Secure and Efficient Medical Image Transmission by New Tailored Visual Cryptography Scheme with LS Compressions

2015 ◽  
Vol 7 (1) ◽  
pp. 26-50 ◽  
Author(s):  
S. Manimurugan ◽  
C. Narmatha
Keyword(s):  
Medical Image ◽  
Medical Images ◽  
Visual Cryptography ◽  
Lossless Compression ◽  
Image Transmission ◽  
Vital Role ◽  
Compression Algorithms ◽  
Merging Process ◽  
Visual Cryptography Scheme ◽  
Short Algorithm

Exchanging a medical image via network from one place to another place or storing a medical image in a particular place in a secure manner has become a challenge. To overwhelm this, secure medical image Lossless Compression (LC) schemes have been proposed. The original input grayscale medical images are encrypted by Tailored Visual Cryptography Encryption Process (TVCE) which is a proposed encryption system. To generate these encrypted images, four types of processes are adopted which play a vital role. These processes are Splitting Process, Converting Process, Pixel Process and Merging process. The encrypted medical image is compressed by proposed compression algorithms, i.e Pixel Block Short algorithm (PBSA) and one conventional Lossless Compression (LC) algorithm has been adopted (JPEG 2000LS). The above two compression methods are used to separate compression for encrypted medical images. And also, decompressions have been done in a separate manner. The encrypted output image which is generated from decompression of the proposed compression algorithm, JPEG 2000LS are decrypted by the Tailored Visual Cryptography Decryption Process (TVCD). To decrypt the encrypted grayscale medical images, four types of processes are involved. These processes are Segregation Process, Inverse Pixel Process, 8-Bit into Decimal Conversion Process and Amalgamate Process. However, this paper is focused on the proposed visual cryptography only. From these processes, two original images have been reconstructed which are given by two compression algorithms. Ultimately, two combinations are compared with each other based on the various parameters. These techniques can be implemented in the field for storing and transmitting medical images in a secure manner. The Confidentiality, Integrity and Availability (CIA property) of a medical image have also been proved by the experimental results. In this paper we have focused on only proposed visual cryptography scheme.

scholarly journals Lossless Compression of Medical Images based on the Differential Probability of Images

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Medical Image ◽  
Large Scale ◽  
Medical Images ◽  
Lossless Compression ◽  
Differential Method ◽  
Huffman Coding ◽  
Difference Matrix ◽  
Differential Probability ◽  
Compression Effect ◽  
The Difference

Lossless compression is crucial in the remote transmission of large-scale medical image and the retainment of complete medical diagnostic information. The lossless compression method of medical image based on differential probability of image is proposed in this study. The medical image with DICOM format was decorrelated by the differential method, and the difference matrix was optimally coded by the Huffman coding method to obtain the optimal compression effect. Experimental results obtained using the new method were compared with those using Lempel–Ziv–Welch, modified run–length encoding, and block–bit allocation methods to verify its effectiveness. For 2-D medical images, the lossless compression effect of the proposed method is the best when the object region is more than 20% of the image. For 3-D medical images, the proposed method has the highest compression ratio among the control methods. The proposed method can be directly used for lossless compression of DICOM images.

scholarly journals OFCS: Optimized Framework of Compressive Sensing for Medical Images in Bottleneck Network Condition

2018 ◽  
Vol 8 (5) ◽  
pp. 2829
Author(s):  
Lakshminarayana M ◽  
Mrinal Sarvagya
Keyword(s):  
Compressive Sensing ◽  
Medical Image ◽  
Medical Images ◽  
Region Of Interest ◽  
Lossless Compression ◽  
Cost Effective ◽  
Signal Quality ◽  
Effective Solution ◽  
The Cost

Compressive sensing is one of teh cost effective solution towards performing compression of heavier form of signals. We reviewed the existing research contribution towards compressive sensing to find that existing system doesnt offer any form of optimization for which reason the signal are superiorly compressed but at the cost of enough resources. Therefore, we introduce a framework that optimizes the performance of the compressive sensing by introducing 4 sequential algorithms for performing Random Sampling, Lossless Compression for region-of-interest, Compressive Sensing using transform-based scheme, and optimization. The contribution of proposed paper is a good balance between computational efficiency and quality of reconstructed medical image when transmitted over network with low channel capacity. The study outcome shows that proposed system offers maximum signal quality and lower algorithm processing time in contrast to existing compression techniuqes on medical images.

