Minh-Trieu Tran
ABSTRACT
In recent years, chest X-ray images have been progressively applied in research studies. Inspired by the recent success of applying deep learning-based approaches to medical image processing, we first propose an architecture for inpainting on chest X-ray images. A system based on deep convolutional neural networks for completion of the missing or distorted areas using the chest X-ray image was designed and implemented in this paper. Our network was trained with chest X-ray images and shows promising results compared to other networks. Through qualitative and quantitative comparisons with other image inpainting methods, the experimental results have proven our method achieved very good performance when compared with other methods. The average PSNR and SSIM values on the test set for the proposed model were 39.51 dB and 0.79 respectively.
- Mashail Alsalamah and Saad Amin. 2016. Medical image inpainting with RBF interpolation technique. International Journal of Advanced Computer Science and Applications 7, 8 (2016), 91–99.Google Scholar
Cross Ref
- Karim Armanious, Vijeth Kumar, Sherif Abdulatif, Tobias Hepp, Sergios Gatidis, and Bin Yang. 2019. ipA-MedGAN: Inpainting of Arbitrary Regions in Medical Imaging. arXiv (2019), arXiv–1910.Google Scholar
- Karim Armanious, Youssef Mecky, Sergios Gatidis, and Bin Yang. 2019. Adversarial inpainting of medical image modalities. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 3267–3271.Google ScholarCross Ref
- Mirko Arnold, Anarta Ghosh, Stefan Ameling, and Gerard Lacey. 2010. Automatic segmentation and inpainting of specular highlights for endoscopic imaging. EURASIP Journal on Image and Video Processing 2010 (2010), 1–12. Google ScholarDigital Library
- Djork-Arné Clevert, Thomas Unterthiner, and Sepp Hochreiter. 2015. Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289 (2015).Google Scholar
- Zhilin Feng, Shuiming Chi, Jianwei Yin, Duanyang Zhao, and Xiaoming Liu. 2007. A variational approach to medical image inpainting based on mumfordshah model. In 2007 International Conference on Service Systems and Service Management. IEEE, 1–5.Google ScholarCross Ref
- Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in neural information processing systems. 2672–2680. Google ScholarDigital Library
- Nicolas Guizard, Kunio Nakamura, Pierrick Coupé, Vladimir S Fonov, Douglas L Arnold, and D Louis Collins. 2015. Non-local means inpainting of MS lesions in longitudinal image processing. Frontiers in neuroscience 9 (2015), 456.Google Scholar
- Jia-Bin Huang, Sing Bing Kang, Narendra Ahuja, and Johannes Kopf. 2014. Image completion using planar structure guidance. ACM Transactions on graphics (TOG) 33, 4 (2014), 1–10. Google ScholarDigital Library
- Olivier Le Meur, Josselin Gautier, and Christine Guillemot. 2011. Examplar-based inpainting based on local geometry. In 2011 18th IEEE international conference on image processing. IEEE, 3401–3404.Google ScholarCross Ref
- Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros. 2016. Context encoders: Feature learning by inpainting. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2536–2544.Google ScholarCross Ref
- Yael Pritch, Eitam Kav-Venaki, and Shmuel Peleg. 2009. Shift-map image editing. In 2009 IEEE 12th International Conference on Computer Vision. IEEE, 151–158.Google Scholar
- Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention. Springer, 234–241.Google ScholarCross Ref
- Ecem Sogancioglu, Shi Hu, Davide Belli, and Bram van Ginneken. 2018. Chest X-ray Inpainting with Deep Generative Models. arXiv preprint arXiv:1809.01471 (2018).Google Scholar
- Minh-Trieu Tran and Guee-Sang Lee. 2019. Super-resolution in Music Score Images by Instance Normalization. Smart Media Journal 8, 4 (2019), 64–71.Google Scholar
- Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky. 2016. Instance normalization: The missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022 (2016).Google Scholar
- Pavel Vlašánek. 2018. Fuzzy image inpainting aimed to medical images. (2018).Google Scholar
- Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M Summers. 2017. Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2097–2106.Google ScholarCross Ref
- Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. 2004. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13, 4 (2004), 600–612. Google ScholarDigital Library
- Chaohao Xie, Shaohui Liu, Chao Li, Ming-Ming Cheng, Wangmeng Zuo, Xiao Liu, Shilei Wen, and Errui Ding. 2019. Image inpainting with learnable bidirectional attention maps. In Proceedings of the IEEE International Conference on Computer Vision. 8858–8867.Google ScholarCross Ref
- Zongben Xu and Jian Sun. 2010. Image inpainting by patch propagation using patch sparsity. IEEE transactions on image processing 19, 5 (2010), 1153–1165. Google ScholarDigital Library
- Zhaoyi Yan, Xiaoming Li, Mu Li, Wangmeng Zuo, and Shiguang Shan. 2018. Shift-net: Image inpainting via deep feature rearrangement. In Proceedings of the European conference on computer vision (ECCV). 1–17.Google ScholarDigital Library
- Kashan Zafar, Syed Omer Gilani, Asim Waris, Ali Ahmed, Mohsin Jamil, Muhammad Nasir Khan, and Amer Sohail Kashif. 2020. Skin Lesion Segmentation from Dermoscopic Images Using Convolutional Neural Network. Sensors 20, 6 (2020), 1601.Google ScholarCross Ref
Index Terms
- Deep Learning-Based Inpainting for Chest X-ray Image
Recommendations
Vertebrae Segmentation from X-ray Images Using Convolutional Neural Network
IHIP 2018: Proceedings of the 2018 International Conference on Information Hiding and Image ProcessingNOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the IHIP 2018 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published ...
Image inpainting based on deep learning: A review
AbstractImage inpainting is an important research direction in the study of computer vision, and is widely used in image editing and photo inpainting etc. Traditional image inpainting algorithms are often difficult to deal with large-scale ...
Highlights- Reviewing the image inpainting on deep learning of the past 15 years.
- ...
Improving lung region segmentation accuracy in chest X-ray images using a two-model deep learning ensemble approach
Highlights- Intensive bacterial/viral infection reduces lung segmentation accuracy in CXR films.
AbstractWe propose a deep learning framework to improve segmentation accuracy of the lung region in Chest X-Ray (CXR) images. The proposed methodology implements a “divide and conquer” strategy where the original CXRs are subdivided into ...
Comments