Deep Learning-Based Ultrasound Image Despeckling by Noise Model Estimation
Speckle noise is an inherent artifact appearing in medical images that significantly lowers the quality and accuracy of diagnosis and treatment. Therefore, speckle reduction is considered as an essential step before processing and analyzing the ultrasound images. In this paper, we propose an ultrasound speckle reduction method based on speckle noise model estimation using a deep learning architecture called “speckle noise-based inception convolutional denoising neural network" (SNICDNN). Regarding the complicated nature of speckle noise, an inception module is added to the first layer to boost the power of feature extraction. Reconstruction of the despeckled image is performed by introducing a mathematical method based on solving a quadratic equation and applying an image-based inception convolutional denoising autoencoder (IICDAE). The results of various quantitative and qualitative evaluations on real ultrasound images demonstrate that SNICDNN outperforms the state-of-the-art methods for ultrasound despeckling. SNICDNN achieves 0.4579 dB and 0.0100 additional gains on average for PSNR and SSIM, respectively, compared to other methods. Denoising ultrasound based on its noise model estimation is not only a novel approach in comparison to traditional denoising autoencoder models but also due to the fact that it uses mathematical solutions to recover denoised images, SNICDNN shows a greater power in ultrasound despeckling.
-
Multi-Temporal Remote Sensing Image Registration with Deep Neural Networks and Region of Interest
*, Seyed Mohammad Hoseini Panah, Masoud Khoshsima
Journal of Space Sciences, Technology and Applications, -
BiliBin: An Intelligent Mobile Phone-based Platform to Monitor Newborn Jaundice
Eisa Zarepour*, Mohammadreza Mohammadi, Morteza Zakeri-Nasrabadi, Sara Aein, Razieh Sangsari, Leila Taheri, Mojtaba Akbari, Ali Zabihallahpour
Iranian Journal of Electrical and Electronic Engineering, Sep 2024 -
Detection of the Location and Angle of Saffron Flowers using Deep Convolutional Networks
MohammadReza Mohammadi *, Mehrdad Karami
Machine Vision and Image Processing, -
Driver Drowsiness Detection by Identification of Yawning and Eye Closure
Mina Zohoorian Yazdi, *
Automotive Science and Engineering, Summer 2019