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

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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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Better than in phase


Computed tomography






Nuclei emit electromagnetic energy


Blood oxygen level dependent


Event-related Potentials


Decibel units


Peak signal to noise ratio


Mean-squared error






Discrete Cosine Transform


Vector quantization.


Field of view

μ(x, y):

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


Data acquisition system


Analog-to-digital converter


Two dimensional


Slice thickness


Focal spot size display technique selection


Nuclear medicine




Bit depth


Intensity, grey level

x, y:

Spatial co-ordinates


Two co-ordinates (X, Y)

F(xi, Yj):

I = 0 → n-1; j = 0 → m − 1


Intensity levels, grey levels


No. of bits


0, 1, 2, … L−1


Mean squared error


Peak signal to noise ratio


Binary or bits


Random event




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




Y intercept


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