Analysis of Lossy and Lossless Compression Algorithms for Computed Tomography Medical Images Based on Bat and Simulated Annealing Optimization Techniques

2021 ◽  
pp. 99-133
Author(s):  
S. N. Kumar ◽  
Ajay Kumar Haridhas ◽  
A. Lenin Fred ◽  
P. Sebastin Varghese
Keyword(s):  
Computed Tomography ◽  
Simulated Annealing ◽  
Medical Images ◽  
Lossless Compression ◽  
Optimization Techniques ◽  
Compression Algorithms

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.

Optimization Techniques for Removing Noise in Digital Medical Images

2021 ◽  
pp. 243-266
Author(s):  
D. Devasena ◽  
M. Jagadeeswari ◽  
B. Sharmila ◽  
K. Srinivasan
Keyword(s):  
Medical Images ◽  
Optimization Techniques

scholarly journals The Image-Based Finite Element Evaluation of the Deformed State

2021 ◽  
pp. 44-54
Author(s):  
O. V Vorobiev ◽  
E. V Semenova ◽  
D. A Mukhin ◽  
E. O Statsenko ◽  
T. V Baltina ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Finite Element ◽  
Numerical Model ◽  
Model Problem ◽  
Medical Images ◽  
Test Problems ◽  
Elementary Geometry ◽  
Material Inhomogeneity ◽  
Image Pixels ◽  
Nodal Displacements

The article presents one of the possible approaches to modeling objects with anisotropic properties based on images of the study area. Data from such images are taken into account when building a numerical model. In this case, material inhomogeneity can be included by integrating the local stiffness matrix of each finite element with a certain weight function. The purpose of the presented work is to develop a finite element for the formation of a computational ensemble and simulation of mechanical behavior taking into account the data of two-dimensional medical images. To implement the proposed approach, we used the assumption that there is a correlation between the values in the image pixels and the elastic properties of the material. Meshing was based on a four-node plane finite element. This approach allows using the quantitative phase or scanning electronic images, as well as computed tomography data. A number of test problems for compression of elementary geometry samples were calculated. The distal part of the rat femur was considered as a model problem. A computed tomography scan of the sample was used to construct a numerical model taking into account the inhomogeneity of the material distribution inside the organ. The distribution field of the nodal displacements based on data obtained from the images of the study area is presented. Within the framework of a model problem, we considered how a computer tomograph resolution influences the quality of the obtained results. For this purpose, calculations were carried out based on compressed input medical images.

Full-waveform inversion of salt models using shape optimization and simulated annealing

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. R793-R804 ◽  
Author(s):  
Debanjan Datta ◽  
Mrinal K. Sen ◽  
Faqi Liu ◽  
Scott Morton
Keyword(s):  
Simulated Annealing ◽  
Least Squares ◽  
Level Set ◽  
Waveform Inversion ◽  
Optimization Techniques ◽  
Model Parameters ◽  
Full Waveform Inversion ◽  
Velocity Models ◽  
Full Waveform ◽  
Starting Model

A good starting model is imperative in full-waveform inversion (FWI) because it solves a least-squares inversion problem using a local gradient-based optimization method. A suboptimal starting model can result in cycle skipping leading to poor convergence and incorrect estimation of subsurface properties. This problem is especially crucial for salt models because the strong velocity contrasts create substantial time shifts in the modeled seismogram. Incorrect estimation of salt bodies leads to velocity inaccuracies in the sediments because the least-squares gradient aims to reduce traveltime differences without considering the sharp velocity jump between sediments and salt. We have developed a technique to estimate velocity models containing salt bodies using a combination of global and local optimization techniques. To stabilize the global optimization algorithm and keep it computationally tractable, we reduce the number of model parameters by using sparse parameterization formulations. The sparse formulation represents sediments using a set of interfaces and velocities across them, whereas a set of ellipses represents the salt body. We use very fast simulated annealing (VFSA) to minimize the misfit between the observed and synthetic data and estimate an optimal model in the sparsely parameterized space. The VFSA inverted model is then used as a starting model in FWI in which the sediments and salt body are updated in the least-squares sense. We partition model updates into sediment and salt updates in which the sediments are updated like conventional FWI, whereas the shape of the salt is updated by taking the zero crossing of an evolving level set surface. Our algorithm is tested on two 2D synthetic salt models, namely, the Sigsbee 2A model and a modified SEG Advanced Modeling Program (SEAM) Phase I model while fixing the top of the salt. We determine the efficiency of the VFSA inversion and imaging improvements from the level set FWI approach and evaluate a few sources of uncertainty in the estimation of salt shapes.

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