Derechos de autor 2024 Investigación e Innovación en Ingenierías
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
DUnet-MI Corrección de ruido en imágenes médicas implementando redes neuronales artificiales tipo autoencoder
Corresponding Author(s) : Andrea Ardila
Investigación e Innovación en Ingenierías,
Vol. 12 Núm. 2 (2024): Julio - Diciembre
Resumen
Objetivo: Presentar un método de aprendizaje profundo denominado Denoising Unet for Medical Image, DUnet-MI, enfocado en la corrección de diferentes niveles de ruido en imágenes médicas en las modalidades de Rayos X, Tomografía Computarizada y Resonancia Magnética. Metodología: Se aborda una solución para reducir ruido Gaussiano y ruido sal y pimienta, que se suele agregar a la imagen médica por el proceso de obtención, transmisión y/o recepción. DUnet-MI es una adaptación del modelo Unet con variaciones en las capas, filtros e hiperparámetros para corregir ruido e incrementar los valores de las métricas de calidad de relación señal a ruido pico, PSNR por sus siglas en inglés, y la medida del índice de similitud estructural, SSIM por sus siglas en inglés. Resultados: se analizó el promedio de 200 imágenes para procesar 7 niveles de ruido. DUnet-MI logró estimaciones promedias de hasta en PSNR y valores de SSIM de al procesar ruido Gaussiano y, en PSNR y en SSIM al corregir ruido sal y pimienta. Conclusiones: DUnet-MI demostró mejoras en las métricas de calidad que superan las obtenidas por los métodos clásicos presentes en la literatura, demostrando su funcionalidad para mejorar la calidad de las imágenes médicas.
Objetivo: Presentar un método de aprendizaje profundo denominado Denoising Unet for Medical Image, DUnet-MI, enfocado en la corrección de diferentes niveles de ruido en imágenes médicas en las modalidades de Rayos X, Tomografía Computarizada y Resonancia Magnética. Metodología: Se aborda una solución para reducir ruido Gaussiano y ruido sal y pimienta, que se suele agregar a la imagen médica por el proceso de obtención, transmisión y/o recepción. DUnet-MI es una adaptación del modelo Unet con variaciones en las capas, filtros e hiperparámetros para corregir ruido e incrementar los valores de las métricas de calidad de relación señal a ruido pico, PSNR por sus siglas en inglés, y la medida del índice de similitud estructural, SSIM por sus siglas en inglés. Resultados: se analizó el promedio de 200 imágenes para procesar 7 niveles de ruido. DUnet-MI logró estimaciones promedias de hasta en PSNR y valores de SSIM de al procesar ruido Gaussiano y, en PSNR y en SSIM al corregir ruido sal y pimienta. Conclusiones: DUnet-MI demostró mejoras en las métricas de calidad que superan las obtenidas por los métodos clásicos presentes en la literatura, demostrando su funcionalidad para mejorar la calidad de las imágenes médicas.
Palabras clave
Descargar cita
Endnote/Zotero/Mendeley (RIS)BibTeX
- S. M. Anwar, M. Majid, A. Qayyum, M. Awais, M. Alnowami, and M. K. Khan, “Medical Image Analysis using Convolutional Neural Networks: A Review,” J Med Syst, vol. 42, no. 11, p. 226, Oct. 2018, doi: 10.1007/s10916-018-1088-1.
- V.-G. Nguyen, “Digital Radiography with a Consumer Camera: Image Denoising and Deblurring,” IEIE Transactions on Smart Processing & Computing, vol. 10, no. 5, pp. 398–406, Oct. 2021, doi: 10.5573/IEIESPC.2021.10.5.398.
- A. Ribeiro et al., “Ionising radiation exposure from medical imaging – A review of Patient’s (un) awareness,” Radiography, vol. 26, no. 2, pp. e25–e30, May 2020, doi: 10.1016/j.radi.2019.10.002.
- I. Domingues, G. Pereira, P. Martins, H. Duarte, J. Santos, and P. H. Abreu, “Using deep learning techniques in medical imaging: a systematic review of applications on CT and PET,” Artif Intell Rev, vol. 53, no. 6, pp. 4093–4160, Nov. 2020, doi: 10.1007/s10462-019-09788-3.
- K. Kim and Y. Lee, “Improvement of signal and noise performance using single image super-resolution based on deep learning in single photon-emission computed tomography imaging system,” Nuclear Engineering and Technology, vol. 53, no. 7, pp. 2341–2347, Jul. 2021, doi: 10.1016/j.net.2021.01.011.
- S. V. Mohd Sagheer and S. N. George, “A review on medical image denoising algorithms,” Biomed Signal Process Control, vol. 61, p. 102036, Aug. 2020, doi: 10.1016/j.bspc.2020.102036.
- S. U. Khan, N. Ullah, I. Ahmed, I. Ahmad, and M. I. Mahsud, “MRI Imaging, Comparison of MRI with other Modalities, Noise in MRI Images and Machine Learning Techniques for Noise Removal: A Review,” Current Medical Imaging Formerly Current Medical Imaging Reviews, vol. 15, no. 3, pp. 243–254, Feb. 2019, doi: 10.2174/1573405614666180726124952.
- W.-H. Lee, M. Ozger, U. Challita, and K. W. Sung, “Noise Learning-Based Denoising Autoencoder,” IEEE Communications Letters, vol. 25, no. 9, pp. 2983–2987, Sep. 2021, doi: 10.1109/LCOMM.2021.3091800.
- A. Saleh Ahmed, W. H. El-Behaidy, and A. A. A. Youssif, “Medical image denoising system based on stacked convolutional autoencoder for enhancing 2-dimensional gel electrophoresis noise reduction,” Biomed Signal Process Control, vol. 69, p. 102842, Aug. 2021, doi: 10.1016/j.bspc.2021.102842.
- G. Contreras, J. Pabon, H. Garcia, F. Rojas, and H. Arguello, “Correction of Designed Compressive Spectral Imaging Measurements Using a Deep Learning-Based Method,” in 2021 XXIII Symposium on Image, Signal Processing and Artificial Vision (STSIVA), IEEE, Sep. 2021, pp. 1–5. doi: 10.1109/STSIVA53688.2021.9592024.
- B. Goyal, A. Dogra, S. Agrawal, and B. S. Sohi, “Noise Issues Prevailing in Various Types of Medical Images,” Biomedical and Pharmacology Journal, vol. 11, no. 3, pp. 1227–1237, Sep. 2018, doi: 10.13005/bpj/1484.
- O. Gulenko et al., “Deep-Learning-Based Algorithm for the Removal of Electromagnetic Interference Noise in Photoacoustic Endoscopic Image Processing,” Sensors, vol. 22, no. 10, p. 3961, May 2022, doi: 10.3390/s22103961.
- D. Thanh, P. Surya, and L. M. Hieu, “A Review on CT and X-Ray Images Denoising Methods,” Informatica, vol. 43, no. 2, pp. 151–159, Jun. 2019, doi: 10.31449/inf.v43i2.2179.
- ugur erkan, D. N. H. Thanh, L. M. Hieu, and S. Enginoglu, “An Iterative Mean Filter for Image Denoising,” IEEE Access, vol. 7, pp. 167847–167859, 2019, doi: 10.1109/ACCESS.2019.2953924.
- N. Kumar and M. Nachamai, “Noise Removal and Filtering Techniques used in Medical Images,” Oriental journal of computer science and technology, vol. 10, no. 1, pp. 103–113, Mar. 2017, doi: 10.13005/ojcst/10.01.14.
- M. A. Aslam, M. A. Munir, and D. Cui, “Noise Removal from Medical Images Using Hybrid Filters of Technique,” J Phys Conf Ser, vol. 1518, no. 1, p. 012061, Apr. 2020, doi: 10.1088/1742-6596/1518/1/012061.
- A. Voulodimos, N. Doulamis, A. Doulamis, and E. Protopapadakis, “Deep Learning for Computer Vision: A Brief Review,” Comput Intell Neurosci, vol. 2018, pp. 1–13, 2018, doi: 10.1155/2018/7068349.
- S. A. Villar, S. Torcida, and G. G. Acosta, “Median Filtering: A New Insight,” J Math Imaging Vis, vol. 58, no. 1, pp. 130–146, May 2017, doi: 10.1007/s10851-016-0694-0.
- M. Elhoseny and K. Shankar, “Optimal bilateral filter and Convolutional Neural Network based denoising method of medical image measurements,” Measurement, vol. 143, pp. 125–135, Sep. 2019, doi: 10.1016/j.measurement.2019.04.072.
- N. Chauhan and B.-J. Choi, “Denoising Approaches Using Fuzzy Logic and Convolutional Autoencoders for Human Brain MRI Image,” INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS, vol. 19, no. 3, pp. 135–139, Sep. 2019, doi: 10.5391/IJFIS.2019.19.3.135.
- C. Tian, L. Fei, W. Zheng, Y. Xu, W. Zuo, and C.-W. Lin, “Deep learning on image denoising: An overview,” Neural Networks, vol. 131, pp. 251–275, Nov. 2020, doi: 10.1016/j.neunet.2020.07.025.
- K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, “Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising,” IEEE Transactions on Image Processing, vol. 26, no. 7, pp. 3142–3155, Jul. 2017, doi: 10.1109/TIP.2017.2662206.
- P. Liu, M. D. El Basha, Y. Li, Y. Xiao, P. C. Sanelli, and R. Fang, “Deep Evolutionary Networks with Expedited Genetic Algorithms for Medical Image Denoising,” Med Image Anal, vol. 54, pp. 306–315, May 2019, doi: 10.1016/j.media.2019.03.004.
- M. Abd Elaziz et al., “Advanced metaheuristic optimization techniques in applications of deep neural networks: a review,” Neural Comput Appl, vol. 33, no. 21, pp. 14079–14099, Nov. 2021, doi: 10.1007/s00521-021-05960-5.
- O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9351, pp. 234–241, 2015, doi: 10.1007/978-3-319-24574-4_28.
- N. Zhang, S.-L. Shen, A. Zhou, and Y.-S. Xu, “Investigation on Performance of Neural Networks Using Quadratic Relative Error Cost Function,” IEEE Access, vol. 7, pp. 106642–106652, 2019, doi: 10.1109/ACCESS.2019.2930520.
- A. F. Agarap, “Deep Learning using Rectified Linear Units (ReLU),” arXiv preprint, Mar. 2018.
- U. Sara, M. Akter, and M. S. Uddin, “Image Quality Assessment through FSIM, SSIM, MSE and PSNR—A Comparative Study,” Journal of Computer and Communications, vol. 07, no. 03, pp. 8–18, 2019, doi: 10.4236/jcc.2019.73002.
- G. van Rossum, “Python,” 1991. https://www.python.org/
- M. Abadi et al., “TensorFlow: A system for large-scale machine learning,” Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2016, pp. 265–283, 2016.
- N. Ketkar, “Introduction to Keras,” in Deep Learning with Python, Berkeley, CA: Apress, 2017, pp. 97–111. doi: 10.1007/978-1-4842-2766-4_7.
- Google, “Te damos la bienvenida a Colaboratory,” Google Colab. https://colab.research.google.com/ (accessed Jul. 02, 2023).
- D. Kermany, K. Zhang, and M. Goldbaum, “Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification,” vol. 2, 2018, doi: 10.17632/RSCBJBR9SJ.2.
- S. Bhuvaji, A. Kadam, P. Bhumkar, and S. Dedge, “Brain Tumor Classification (MRI),” 2020.
- M. Maftouni, A. Chung Chee Law, and B. Shen, “A Robust Ensemble-Deep Learning Model for COVID-19 Diagnosis based on an Integrated CT Scan Images Database,” in Proceedings of the 2021 IISE Annual Conference, Institute of Industrial and Systems Engineers (IISE), Jun. 2021, pp. 632–637.
- Kaggle, “Kaggle.” https://www.kaggle.com/
- G. Bradski, “The OpenCV Library,” Dr. Dobb’s Journal of Software Tools, vol. 25, no. 11, pp. 122–125, 2000.
- C. R. Harris et al., “Array programming with NumPy,” Nature, vol. 585, no. 7825, pp. 357–362, 2020, doi: 10.1038/s41586-020-2649-2.
- S. van der Walt et al., “scikit-image: image processing in Python,” PeerJ, vol. 2, p. e453, Jun. 2014, doi: 10.7717/peerj.453.
Referencias
S. M. Anwar, M. Majid, A. Qayyum, M. Awais, M. Alnowami, and M. K. Khan, “Medical Image Analysis using Convolutional Neural Networks: A Review,” J Med Syst, vol. 42, no. 11, p. 226, Oct. 2018, doi: 10.1007/s10916-018-1088-1.
V.-G. Nguyen, “Digital Radiography with a Consumer Camera: Image Denoising and Deblurring,” IEIE Transactions on Smart Processing & Computing, vol. 10, no. 5, pp. 398–406, Oct. 2021, doi: 10.5573/IEIESPC.2021.10.5.398.
A. Ribeiro et al., “Ionising radiation exposure from medical imaging – A review of Patient’s (un) awareness,” Radiography, vol. 26, no. 2, pp. e25–e30, May 2020, doi: 10.1016/j.radi.2019.10.002.
I. Domingues, G. Pereira, P. Martins, H. Duarte, J. Santos, and P. H. Abreu, “Using deep learning techniques in medical imaging: a systematic review of applications on CT and PET,” Artif Intell Rev, vol. 53, no. 6, pp. 4093–4160, Nov. 2020, doi: 10.1007/s10462-019-09788-3.
K. Kim and Y. Lee, “Improvement of signal and noise performance using single image super-resolution based on deep learning in single photon-emission computed tomography imaging system,” Nuclear Engineering and Technology, vol. 53, no. 7, pp. 2341–2347, Jul. 2021, doi: 10.1016/j.net.2021.01.011.
S. V. Mohd Sagheer and S. N. George, “A review on medical image denoising algorithms,” Biomed Signal Process Control, vol. 61, p. 102036, Aug. 2020, doi: 10.1016/j.bspc.2020.102036.
S. U. Khan, N. Ullah, I. Ahmed, I. Ahmad, and M. I. Mahsud, “MRI Imaging, Comparison of MRI with other Modalities, Noise in MRI Images and Machine Learning Techniques for Noise Removal: A Review,” Current Medical Imaging Formerly Current Medical Imaging Reviews, vol. 15, no. 3, pp. 243–254, Feb. 2019, doi: 10.2174/1573405614666180726124952.
W.-H. Lee, M. Ozger, U. Challita, and K. W. Sung, “Noise Learning-Based Denoising Autoencoder,” IEEE Communications Letters, vol. 25, no. 9, pp. 2983–2987, Sep. 2021, doi: 10.1109/LCOMM.2021.3091800.
A. Saleh Ahmed, W. H. El-Behaidy, and A. A. A. Youssif, “Medical image denoising system based on stacked convolutional autoencoder for enhancing 2-dimensional gel electrophoresis noise reduction,” Biomed Signal Process Control, vol. 69, p. 102842, Aug. 2021, doi: 10.1016/j.bspc.2021.102842.
G. Contreras, J. Pabon, H. Garcia, F. Rojas, and H. Arguello, “Correction of Designed Compressive Spectral Imaging Measurements Using a Deep Learning-Based Method,” in 2021 XXIII Symposium on Image, Signal Processing and Artificial Vision (STSIVA), IEEE, Sep. 2021, pp. 1–5. doi: 10.1109/STSIVA53688.2021.9592024.
B. Goyal, A. Dogra, S. Agrawal, and B. S. Sohi, “Noise Issues Prevailing in Various Types of Medical Images,” Biomedical and Pharmacology Journal, vol. 11, no. 3, pp. 1227–1237, Sep. 2018, doi: 10.13005/bpj/1484.
O. Gulenko et al., “Deep-Learning-Based Algorithm for the Removal of Electromagnetic Interference Noise in Photoacoustic Endoscopic Image Processing,” Sensors, vol. 22, no. 10, p. 3961, May 2022, doi: 10.3390/s22103961.
D. Thanh, P. Surya, and L. M. Hieu, “A Review on CT and X-Ray Images Denoising Methods,” Informatica, vol. 43, no. 2, pp. 151–159, Jun. 2019, doi: 10.31449/inf.v43i2.2179.
ugur erkan, D. N. H. Thanh, L. M. Hieu, and S. Enginoglu, “An Iterative Mean Filter for Image Denoising,” IEEE Access, vol. 7, pp. 167847–167859, 2019, doi: 10.1109/ACCESS.2019.2953924.
N. Kumar and M. Nachamai, “Noise Removal and Filtering Techniques used in Medical Images,” Oriental journal of computer science and technology, vol. 10, no. 1, pp. 103–113, Mar. 2017, doi: 10.13005/ojcst/10.01.14.
M. A. Aslam, M. A. Munir, and D. Cui, “Noise Removal from Medical Images Using Hybrid Filters of Technique,” J Phys Conf Ser, vol. 1518, no. 1, p. 012061, Apr. 2020, doi: 10.1088/1742-6596/1518/1/012061.
A. Voulodimos, N. Doulamis, A. Doulamis, and E. Protopapadakis, “Deep Learning for Computer Vision: A Brief Review,” Comput Intell Neurosci, vol. 2018, pp. 1–13, 2018, doi: 10.1155/2018/7068349.
S. A. Villar, S. Torcida, and G. G. Acosta, “Median Filtering: A New Insight,” J Math Imaging Vis, vol. 58, no. 1, pp. 130–146, May 2017, doi: 10.1007/s10851-016-0694-0.
M. Elhoseny and K. Shankar, “Optimal bilateral filter and Convolutional Neural Network based denoising method of medical image measurements,” Measurement, vol. 143, pp. 125–135, Sep. 2019, doi: 10.1016/j.measurement.2019.04.072.
N. Chauhan and B.-J. Choi, “Denoising Approaches Using Fuzzy Logic and Convolutional Autoencoders for Human Brain MRI Image,” INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS, vol. 19, no. 3, pp. 135–139, Sep. 2019, doi: 10.5391/IJFIS.2019.19.3.135.
C. Tian, L. Fei, W. Zheng, Y. Xu, W. Zuo, and C.-W. Lin, “Deep learning on image denoising: An overview,” Neural Networks, vol. 131, pp. 251–275, Nov. 2020, doi: 10.1016/j.neunet.2020.07.025.
K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, “Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising,” IEEE Transactions on Image Processing, vol. 26, no. 7, pp. 3142–3155, Jul. 2017, doi: 10.1109/TIP.2017.2662206.
P. Liu, M. D. El Basha, Y. Li, Y. Xiao, P. C. Sanelli, and R. Fang, “Deep Evolutionary Networks with Expedited Genetic Algorithms for Medical Image Denoising,” Med Image Anal, vol. 54, pp. 306–315, May 2019, doi: 10.1016/j.media.2019.03.004.
M. Abd Elaziz et al., “Advanced metaheuristic optimization techniques in applications of deep neural networks: a review,” Neural Comput Appl, vol. 33, no. 21, pp. 14079–14099, Nov. 2021, doi: 10.1007/s00521-021-05960-5.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9351, pp. 234–241, 2015, doi: 10.1007/978-3-319-24574-4_28.
N. Zhang, S.-L. Shen, A. Zhou, and Y.-S. Xu, “Investigation on Performance of Neural Networks Using Quadratic Relative Error Cost Function,” IEEE Access, vol. 7, pp. 106642–106652, 2019, doi: 10.1109/ACCESS.2019.2930520.
A. F. Agarap, “Deep Learning using Rectified Linear Units (ReLU),” arXiv preprint, Mar. 2018.
U. Sara, M. Akter, and M. S. Uddin, “Image Quality Assessment through FSIM, SSIM, MSE and PSNR—A Comparative Study,” Journal of Computer and Communications, vol. 07, no. 03, pp. 8–18, 2019, doi: 10.4236/jcc.2019.73002.
G. van Rossum, “Python,” 1991. https://www.python.org/
M. Abadi et al., “TensorFlow: A system for large-scale machine learning,” Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2016, pp. 265–283, 2016.
N. Ketkar, “Introduction to Keras,” in Deep Learning with Python, Berkeley, CA: Apress, 2017, pp. 97–111. doi: 10.1007/978-1-4842-2766-4_7.
Google, “Te damos la bienvenida a Colaboratory,” Google Colab. https://colab.research.google.com/ (accessed Jul. 02, 2023).
D. Kermany, K. Zhang, and M. Goldbaum, “Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification,” vol. 2, 2018, doi: 10.17632/RSCBJBR9SJ.2.
S. Bhuvaji, A. Kadam, P. Bhumkar, and S. Dedge, “Brain Tumor Classification (MRI),” 2020.
M. Maftouni, A. Chung Chee Law, and B. Shen, “A Robust Ensemble-Deep Learning Model for COVID-19 Diagnosis based on an Integrated CT Scan Images Database,” in Proceedings of the 2021 IISE Annual Conference, Institute of Industrial and Systems Engineers (IISE), Jun. 2021, pp. 632–637.
Kaggle, “Kaggle.” https://www.kaggle.com/
G. Bradski, “The OpenCV Library,” Dr. Dobb’s Journal of Software Tools, vol. 25, no. 11, pp. 122–125, 2000.
C. R. Harris et al., “Array programming with NumPy,” Nature, vol. 585, no. 7825, pp. 357–362, 2020, doi: 10.1038/s41586-020-2649-2.
S. van der Walt et al., “scikit-image: image processing in Python,” PeerJ, vol. 2, p. e453, Jun. 2014, doi: 10.7717/peerj.453.