1. Home
  2. Vol 7, No 2 (2023)
  3. Surono

New Approach to Image Segmentation: U-Net Convolutional Network for Multiresolution CT Image Lung Segmentation

Sugiyarto Surono, Muhammad Rivaldi, Deshinta Arrova Dewi, Nursyiya Irsalinda

Abstract


Image processing is the main topic of discussion in the field of computer vision technology. With the increase in the number of images used over time, the types of images with different resolution qualities are becoming more diverse. Low image resolution leads to uncertainty in the task of image processing. Therefore, a method with high performance is needed for image processing. In image processing, there is a Convolutional Neural Networks (CNN) architecture for semantic segmentation of pixels called U-Net. U-Net is formed by an encoder network and decoder network that will later produce segmented images. In this paper, researchers applied the U-Net architecture to the lung CT image dataset, which has different resolutions in each image, to segment the image that produces a segmented lung image. In this study, we conducted experiments for many training and testing data ratios while also comparing the model performances between the single resolution dataset and the multiresolution dataset. The results showed that the segmentation accuracy using a single resolution dataset is as follows: 5 to 5 ratio is 66.00%, 8 to 2 ratio is 88.96%, and 9 to 1 ratio is 94.47%. For the multiresolution dataset, the application is: 5 to 5 ratio is 82.42%, 8 to 2 ratio is 90.12%, and 9 to 1 ratio is 93.66%. And for the result, the training time using single resolution dataset are: 5 to 5 ratio is 59.94 seconds, 8 to 2 ratio is 87.16 seconds, and 9 to 1 ratio is 195.34 seconds, as for multiresolution data application are: 5 to 5 ratio is 49.60 seconds, 8 to 2 ratio is 102.08 seconds, and 9 to 1 ratio is 199.79 seconds. Based on those results, we obtained the best accuracy for single resolution at a 9:1 ratio and the best training time for multiresolution at a 5:5 ratio.

 

Doi: 10.28991/ESJ-2023-07-02-014

Full Text: PDF


Keywords


U-Net; Image Segmentation; Multiresolution Image; Convolutional Neural Networks (CNN); Resolution Image.

References


Voulodimos, A., Doulamis, N., Doulamis, A., & Protopapadakis, E. (2018). Deep Learning for Computer Vision: A Brief Review. Computational Intelligence and Neuroscience, 7068349, 1-14. doi:10.1155/2018/7068349.

Soydaner, D. (2020). A Comparison of Optimization Algorithms for Deep Learning. International Journal of Pattern Recognition and Artificial Intelligence, 34(13), 1–26. doi:10.1142/S0218001420520138.

Albawi, S., Mohammed, T. A., & Al-Zawi, S. (2018). Understanding of a convolutional neural network. Proceedings of 2017 International Conference on Engineering and Technology, ICET 2017, 1–6. doi:10.1109/ICEngTechnol.2017.8308186.

Khan, A., Sohail, A., Zahoora, U., & Qureshi, A. S. (2020). A survey of the recent architectures of deep convolutional neural networks. Artificial Intelligence Review, 53(8), 5455–5516. doi:10.1007/s10462-020-09825-6.

Hao, S., Zhou, Y., & Guo, Y. (2020). A Brief Survey on Semantic Segmentation with Deep Learning. Neurocomputing, 406, 302–321. doi:10.1016/j.neucom.2019.11.118.

Salehi, S.S.M., Erdogmus, D., Gholipour, A. (2017). Tversky Loss Function for Image Segmentation Using 3D Fully Convolutional Deep Networks. Machine Learning in Medical Imaging. MLMI 2017, Lecture Notes in Computer Science, 10541, Springer, Cham, Switzerland. doi:10.1007/978-3-319-67389-9_44.

Huang, H., Lin, L., Tong, R., Hu, H., Zhang, Q., Iwamoto, Y., Han, X., Chen, Y.-W., & Wu, J. (2020). UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). doi:10.1109/icassp40776.2020.9053405.

Ronneberger, O., Fischer, P., Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015, Lecture Notes in Computer Science, 9351. Springer, Cham, Switzerland. doi:10.1007/978-3-319-24574-4_28.

Isensee, F., Petersen, J., Klein, A., Zimmerer, D., Jaeger, P. F., Kohl, S., Wasserthal, J., Koehler, G., Norajitra, T., Wirkert, S., & Maier-Hein, K. H. (2019). Abstract: nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation. Bildverarbeitung Für Die Medizin 2019, 22–22, Springer Vieweg, Wiesbaden, Germany. doi:10.1007/978-3-658-25326-4_7.

Yalçın, S., & Vural, H. (2022). Brain stroke classification and segmentation using encoder-decoder based deep convolutional neural networks. Computers in Biology and Medicine, 105941, 149. doi:10.1016/j.compbiomed.2022.105941.

Nair, R. R., & Singh, T. (2019). Multi-sensor medical image fusion using pyramid-based DWT: A multi-resolution approach. IET Image Processing, 13(9), 1447–1459. doi:10.1049/iet-ipr.2018.6556.

Sultana, F., Sufian, A., & Dutta, P. (2020). Evolution of Image Segmentation using Deep Convolutional Neural Network: A Survey. Knowledge-Based Systems, 201–202. doi:10.1016/j.knosys.2020.106062.

