FOE NET: Segmentation of Fetal in Ultrasound Images Using V-NET

Abstract

Ultrasound is a non-invasive method to diagnose and treat medical conditions. It is becoming increasingly popular to use portable ultrasound scanning devices to reduce patient wait times and make healthcare more convenient for patients. By using ultrasound imaging, you will be able to obtain images with better quality and also gain information about soft tissues. The interference caused by tissues reflected in ultrasound waves resulted in intensified speckle sound, complicating imaging. In this paper, a novel Foe-Net has been proposed for segmenting the fetal in ultrasound images. Initially, the input US images are noise removal phase using two different filters Adaptive Gaussian Filter (AGF) and Adaptive Bilateral Filter (ABF) used to reduce the noise artifacts. Then, the US images are enhanced using CLAHE and MSR for smoothing to enhance the image quality. Finally, the denoised images are input to the V-net is used to segment the fetal in the US images. The experimental outcomes of the proposed Multi-Scale Retinex (MSR) is an image enhancement technique that improves image quality by adjusting its illumination and enhancing details. Foe-Net was measured by specific parameters such as specificity, precision, and accuracy. The proposed Foe-Net achieves an overall accuracy of 99.48%, specificity of 98.56 %, and precision of 96.82 % for segmented fetal in ultrasound images. The proposed Foe-Net attains better pre-processing outcomes at low error rates and, high SNR, high PSNR, and high SSIM values.