Indian Journal of Science and Technology
Year: 2020, Volume: 13, Issue: 39, Pages: 4142-4150
S Sheela1*, 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
Email: [email protected]
Received Date:19 October 2020, Accepted Date:26 October 2020, Published Date:07 November 2020
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
© 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).
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