|تعداد مشاهده مقاله||2,245,251|
|تعداد دریافت فایل اصل مقاله||1,598,034|
|Journal of Electrical and Computer Engineering Innovations (JECEI)|
|دوره 9، شماره 1، فروردین 2021، صفحه 37-46 اصل مقاله (878.9 K)|
|نوع مقاله: Original Research Paper|
|شناسه دیجیتال (DOI): 10.22061/jecei.2020.7404.390|
|M. Taheri 1؛ M. Rastgarpour 2؛ A. Koochari 1|
|1Department of Computer Engineering, Faculty of Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran.|
|2Department of Computer Engineering, Faculty of Engineering, Islamic Azad University, Saveh Branch, Saveh, Iran.|
|تاریخ دریافت: 21 اردیبهشت 1399، تاریخ بازنگری: 18 شهریور 1399، تاریخ پذیرش: 22 آبان 1399|
|Background and Objectives: medical image Segmentation is a challenging task due to low contrast between Region of Interest and other textures, hair artifacts in dermoscopic medical images, illumination variations in images like Chest-Xray and various imaging acquisition conditions.|
Methods: In this paper, we have utilized a novel method based on Convolutional Neural Networks (CNN) for medical image Segmentation and finally, compared our results with two famous architectures, include U-net and FCN neural networks. For loss functions, we have utilized both Jaccard distance and Binary-crossentropy and the optimization algorithm that has used in this method is SGD+Nestrov algorithm. In this method, we have used two preprocessing include resizing image’s dimensions for increasing the speed of our process and Image augmentation for improving the results of our network. Finally, we have implemented threshold technique as postprocessing on the outputs of neural network to improve the contrast of images. We have implemented our model on the famous publicly, PH2 Database, toward Melanoma lesion segmentation and chest Xray images because as we have mentioned, these two types of medical images contain hair artifacts and illumination variations and we are going to show the robustness of our method for segmenting these images and compare it with the other methods.
Results: Experimental results showed that this method could outperformed two other famous architectures, include Unet and FCN convolutional neural networks. Additionally, we could improve the performance metrics that have used in dermoscopic and Chest-Xray segmentation which used before.
Conclusion: In this work, we have proposed an encoder-decoder framework based on deep convolutional neural networks for medical image segmentation on dermoscopic and Chest-Xray medical images. Two techniques of image augmentation, image rotation and horizontal flipping on the training dataset are performed before feeding it to the network for training. The predictions produced from the model on test images were postprocessed using the threshold technique to remove the blurry boundaries around the predicted lesions.
|Deep Learning؛ Convolutional Neural Networks؛ Medical Image Segmentation؛ Image Processing؛ Computer Vision|
 J.U. Kim, H.G. Kim, Y.M. Ro, “Iterative deep convolutional encoder-decoder network for medical image segmentation,” presented at the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Seogwipo, South Korea, 2017.
 Y. Chang et al., “Automatic segmentation and cardiopathy classification in cardiac MRI images based on deep neural networks,” presented at the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada, 2018.
 P. Moeskops et al., “Deep learning for multi-task medical image segmentation in multiple modalities,” in Proc. International Conference on Medical Image Computing and Computer-Assisted Intervention,: 468-486, 2016.
 D. Abdelhafiz et al., “Convolutional neural network for automated mass segmentation in mammography,” presented at the 2018 IEEE 8th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), Las Vegas, NV, USA, 2018.
 H. Fu et al., “Retinal vessel segmentation via deep learning network and fully-connected conditional random fields,” Presented at the 2016 IEEE 13th international symposium on biomedical imaging (ISBI), Prague, Czech Republic, 2016.
 T. Mendonça et al., “PH 2-A dermoscopic image database for research and benchmarking,” presented at the 2013 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC), Osaka, Japan 2013.
 S. Ioffe, C. Szegedy, "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,” in Proc. the 32nd International Conference on Machine Learning, PMLR, 37:448-456, 2015.
 M.H. Jafari, E. Nasr-Esfahani, N. Karimi, S. Soroushmehr, S. Samavi, K. Najarian, “Extraction of skin lesions from nondermoscopic images using deep learning,” arXiv preprint arXiv: 1609.02374, 2016.
 M. Avendi, A. Kheradvar, H. Jafarkhani, “A combined deeplearning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI,” Med. Image Anal., 30: 108–119, 2016.
 J. Carvalho Souza, J.O.B. Diniz, J.L. Ferreira, G.L. Franc¸a da Silva, A. Corr´ea Silva, A. Cardoso de Paiva, “An automatic method for lung segmentation and reconstruction in chest X-Ray using deep neural networks,” Comput. Methods Programs Biomed., 177: 285-296, 2019.
 D. Gutman, N.C.F. Codella, E. Celebi, B. Helba, M. Marchetti, N. Mishra, A. Halpern, "skin lesion analysis toward melanoma detection: A challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)," presented at the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI), Washington, DC, USA, 2018.
 D. Gutman, N.C.F. Codella, E. Celebi, B. Helba, M. Marchetti, N. Mishra, A. Halpern, "Skin lesion analysis toward melanoma detection: A challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)," May 2016.
تعداد مشاهده مقاله: 907
تعداد دریافت فایل اصل مقاله: 494