人工智能
图像分割
计算机科学
散斑噪声
计算机视觉
模式识别(心理学)
特征(语言学)
锐化
分割
图像处理
斑点图案
噪音(视频)
尺度空间分割
图像(数学)
语言学
哲学
作者
Mahmood Alzubaidi,Marco Agus,Khaled A. Al-Thelaya,Michel Makhlouf,Khalid Alyafei,Mowafa Househ
标识
DOI:10.1145/3576938.3576939
摘要
In ultrasound images, the speckle noise phenomenon is a typical issue that limits the accuracy of the images obtained from any quantitative measurement. Noise elimination in ultrasound images in the pre-processing stage is an important step to make the image fit for the following steps involved in image segmentation. However, various noises produce a range of significant impacts on the quality of the image and thus tend to affect the interpretation of neural networks. In this study, 35 different image processing techniques, including denoising, feature descriptor, edge detection, miscellaneous, clustering, morphological, sharpening, and composite techniques were used to determine the most optimal ultrasound image in order to improve the segmentation of ultrasound images. The performance of image processing techniques was compared using a statistical parameter, peak signal-to-noise ratio (PSNR). All techniques were examined on deep learning segmentation approach. The segmentation performance was evaluated using mean intersection over union (mIoU) and mean pixel accuracy (mPA). The results showed that the composite image technique (FancyPCA + Bilateral + Gabor) slightly enhanced the segmentation performance, with the most ideal mIoU 0.96893%, mPA 0.97831%, and average PSNR 53.034 dB.
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