Image Noise Removal in Ultrasound Breast Images Based on Hybrid Deep Learning Technique
Abstract
:1. Introduction
2. Literature Review
3. Proposed Framework Methodologies
3.1. Dataset Availability
3.2. Effective Destruction of Local Speckle Noise in Breast Images
3.3. Breast Image Pre-Processing and Image Enhancement
3.4. High-Pass Ultrasound Image Spatial Filtering for Breast Images
- Step 1: To improve the process of acquiring the bootstrap image, we first determine a grayscale value of N, of the source ultrasound breast image f, and then use Equation (3) to identify the associated breast ultrasound image h that best fits the categorization.
- Step 2: Having completed Step 1, the ultrasound breast image is utilized as the reference image I’m of the guide-filtration algorithm.
- Step 3: I’’ is the ultrasound breast outcome, and it is achieved by improving the edge data of I’ along with a high-pass filter in order to enlarge the edge data retention.
3.5. Logical-Pool Recurrent Neural Network—Local Speckle Noise Destruction
Local Speckle Noise Destruction Algorithm
Algorithm 1. Noise removal of the local speckle noise. | |
1: | Begin |
2: | Logarithmic and computational transforms are used to improve the differentiation of the input ultrasound breast images; the algorithm (guided filter) is used to improve the details of the glandular ultrasound images; and the spatial high-pass filtering algorithm is used to denoise the over-sharpening of the ultrasound breast images, all based on their grayscale values |
3: | The pre-processed ultrasound breast images are fed into a local-speckle-noise destruction model of a logical-pool recurrent neural network |
4: | Ultrasound breast images are susceptible to losing image edge information during the local speckle noise reduction procedure. If we want to preserve the edge information after local speckle noise removal is applied, we will need to understand how that information is lost during processing. The meaning of “edge information loss”.
|
5: | In order to construct ultrasound image gradients, we first analyze the aforementioned stages and then use edge loss pairs to compare the edges of canonical clear images of ultrasound breast images. The unique anatomy of the breast emphasizes the significance of the gradients in the vertical plane. That is why we first use contrast in the vertical direction to depict breast ultrasound images. Integrating edge loss and L1 distance with a recurrent neural network yields the following objective function:
|
6: | Enhance the loss function to optimize the edge-specific improvement feature of the ultrasound images during training with the logical-pool recurrent neural network. The resulting model will be more responsive in edge local speckle noise destruction in ultrasound images, enhancing its effect on ultrasound breast images |
7: | While noise removal reduces the local speckle noise of ultrasound breast images, the edge information is preserved by the action of the advantage term in the logical-pool recurrent neural network as described above |
8: | End |
3.6. Performance Metric Evaluation Standards
4. Experimental Results
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviation
Short Form | Abbreviation |
LPRNN | Logical-Pool Recurrent Neural Network |
SNR | Signal-to-Noise Ratio |
PSNR | Peak Signal-to-Noise Ratio |
DCNN | Deep Convolutional Neural Network |
IR | InfraRed |
3D | Three Dimensional |
KSVD | K-singular Value Decomposition |
SARN | Spatial-wise Attention Residual network |
CNN | Convolutional Neural Network |
JPEG | Joint Photographic Experts Group |
MRI | Magnetic Resonance Imaging |
ISO | International Organization for Standardization |
CBIS-DDSM | Curated Breast Imaging Subset-Digital Database for Screening Mammography |
XML | Extensible Markup Language |
SAR | Specific Absorption Rate |
MSE | Mean Square Error |
BPI | Border Protection Index |
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No. of Iterations | Mean Square Error | False Recognition Value |
---|---|---|
1 | 1.4 | 0.7 |
2 | 1 | 0.5 |
3 | 0.8 | 0.4 |
4 | 0.7 | 0.3 |
5 | 0.4 | 0.2 |
6 | 0.3 | 0.1 |
Methods | Signal-to-Noise Ratio | Peak Signal-to-Noise Ratio |
---|---|---|
Value in dB | ||
Proposed LPRNN | 63.8 | 68.7 |
Saeed Izadi et al. (2022) [6] | 58.7 | 64.2 |
Thayammal et al. (2021) [5] | 57.4 | 63.7 |
Sujeet More et al. (2021) [8] | 59.8 | 65.1 |
Nguyen Thanh-Trung et al. (2021) [11] | 56.5 | 63.4 |
Dihan Zheng et al. (2021) [14] | 54.2 | 62.8 |
Aayushi Nirmal et al. (2020) [9] | 52.6 | 62.6 |
Methods | INbreast Dataset | CBIS-DDSM Dataset | ||||
---|---|---|---|---|---|---|
25 db | 45 db | 65 db | 25 db | 45 db | 65 db | |
Proposed LPRNN | 6 | 9 | 10 | 11 | 15 | 13 |
Saeed Izadi et al. (2022) [6] | 21 | 19 | 21 | 21 | 23 | 17 |
Thayammal et al. (2021) [5] | 10 | 13 | 16 | 13 | 16 | 18 |
Sujeet More et al. (2021) [8] | 18 | 21 | 23 | 14 | 17 | 21 |
Nguyen Thanh-Trung et al. (2021) [11] | 11 | 21 | 24 | 17 | 20 | 19 |
Dihan Zheng et al. (2021) [14] | 19 | 20 | 22 | 15 | 21 | 20 |
Aayushi Nirmal et al. (2020) [9] | 17 | 21 | 21 | 15 | 21 | 22 |
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Vimala, B.B.; Srinivasan, S.; Mathivanan, S.K.; Muthukumaran, V.; Babu, J.C.; Herencsar, N.; Vilcekova, L. Image Noise Removal in Ultrasound Breast Images Based on Hybrid Deep Learning Technique. Sensors 2023, 23, 1167. https://doi.org/10.3390/s23031167
Vimala BB, Srinivasan S, Mathivanan SK, Muthukumaran V, Babu JC, Herencsar N, Vilcekova L. Image Noise Removal in Ultrasound Breast Images Based on Hybrid Deep Learning Technique. Sensors. 2023; 23(3):1167. https://doi.org/10.3390/s23031167
Chicago/Turabian StyleVimala, Baiju Babu, Saravanan Srinivasan, Sandeep Kumar Mathivanan, Venkatesan Muthukumaran, Jyothi Chinna Babu, Norbert Herencsar, and Lucia Vilcekova. 2023. "Image Noise Removal in Ultrasound Breast Images Based on Hybrid Deep Learning Technique" Sensors 23, no. 3: 1167. https://doi.org/10.3390/s23031167
APA StyleVimala, B. B., Srinivasan, S., Mathivanan, S. K., Muthukumaran, V., Babu, J. C., Herencsar, N., & Vilcekova, L. (2023). Image Noise Removal in Ultrasound Breast Images Based on Hybrid Deep Learning Technique. Sensors, 23(3), 1167. https://doi.org/10.3390/s23031167