scholarly journals Integrated HNAS Network Model Based Lossless Compression with Data Hiding using Parity Check in Medical Images

2020 ◽  
Vol 9 (4) ◽  
pp. 2656-2662
Keyword(s):  
Network Model ◽  
Data Hiding ◽  
Compression Ratio ◽  
Reversible Data Hiding ◽  
Medical Image ◽  
Medical Images ◽  
Care Delivery ◽  
Lossless Compression ◽  
Parity Check ◽  
Compression Technique

A massive volume of medical data is generating through advanced medical image modalities. With advancements in telecommunications, Telemedicine, and Teleradiologyy have become the most common and viable methods for effective health care delivery around the globe. For sufficient storage, medical images should be compressed using lossless compression techniques. In this paper, we aim at developing a lossless compression technique to achieve a better compression ratio with reversible data hiding. The proposed work segments foreground and background area in medical images using semantic segmentation with the Hierarchical Neural Architecture Search (HNAS) Network model. After segmenting the medical image, confidential patient data is hidden in the foreground area using the parity check method. Following data hiding, lossless compression of foreground and background is done using Huffman and Lempel-Ziv-Welch methods. The performance of our proposed method has been compared with those obtained from standard lossless compression algorithms and existing reversible data hiding methods. This proposed method achieves better compression ratio and a hundred percent reversible when data extraction.

scholarly journals Brain Tumor Classification & Segmentation by Using Advanced DNN, CNN & ResNet-50 Neural Networks

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Brain Tumor ◽  
Image Classification ◽  
Medical Image ◽  
Data Augmentation ◽  
Medical Images ◽  
Medical Science ◽  
Vital Role ◽  
Tumor Classification

In the medical domain, brain image classification is an extremely challenging field. Medical images play a vital role in making the doctor's precise diagnosis and in the surgery process. Adopting intelligent algorithms makes it feasible to detect the lesions of medical images quickly, and it is especially necessary to extract features from medical images. Several studies have integrated multiple algorithms toward medical images domain. Concerning feature extraction from the medical image, a vast amount of data is analyzed to achieve processing results, helping physicians deliver more precise case diagnoses. Image processing mechanism becomes extensive usage in medical science to advance the early detection and treatment aspects. In this aspect, this paper takes tumor, and healthy images as the research object and primarily performs image processing and data augmentation process to feed the dataset to the neural networks. Deep neural networks (DNN), to date, have shown outstanding achievement in classification and segmentation tasks. Carrying this concept into consideration, in this study, we adopted a pre-trained model Resnet_50 for image analysis. The paper proposed three diverse neural networks, particularly DNN, CNN, and ResNet-50. Finally, the splitting dataset is individually assigned to each simplified neural network. Once the image is classified as a tumor accurately, the OTSU segmentation is employed to extract the tumor alone. It can be examined from the experimental outcomes that the ResNet-50 algorithm shows high accuracy 0.996, precision 1.00 with best F1 score 1.0, and minimum test losses of 0.0269 in terms of Brain tumor classification. Extensive experiments prove our offered tumor detection segmentation efficiency and accuracy. To this end, our approach is comprehensive sufficient and only requires minimum pre-and post-processing, which allows its adoption in various medical image classification & segmentation tasks.

scholarly journals Medical Image Classification Based On Normalized Coding Network with Multiscale Perception

2019 ◽  
Vol 8 (11) ◽  
pp. 2694-2697
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
Medical Image ◽  
Medical Images ◽  
Vital Role ◽  
Multi Scale ◽  
Proposed Model ◽  
Dataset Size ◽  
High Resolution Images ◽  
High Level

Medical imaging classification is playing a vital role in identifying and diagnoses the diseases, which is very helpful to doctor. Conventional ways classify supported the form, color, and/or texture, most of tiny problematic areas haven’t shown in medical images, which meant less efficient classification and that has poor ability to identify disease. Advanced deep learning algorithms provide an efficient way to construct a finished model that can compute final classification labels with the raw pixels of medical images. These conventional algorithms are not sufficient for high resolution images due to small dataset size, advanced deep learning models suffer from very high computational costs and limitations in the channels and multilayers in the channels. To overcome these limitations, we proposed a new algorithm Normalized Coding Network with Multi-scale Perceptron (NCNMP), which combines high-level features and traditional features. The Architecture of the proposed model includes three stages. Training, retrieve, fuse. We examined the proposed algorithm on medical image dataset NIH2626. We got an overall image classification accuracy of 91.35, which are greater than the present methods.

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