Singh, S., Mittal, N., & Singh, H. (2022). Multifocus Image Fusion Based on Multiresolution Pyramid and Bilateral Filter. IETE Journal of Research, 68(4), 2476–2487. doi:10.1080/03772063.2019.1711205.

Peryanto, A., Yudhana, A., & Umar, R. (2020). Klasifikasi Citra Menggunakan Convolutional Neural Network DAN K Fold Cross Validation. Journal of Applied Informatics and Computing, 4(1), 45–51. doi:10.30871/jaic.v4i1.2017.

Duarte, D., Nex, F., Kerle, N., & Vosselman, G. (2018). Multi-resolution feature fusion for image classification of building damages with convolutional neural networks. Remote Sensing, 10(10). doi:10.3390/rs10101636.

Minaee, S., Boykov, Y., Porikli, F., Plaza, A., Kehtarnavaz, N., & Terzopoulos, D. (2022). Image Segmentation Using Deep Learning: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(7), 3523–3542. doi:10.1109/TPAMI.2021.3059968.

Hesamian, M. H., Jia, W., He, X., & Kennedy, P. (2019). Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges. Journal of Digital Imaging, 32(4), 582–596. doi:10.1007/s10278-019-00227-x.

Lyu, H., Fu, H., Hu, X., & Liu, L. (2019). Esnet: Edge-Based Segmentation Network for Real-Time Semantic Segmentation in Traffic Scenes. IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan. doi:10.1109/icip.2019.8803132.

Liu, Y., Chu, L., Chen, G., Wu, Z., Chen, Z., Lai, B., & Hao, Y. (2021). Paddleseg: A high-efficient development toolkit for image segmentation. arXiv preprint, arXiv:2101.06175. doi:10.48550/arXiv.2101.06175.

Ko, T. Y., & Lee, S. H. (2020). Novel method of semantic segmentation applicable to augmented reality. Sensors (Switzerland), 20(6), 1737. doi:10.3390/s20061737.

Ghosh, S., Das, N., Das, I., & Maulik, U. (2019). Understanding deep learning techniques for image segmentation. ACM Computing Surveys, 52(4), 1-35. doi:10.1145/3329784.

Soomro, T. A., Afifi, A. J., Gao, J., Hellwich, O., Zheng, L., & Paul, M. (2019). Strided fully convolutional neural network for boosting the sensitivity of retinal blood vessels segmentation. Expert Systems with Applications, 134, 36–52. doi:10.1016/j.eswa.2019.05.029.

Zheng, M., Li, T., Zhu, R., Tang, Y., Tang, M., Lin, L., & Ma, Z. (2020). Conditional Wasserstein generative adversarial network-gradient penalty-based approach to alleviating imbalanced data classification. Information Sciences, 512, 1009–1023. doi:10.1016/j.ins.2019.10.014.

Phaladisailoed, T., & Numnonda, T. (2018). Machine Learning Models Comparison for Bitcoin Price Prediction. 2018 10th International Conference on Information Technology and Electrical Engineering (ICITEE). doi:10.1109/iciteed.2018.8534911.

Ting, F. F., Tan, Y. J., & Sim, K. S. (2019). Convolutional neural network improvement for breast cancer classification. Expert Systems with Applications, 120, 103–115. doi:10.1016/j.eswa.2018.11.008.

Yao, P., Wu, H., Gao, B., Tang, J., Zhang, Q., Zhang, W., Yang, J. J., & Qian, H. (2020). Fully hardware-implemented memristor convolutional neural network. Nature, 577(7792), 641–646. doi:10.1038/s41586-020-1942-4.

Michelucci, U. (2019). Advanced applied deep learning: Convolutional neural networks and object detection. Springer, Berkeley, United States. doi:10.1007/978-1-4842-4976-5.

Singh, H. (2019). Practical Machine Learning and Image Processing. Springer, Berkeley, United States. doi:10.1007/978-1-4842-4149-3.

Charniak, E. (2018). Intro to Deep Learning. MIT Press, Cambridge, Massachusetts, United States.

Wang, Y., Li, Y., Song, Y., & Rong, X. (2020). The influence of the activation function in a convolution neural network model of facial expression recognition. Applied Sciences (Switzerland), 10(5), 1897. doi:10.3390/app10051897.

Jadon, S. (2020). A survey of loss functions for semantic segmentation. 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). doi:10.1109/cibcb48159.2020.9277638.

Soomro, T. A., Afifi, A. J., Gao, J., Hellwich, O., Paul, M., & Zheng, L. (2018). Strided U-Net Model: Retinal Vessels Segmentation using Dice Loss. IEEE Digital Image Computing: Techniques and Applications (DICTA), Canberra, ACT, Australia. doi:10.1109/dicta.2018.8615770.

Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., Torr, P. H. S., & Zhang, L. (2021). Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/cvpr46437.2021.00681.

Xiao, Z., Liu, B., Geng, L., Zhang, F., & Liu, Y. (2020). Segmentation of Lung Nodules Using Improved 3D-UNet Neural Network. Symmetry, 12(11), 1787. doi:10.3390/sym12111787.


Full Text: PDF

DOI: 10.28991/ESJ-2023-07-02-014

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Sugiyarto Surono, Muhammad Rivaldi, DESHINTA ARROVA DEWI, Nursyiya Irsalinda