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Enhancement Sushisen algorithms in Images analysis Technologies to increase computerized tomography images

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Abstract

Contrary to one of the major problems in computer tomography image analysis, Image enhancement can be used to improve the clarity and quality of the picture or to provide better conversion presentation for further processing. Contrast growth of one of the acceptable methods for image enhancement in various applications in the medical field is increased. It will help to show and remove brain myocardial infarction, cancer and cancer related details from CT images. In contrast to CT images, a comparison learning of five contrasting techniques has been presented in this paper. Types of technology include electric law conversion, logarithmic change, histogram equations, contrast pulling and lap less changes, all of these techniques are compared to each other, so that it can be achieved that a better CT image is in contrast. To compare technical parameters, peak signals are used for noise ratio (PSNR) and mean class error. Logarithmic result provides an image with clear and Sushisen algorithms better quality than all other techniques and has the highest PSNR value. Comparative findings are a better way for future studies, especially for unusual images of CT images due to brain damage.

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Abbreviations

OOPS:

Out-of-phase

IPS:

Better than in phase

CT:

Computed tomography

EP:

Electroencephalography

MEG:

Magnetoencephalography

NMR:

Nuclei emit electromagnetic energy

BOLD:

Blood oxygen level dependent

ERPs:

Event-related Potentials

dB:

Decibel units

PSNR:

Peak signal to noise ratio

MSE:

Mean-squared error

D:

Distortion

R:

Rate

DCT:

Discrete Cosine Transform

VQ:

Vector quantization.

FOVs:

Field of view

μ(x, y):

Linear attenuation coefficient for the material in the slice Filtered Back projection

DAS:

Data acquisition system

ADC:

Analog-to-digital converter

2D:

Two dimensional

ST:

Slice thickness

FOV:

Focal spot size display technique selection

NM:

Nuclear medicine

X-rays:

Radiographs

B:

Bit depth

F:

Intensity, grey level

x, y:

Spatial co-ordinates

Arguments:

Two co-ordinates (X, Y)

F(xi, Yj):

I = 0 → n-1; j = 0 → m − 1

l:

Intensity levels, grey levels

B:

No. of bits

l:

0, 1, 2, … L−1

MSE:

Mean squared error

PSNR:

Peak signal to noise ratio

log2:

Binary or bits

E:

Random event

p(E):

Probability

I(E):

Units of information

Let T(x, y):

(N * m) template

Let I(X, Y):

(N * M) image

(k, q):

Parameter space, can use (k, q) co-ordinates to represent a line

(x, y):

Image space, (x, y) co-ordinates

k:

Gradient

q:

Y intercept

t(l):

Accumulated frequencies

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Senthil, P., Suganya, M., Baidari, I. et al. Enhancement Sushisen algorithms in Images analysis Technologies to increase computerized tomography images. Int. j. inf. tecnol. (2020). https://doi.org/10.1007/s41870-020-00429-5

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Keywords

  • Computed tomography
  • Enhancement techniques
  • Increasing contrast
  • PSNR and MSE