Applying Speckle Noise Suppression to Refractive Indices Change Detection in Porous Silicon Microarrays

Ren, Ruyong; Jia, Zhenhong; Yang, Jie; Kasabov, Nikola

doi:10.3390/s19132975

Cited by 4 publications

(1 citation statement)

References 37 publications

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“…According to Figure 1 , the breast ultrasound image speckle de-noising process mainly consists of three parts: preprocessing, training and de-noising. First of all, the acquired breast ultrasound images are aligned, the standardized and clear ultrasound images are found, and the contrast increase processing is completed ( Piao et al, 2018 ; Ren et al, 2019 ). After processing, data expansion processing is performed to obtain training samples, and the 3D convolutional cloud network is obtained after training on the training samples.…”

Section: Methodsmentioning

confidence: 99%

Fast Speckle Noise Suppression Algorithm in Breast Ultrasound Image Using Three-Dimensional Deep Learning

Wang

Zhao

et al. 2022

Front. Physiol.

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The rapid development of ultrasound medical imaging technology has greatly broadened the scope of application of ultrasound, which has been widely used in the screening, diagnosis of breast diseases and so on. However, the presence of excessive speckle noise in breast ultrasound images can greatly reduce the image resolution and affect the observation and judgment of patients’ condition. Therefore, it is particularly important to investigate image speckle noise suppression. In the paper, we propose fast speckle noise suppression algorithm in breast ultrasound image using three-dimensional (3D) deep learning. Firstly, according to the gray value of the breast ultrasound image, the input breast ultrasound image contrast is enhanced using logarithmic and exponential transforms, and guided filter algorithm was used to enhance the details of glandular ultrasound image, and spatial high-pass filtering algorithm was used to suppress the excessive sharpening of breast ultrasound image to complete the pre-processing of breast ultrasound image and improve the image clarity; Secondly, the pre-processed breast ultrasound images were input into the 3D convolutional cloud neural network image speckle noise suppression model; Finally, the edge sensitive terms were introduced into the 3D convolutional cloud neural network to suppress the speckle noise of breast ultrasound images while retaining image edge information. The experiments demonstrate that the mean square error and false recognition rate all reduced to below 1.2% at the 100th iteration of training, and the 3D convolutional cloud neural network is well trained, and the signal-to-noise ratio of ultrasound image speckle noise suppression is greater than 60 dB, the peak signal-to-noise ratio is greater than 65 dB, the edge preservation index value exceeds the experimental threshold of 0.45, the speckle noise suppression time is low, the edge information is well preserved, and the image details are clearly visible. The speckle noise suppression time is low, the edge information is well preserved, and the image details are clearly visible, which can be applied to the field of breast ultrasound diagnosis.

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Section: Methodsmentioning

confidence: 99%