Enhancer for ovarian cyst segmentation using adaptive thresholding technique

S Sheela; M Sumathi

doi:10.17485/IJST/v13i39.1602

Article

Enhancer for ovarian cyst segmentation using adaptive thresholding technique

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v13i39.1602

Year: 2020, Volume: 13, Issue: 39, Pages: 4142-4150

Original Article

Enhancer for ovarian cyst segmentation using adaptive thresholding technique

S Sheela^1*, M Sumathi²

¹Research Scholar, Sathyabama Institute of Science and Technology, Chennai, 119, Tel.:9710939093
²Professor, Department of ECE, Sathyabama Institute of Science and Technology, Chennai,119

*Corresponding Author
Tel: 9710939093
Email: [email protected]

Received Date:19 October 2020, Accepted Date:26 October 2020, Published Date:07 November 2020

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objective: To achieve the accurate segmentation of ovarian cyst from the ultrasound images. Method: Ovarian cyst ultrasound images are taken from ultrasound images.com and sonoworld.com. The cysts are segmented using adaptive thresholding technique. The segmented image (binary image) is divided into sub blocks and then number of binary transition in each block is calculated. Based on the number of transition, the pixel values are replaced by 0 or the same pixel value is maintained. In order to measure the performance of the proposed enhancer various measures like Accuracy (ACC), Dice Coefficient (DC), Jaccard Similarity Index (JSI), Matthews correlation coefficient (MCC), Sensitivity, Specificity and Precision are measured. Findings: In order to analyse the performance of the enhancer with adaptive thresholding technique, 100 ultrasound ovarian cyst images are taken. The enhancer produced better result than the existing adaptive thresholding technique. Novelty/Application: The proposed enhancer enriches the quality of the ovarian cyst segmentation.

Keywords: Segmentation; adaptive thresholding technique; ultrasound images; poly cystic ovarian syndrome; follicle; ovarian cyst

References

Kumar H, Prasanna S, Srinivasan. Segmentation of Polycystic Ovary in Ultrasound Images. In: 2nd International Conference on Current Trends in Engineering and Technology. p. 237–240.
Sheela S, Sumathi M. Study and Theoretical Investigations on PCOS. In: IEEE International Conference on Computational Intelligence and Computing Research. 2014:559–588.
Gopalakrishnan C, Iyapparaja M. Active contour with modified Otsu method for automatic detection of polycystic ovary syndrome from ultrasound image of ovary. Multimedia Tools and Applications. 2019. Available from: https://doi.org/10.1007/s11042-019-07762-3
Bogachev MI, Markelov OA, Volkov VY. Adaptive Object Selection Based on Multi-Threshold Image Processing. In: Wave Electronics and its Application in Information and Telecommunication Systems, WECONF . 2019:1–5.
Adhikari G, Mukherjee R, Dasgupta T. A Local Adaptive Region-Wise Histogram Correction and Thresholding Technique for Very Poorly Illuminated Images. In: 2018 International Conference on Wireless Communications. 2018.
Upadhyay P, Kumar S, Chandra A, Sharma. A Novel Approach of Adaptive Thresholding for Image Segmentation on GPU. 4th International Conference on Parallel, Distributed and Grid Computing. 2016;2016:652–655. Available from: https://doi.org/10.1109/PDGC.2016.7913203
Singh T, Romen. A New Local Adaptive Thresholding Technique in Binarization. 2012;8(6):271–277. Available from: http://arxiv.org/abs/1201.5227
Deshpande SS, Wakankar A. Automated Detection of Polycystic Ovarian Syndrome Using Follicle Recognition. In: Proceedings of 2014 IEEE International Conference on Advanced Communication, Control and Computing Technologies. (Vol. 978, pp. 1341-1346) 2015.

Copyright

© 2020 Sheela & Sumathi.This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee).