Effect of Speckle Filtering in the Performance of Segmentation of Ultrasound Images Using CNNs
The convolutional neural networks (CNNs) as tools for ultrasound image segmentation often have their performance affected by the low signal-to-noise ratio of the images. This prevents a correct classification and extraction of relevant information and therefore affects clinical diagnosis. We propose...
- Autores:
-
Romero-Mercado, Caleb D.
Contreraz-Ortiz, Sonia H.
Marrugo, Andres G.
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12161
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12161
- Palabra clave:
- Photoacoustic Tomography;
Thermoacoustics;
Echography
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Effect of Speckle Filtering in the Performance of Segmentation of Ultrasound Images Using CNNs |
| title |
Effect of Speckle Filtering in the Performance of Segmentation of Ultrasound Images Using CNNs |
| spellingShingle |
Effect of Speckle Filtering in the Performance of Segmentation of Ultrasound Images Using CNNs Photoacoustic Tomography; Thermoacoustics; Echography LEMB |
| title_short |
Effect of Speckle Filtering in the Performance of Segmentation of Ultrasound Images Using CNNs |
| title_full |
Effect of Speckle Filtering in the Performance of Segmentation of Ultrasound Images Using CNNs |
| title_fullStr |
Effect of Speckle Filtering in the Performance of Segmentation of Ultrasound Images Using CNNs |
| title_full_unstemmed |
Effect of Speckle Filtering in the Performance of Segmentation of Ultrasound Images Using CNNs |
| title_sort |
Effect of Speckle Filtering in the Performance of Segmentation of Ultrasound Images Using CNNs |
| dc.creator.fl_str_mv |
Romero-Mercado, Caleb D. Contreraz-Ortiz, Sonia H. Marrugo, Andres G. |
| dc.contributor.author.none.fl_str_mv |
Romero-Mercado, Caleb D. Contreraz-Ortiz, Sonia H. Marrugo, Andres G. |
| dc.subject.keywords.spa.fl_str_mv |
Photoacoustic Tomography; Thermoacoustics; Echography |
| topic |
Photoacoustic Tomography; Thermoacoustics; Echography LEMB |
| dc.subject.armarc.none.fl_str_mv |
LEMB |
| description |
The convolutional neural networks (CNNs) as tools for ultrasound image segmentation often have their performance affected by the low signal-to-noise ratio of the images. This prevents a correct classification and extraction of relevant information and therefore affects clinical diagnosis. We propose a study of the effect of different speckle filtering methods on CNN performance. For the proposed metrics (Jaccard coefficient and BF-Score), it was obtained that the SRAD filter exhibited the best behavior even in the lowest quality data. In addition, the lowest values were obtained for the standard deviation and variance, which translates into lower data dispersion, better repeatability, and, therefore, greater confidence in its accuracy. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. |
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2022 |
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2022 |
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2023-07-19T12:57:15Z |
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2023-07-19T12:57:15Z |
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2023 |
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Romero-Mercado, C. D., Contreras-Ortiz, S. H., & Marrugo, A. G. (2022, November). Effect of Speckle Filtering in the Performance of Segmentation of Ultrasound Images Using CNNs. In Workshop on Engineering Applications (pp. 150-159). Cham: Springer Nature Switzerland. |
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https://hdl.handle.net/20.500.12585/12161 |
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10.1007/978-3-031-20611-5_13 |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
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Romero-Mercado, C. D., Contreras-Ortiz, S. H., & Marrugo, A. G. (2022, November). Effect of Speckle Filtering in the Performance of Segmentation of Ultrasound Images Using CNNs. In Workshop on Engineering Applications (pp. 150-159). Cham: Springer Nature Switzerland. 10.1007/978-3-031-20611-5_13 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
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https://hdl.handle.net/20.500.12585/12161 |
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eng |
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eng |
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Romero-Mercado, Caleb D.b47285bd-6c45-4797-bbf7-8e88b0cd4198Contreraz-Ortiz, Sonia H.377948db-f010-4cc5-ae11-973b8e035474Marrugo, Andres G.3d6cd388-d48f-4669-934f-49ca4179f5422023-07-19T12:57:15Z2023-07-19T12:57:15Z20222023Romero-Mercado, C. D., Contreras-Ortiz, S. H., & Marrugo, A. G. (2022, November). Effect of Speckle Filtering in the Performance of Segmentation of Ultrasound Images Using CNNs. In Workshop on Engineering Applications (pp. 150-159). Cham: Springer Nature Switzerland.https://hdl.handle.net/20.500.12585/1216110.1007/978-3-031-20611-5_13Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarThe convolutional neural networks (CNNs) as tools for ultrasound image segmentation often have their performance affected by the low signal-to-noise ratio of the images. This prevents a correct classification and extraction of relevant information and therefore affects clinical diagnosis. We propose a study of the effect of different speckle filtering methods on CNN performance. For the proposed metrics (Jaccard coefficient and BF-Score), it was obtained that the SRAD filter exhibited the best behavior even in the lowest quality data. In addition, the lowest values were obtained for the standard deviation and variance, which translates into lower data dispersion, better repeatability, and, therefore, greater confidence in its accuracy. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.9 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Communications in Computer and Information ScienceEffect of Speckle Filtering in the Performance of Segmentation of Ultrasound Images Using CNNsinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/drafthttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/resource_type/c_2df8fbb1Photoacoustic Tomography;Thermoacoustics;EchographyLEMBCartagena de IndiasBhatti, U.A., Yu, Z., Chanussot, J., Zeeshan, Z., Yuan, L., Luo, W., Nawaz, S.A., (...), Mehmood, A. Local Similarity-Based Spatial–Spectral Fusion Hyperspectral Image Classification With Deep CNN and Gabor Filtering (2022) IEEE Transactions on Geoscience and Remote Sensing, 60. Cited 81 times. https://ieeexplore.ieee.org/servlet/opac?punumber=36 doi: 10.1109/TGRS.2021.3090410Byra Reddy, G.R., Prasanna Kumar, H. Breast Ultrasound Image Segmentation to Detect Tumor by Using Level Sets (2022) Lecture Notes in Networks and Systems, 355, pp. 319-325. springer.com/series/15179 ISBN: 978-981168511-8 doi: 10.1007/978-981-16-8512-5_35Cesur, E., Yildiz, N., Tavsanoglu, V. On an improved FPGA implementation of CNN-based Gabor-type filters (2012) IEEE Transactions on Circuits and Systems II: Express Briefs, 59 (11), art. no. 6341057, pp. 815-819. Cited 17 times. http://www.ieee-cas.org doi: 10.1109/TCSII.2012.2218471Chen, M., Yu, L., Zhi, C., Sun, R., Zhu, S., Gao, Z., Ke, Z., (...), Zhang, Y. Improved faster R-CNN for fabric defect detection based on Gabor filter with Genetic Algorithm optimization (2022) Computers in Industry, 134, art. no. 103551. Cited 37 times. https://www.journals.elsevier.com/computers-in-industry doi: 10.1016/j.compind.2021.103551Gómez-Flores, W., Coelho de Albuquerque Pereira, W. A comparative study of pre-trained convolutional neural networks for semantic segmentation of breast tumors in ultrasound (Open Access) (2020) Computers in Biology and Medicine, 126, art. no. 104036. Cited 34 times. www.elsevier.com/locate/compbiomed doi: 10.1016/j.compbiomed.2020.104036Jeon, M., Kim, C. Multimodal photoacoustic tomography (2013) IEEE Transactions on Multimedia, 15 (5), art. no. 6425487, pp. 975-982. Cited 66 times. doi: 10.1109/TMM.2013.2244203Kim, J., Lee, D., Jung, U., Kim, C. Photoacoustic imaging platforms for multimodal imaging (2015) Ultrasonography, 34 (2), pp. 88-97. Cited 93 times. http://e-ultrasonography.org/upload/usg-14062.pdf doi: 10.14366/usg.14062Lavreniuk, M., Shelestov, A., Kussul, N., Rubel, O., Lukin, V., Egiazarian, K. Use of modified BM3D filter and CNN classifier for SAR data to improve crop classification accuracy (2019) 2019 IEEE 2nd Ukraine Conference on Electrical and Computer Engineering, UKRCON 2019 - Proceedings, art. no. 8879805, pp. 1071-1076. Cited 3 times. http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8869682 ISBN: 978-172813882-4 doi: 10.1109/UKRCON.2019.8879805Lu, L., Liang, Y., Xiao, Q., Yan, S. Evaluating fast algorithms for convolutional neural networks on FPGAs (Open Access) (2017) Proceedings - IEEE 25th Annual International Symposium on Field-Programmable Custom Computing Machines, FCCM 2017, art. no. 7966660, pp. 101-108. Cited 189 times. ISBN: 978-153864036-4 doi: 10.1109/FCCM.2017.64Meza, J., Contreras-Ortiz, S.H., Romero, L.A., Marrugo, A.G. Three-dimensional multimodal medical imaging system based on freehand ultrasound and structured light (Open Access) (2021) Optical Engineering, 60 (5), art. no. 054106. Cited 7 times. http://www.spie.org/x867.xml doi: 10.1117/1.OE.60.5.054106Meza, J., Romero, L.A., Marrugo, A.G. MarkerPose: Robust real-time planar target tracking for accurate stereo pose estimation (2021) IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 1282-1290. Cited 2 times. http://ieeexplore.ieee.org/xpl/conferences.jsp ISBN: 978-166544899-4 doi: 10.1109/CVPRW53098.2021.00141Pei, S., Cong, S., Zhang, B., Liang, C., Zhang, L., Liu, J., Guo, Y., (...), Zhang, S. Diagnostic value of multimodal ultrasound imaging in differentiating benign and malignant TI-RADS category 4 nodules (2019) International Journal of Clinical Oncology, 24 (6), pp. 632-639. Cited 16 times. link.springer.de/link/service/journals/10147/index.htm doi: 10.1007/s10147-019-01397-yRouhi, R., Jafari, M. Classification of benign and malignant breast tumors based on hybrid level set segmentation (2016) Expert Systems with Applications, 46, pp. 45-59. Cited 54 times. doi: 10.1016/j.eswa.2015.10.011Rouhi, R., Jafari, M., Kasaei, S., Keshavarzian, P. Benign and malignant breast tumors classification based on region growing and CNN segmentation (Open Access) (2015) Expert Systems with Applications, 42 (3), pp. 990-1002. Cited 304 times. doi: 10.1016/j.eswa.2014.09.020Sharifrazi, D., Alizadehsani, R., Roshanzamir, M., Joloudari, J.H., Shoeibi, A., Jafari, M., Hussain, S., (...), Acharya, U.R. Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images (Open Access) (2021) Biomedical Signal Processing and Control, 68, art. no. 102622. Cited 89 times. http://www.elsevier.com/wps/find/journalbibliographicinfo.cws_home/706718/description#bibliographicinfo doi: 10.1016/j.bspc.2021.102622Tripathi, P., Dass, R., Sen, J. A Comparative Analysis of Different Despeckling Filters Using Breast Ultrasonographic Images (2022) Lecture Notes in Electrical Engineering, 841, pp. 425-430. Cited 2 times. http://www.springer.com/series/7818 ISBN: 978-981168773-0 doi: 10.1007/978-981-16-8774-7_34Xie, X., Shi, F., Niu, J., Tang, X. Breast ultrasound image classification and segmentation using convolutional neural networks (2018) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11166 LNCS, pp. 200-211. Cited 28 times. https://www.springer.com/series/558 ISBN: 978-303000763-8 doi: 10.1007/978-3-030-00764-5_19Zhao, C., Wang, Q., Tao, X., Wang, M., Yu, C., Liu, S., Li, M., (...), Yang, M. 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