are common because of various factors such as metallic implants, foreign objects or specular reflections during the image captures. The completion of such missing or distorted regions is important for the enhancement of post-processing tasks such as segmentation or classification. In this paper, a novel framework for medical image inpainting is presented by using a multi-task learning model for CT images targeting the learning of the shape and structure of the organs of interest. This novelty has been accomplished through simultaneous training for the prediction of edges and organ boundaries with the image inpainting, while state-of-the-art methods still focus only on the inpainting area without considering the global structure of the target organ. Therefore, our model reproduces medical images with sharp contours and exact organ locations. Consequently, our technique generates more realistic and believable images compared to other approaches. Additionally, in quantitative evaluation, the proposed method achieved the best results in the literature so far, which include a PSNR value of 43.44 dB and SSIM of 0.9818 for the square-shaped regions; a PSNR value of 38.06 dB and SSIM of 0.9746 for the arbitrary-shaped regions. The proposed model generates the sharp and clear images for inpainting by learning the detailed structure of organs. Our method was able to show how promising the method is when applying it in medical image analysis, where the completion of missing or distorted regions is still a challenging task.

A Convolutional Neural Network-Based Framework for Medical Images Analyzing in Enhancing Medical Diagnosis

2020 ◽  
Vol 23 (13) ◽  
Author(s):  
Ayad Hameed Mousa ◽  
Zahraa Noor Aldeen ◽  
Ali Hussein Mohammed ◽  
Mohammed G. K. Abboosh
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Medical Diagnosis ◽  
Medical Images

scholarly journals Multipurpose Watermarking Algorithm for Medical Images

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Shaozhang Xiao ◽  
Zhengwei Zhang ◽  
Yue Zhang ◽  
Changhui Yu
Keyword(s):  
Medical Image ◽  
Medical Images ◽  
Image Watermarking ◽  
Visual Quality ◽  
Image Management ◽  
Content Authentication ◽  
Tamper Localization ◽  
Single Function ◽  
Multipurpose Watermarking ◽  
Visible Watermark

Considering the existing medical image watermarking algorithms, a single function often has limitations, and a multipurpose watermarking algorithm for medical images is proposed. First, medical images are divided into regions of interest (ROIs) and regions of noninterest (RONIs). Then, the authentication watermark produced for each subblock of the ROI is embedded into the corresponding mapping subblock. The visible watermark is embedded into the RONI, and, finally, the watermark information and constructed authentication information in each subblock of the ROI are embedded into the corresponding RONI subblock. Simulation results show that the embedded visible watermark can protect and facilitate medical image management. In addition, the proposed algorithm has strong robustness and very good visual quality. It can simultaneously realize copyright protection and content authentication and also has high tamper localization capability.

scholarly journals 3D Medical image compression using the quincunx wavelet coupled with SPIHT

2020 ◽  
Vol 18 (2) ◽  
pp. 821
Author(s):  
Benlabbes Haouari
Keyword(s):  
Wavelet Transform ◽  
Medical Imaging ◽  
Image Compression ◽  
Medical Image ◽  
Medical Images ◽  
Digital Technologies ◽  
Visual Quality ◽  
Medical Image Compression ◽  
Coding Scheme

<p>Medical imaging is a growing field due to the development of digital technologies that produce 3D and even 4D data. The counterpart to the resolution offered by these voluminal images resides in the amount of gigantic data, hence the need for compression. This article presents a new coding scheme dedicated to 3D medical images. The originality of our approach lies in the application of the Quinqunx wavelet transform coupled with the SPIHT encoder on a database of medical images. This approach achieves much higher compression rates, while maintaining a very acceptable visual quality.</p>

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