A comparison Study of Image Edge Segmentation Methods using Prewitt, Sobel and Laplacian of Gaussian for Medical Images

Authors

  • Management Information Systems Department, College of Administration & Economics, University of Basrah.

DOI:

https://doi.org/10.32792/jeps.v12i2.161

Keywords:

Image segmentation, Edge detection,, Prewitt,, Sobel,, Laplacian of Gaussian, PSNR, SNR

Abstract

Image processing has an important and main role in several fields. It uses to understand and discover the
image and its objects in efficiently and meaningful way. The understanding is a main step to extract
information form image. The more realization has been established from different scientists in the field
for image segmentation. The main segmentation purpose is to detect the edges information which
available inside an image clearly. Edges are the important character for image and it has produced by
summaries of the things. Mostly, Edge detection steps and its techniques have employed to evaluate and
analysis of image characteristic. Many and several kinds of techniques for detecting the edges from any
type of images. This paper has achieved the comprehensive analysis about the many edge detection
techniques like Prewitt, Sobel and Laplacian of Gaussian. The comparisons are in terms PSNR (Peak
signal to noise ratio), SNR (Signal to noise ratio) and Entropy. Finally, experimentally observed that
Laplacian of Gaussian technique is working well and recorded better results than others techniques.

References

A. S. Abdullah, M. H. Ali, and M. Waleed, “Distributed Prewitt Edge Detection System Using

Lightness of Ycbcr Color Space,” Webology, vol. 19, no. 1, pp. 1460–1473, 2022.

D. Kurchaniya and M. Dixit, “A Comprehensive Analysis of Image Edge Detection Techniques,”

vol. 12, no. 11, pp. 1–12, 2017.

D. Kumar and S. Dhingra, “A Review on Quality of Image during CBIR Operations and

Compression,” in Computing & Intelligent Systems, 2021, pp. 201–211.

M. J. Firdouse, “A Survey on Lung Segmentation Methods,” vol. 10, no. 9, pp. 2875–2885, 2017.

X. Yan et al., “Combination of Sobel + Prewitt Edge Detection Method with Roberts + Canny on

Passion Flower Image Identification Combination of Sobel + Prewitt Edge Detection Method with

Roberts + Canny on Passion Flower Image Identification,” Virtual Conf. Eng. Sci. Technol., pp. 1–

, 2021.

K. M. Sudharshan, P. Joshi, and T. F. Francis, “Design of a Sobel Edge Detection Algorithm on

FPGA,” Turkish J. Comput. Math. Educ., vol. 12, no. 12, pp. 2458–2462, 2021.

E. G. Kaur, E. Komal, and P. G. Singh, “Edge Detection using Enhanced Laplacian Operator on

Diabetic affected Eyes,” Int. J. Innov. Sci. Eng. Technol., vol. 8, no. 3, pp. 132–139, 2021.

O. Performances, “Evaluation and Comparative Study of Edge Detection Techniques,” J. Comput.

Eng. (, vol. 22, no. 5, pp. 6–15, 2020.

K. B. Krishnan, S. P. Ranga, and N. Guptha, “A Survey on Different Edge Detection Techniques

for Image Segmentation,” vol. 10, no. January, 2017.

M. Waseem Khan, “A Survey: Image Segmentation Techniques,” Int. J. Futur. Comput. Commun.,

vol. 3, no. 2, pp. 89–93, 2014.

X. Yan et al., “A Survey of Sobel Edge Detection VLSI Architectures A Survey of Sobel Edge

Detection VLSI Architectures,” J. Phys., vol. 3, no. 2, pp. 1–11, 2021.

K. Elakkia and P. Narendran, “Survey of Medical Image Segmentation Using Removal of

Gaussian Noise in Medical Image,” IJESC, vol. 6, no. 6, pp. 7593–7595, 2016.

L. Zhou and E. Xu, “Research and Implementation of an OpenMV- Based Target Edge Detection

and Tracking System,” J. Phys., pp. 1–8, 2022.

P. Kandhway and A. Kumar, Spatial context-based optimal multilevel energy curve thresholding

for image segmentation using soft computing techniques, vol. 9. Springer London, 2019.

M. A. Sullabi, “Using Prewitt Operator as Gradient-Based Method for Fingerprint Singular

Points,” Int. J. Enginereing Inf. Technol., vol. 6, no. 2, pp. 2–5, 2020.

A. Dixit, S. Majumdar, N. Campus, U. Pradesh, and E. Systems, “C OMPARATIVE A NALYSIS

OF C OIFLET AND D AUBECHIES W AVELETS USING G LOBAL T HRESHOLD FOR I

MAGE D E -,” vol. 6, no. 5, pp. 2247–2252, 2013.

S. Bejinariu, H. Costin, S. Member, F. Rotaru, and R. Luca, “Nature Inspired Optimization

Techniques for Image Processing— A Short Review,” in Intelligent Systems Reference Library

Jayanthi & Shashikumar, “Survey on Agriculture Image Segmentation Techniques,” Asian J. Appl.

Sci. Technol., vol. 1, no. 8, pp. 143–147, 2017.

M. Yasir, S. Hossain, S. Nazir, S. Khan, and R. Thapa, “Object Identification Using Manipulated

Edge Detection Techniques,” Sci. Puplishing Gr., vol. 3, no. 1, pp. 1–6, 2022.

R. R. K. Al-taie, B. J. Saleh, and L. A. Salman, “Image Edge-Segmentation Techniques : A

Review,” Int. J. Sci. Res. Sci. Eng. Technol., vol. 8, no. 5, pp. 252–257, 2021.

S. M. A. Huda, I. J. Ila, S. Sarder, and N. Y. Ali, “An Improved Approach for Detection of

Diabetic Retinopathy Using Feature Importance and Machine Learning Algorithms,” Int. J. Appl.

Eng. Res., vol. 13, no. June, pp. 1716–1721, 2019.

S. R. J. Ramson, K. L. Raju, S. Vishnu, and T. Anagnostopoulos, Nature Inspired Optimization

Techniques for Image Processing — A Short Chapter 5 Nature Inspired Optimization Techniques

for Image Processing — A Short Review, no. January. Springer International Publishing, 2019.

F. Banu, “An Optimized Approach of Modified BAT Algorithm to Record Deduplication,” Int. J.

Comput. Appl., vol. 62, no. 1, pp. 10–15, 2013.

S. A. Saleem, “Survey on Color Image Enhancement Techniques using Spatial Filtering Survey on

Color Image Enhancement Techniques using Spatial Filtering,” no. May 2014, 2015.

S. Kaur, “Review and Analysis of Various Image Enhancement Techniques,” Int. J. Comput. Appl.

Technol. Res., vol. 4, no. 5, pp. 414–418, 2015.

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Published

2023-02-09

Issue

Vol. 12 No. 2 (2022): University of Thi-Qar Journal of education for pure science

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