Open Access

ARTICLE

Automatic Segmentation and Detection System for Varicocele Using Ultrasound Images

Ayman M. Abdalla^1,*, Mohammad Abu Awad², Omar AlZoubi², La'aly A. Al-Samrraie³

1 Department of Computer Science, Al-Zaytoonah University of Jordan, Amman, 11733, Jordan
2 Department of Computer Science, Jordan University of Science and Technology, Irbid, 22110, Jordan
3 Department of Water and Environmental Engineering, Al-Huson University College, Al-Balqa Applied University, Irbid, Jordan

* Corresponding Author: Ayman M. Abdalla. Email:

Computers, Materials & Continua 2022, 72(1), 797-814. https://doi.org/10.32604/cmc.2022.024913

Received 04 November 2021; Accepted 27 December 2021; Issue published 24 February 2022

Abstract

The enlarged veins in the pampiniform venous plexus, known as varicocele disease, are typically identified using ultrasound scans. The medical diagnosis of varicocele is based on examinations made in three positions taken to the right and left testicles of the male patient. The proposed system is designed to determine whether a patient is affected. Varicocele is more frequent on the left side of the scrotum than on the right and physicians commonly depend on the supine position more than other positions. Therefore, the experimental results of this study focused on images taken in the supine position of the left testicles of patients. There are two possible vein structures in each image: a cross-section (circular) and a tube (non-circular) structure. This proposed system identifies dilated (varicocele) veins of these structures in ultrasound images in three stages: preprocessing, processing, and detection and measurement. These three stages are applied in three different color modes: Grayscale, Red-Green-Blue (RGB), and Hue, Saturation, and Value (HSV). In the preprocessing stage, the region of interest enclosing the pampiniform plexus area is extracted using a median filter and threshold segmentation. Then, the processing stage employs different filters to perform image denoising. Finally, edge detection is applied in multiple steps and the detected veins are measured to determine if dilated veins exist. Overall implementation results showed the proposed system is faster and more effective than the previous work.

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Keywords

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