Skull Stripping Based on the Segmentation Models
محتوى المقالة الرئيسي
الملخص
Skull image separation is one of the initial procedures used to detect brain abnormalities. In an MRI image of the brain, this process involves distinguishing the tissue that makes up the brain from the tissue that does not make up the brain. Even for experienced radiologists, separating the brain from the skull is a difficult task, and the accuracy of the results can vary quite a little from one individual to the next. Therefore, skull stripping in brain magnetic resonance volume has become increasingly popular due to the requirement for a dependable, accurate, and thorough method for processing brain datasets. Furthermore, skull stripping must be performed accurately for neuroimaging diagnostic systems since neither non-brain tissues nor the removal of brain sections can be addressed in the subsequent steps, resulting in an unfixed mistake during further analysis. Therefore, accurate skull stripping is necessary for neuroimaging diagnostic systems. This paper proposes a system based on deep learning and Image processing, an innovative method for converting a pre-trained model into another type of pre-trainer using pre-processing operations and the CLAHE filter as a critical phase. The global IBSR data set was used as a test and training set. For the system's efficacy, work was performed based on the principle of three dimensions and three sections of MR images and two-dimensional images, and the results were 99.9% accurate.
تفاصيل المقالة
القسم
كيفية الاقتباس
المراجع
Al-Juboori, R.A.L., 2017. Contrast enhancement of the mammographic image using retinex with CLAHE methods. Iraqi Journal of Science, pp. 327-336.
Al-Majeed, S.A., and Al-Tamimi, M.S., 2020. Survey based study: classification of patients with alzheimer’s disease. Iraqi Journal of Science, pp. 3104-3126. Doi:10.24996/ijs.2020.61.11.31.
Al-Tamimi, M.S.H. and Sulong, G., 2014. A review of snake models in medical MR image segmentation. Jurnal Teknologi, 2(1), pp.101-106. Doi:10.11113/jt. v69.3116.
Al-Tamimi, M.S., Ghazali, N.H., Mahrom, N., Ghazali, N., and Sulong, G., 2018. Brain tumour detection using fine-tuning mechanism for magnetic resonance imaging. In MATEC Web of Conferences (Vol. 150, P.06025). EDP Sciences. Doi:10.1051/matecconf/201815006025.
Atkins, M.S., Siu, K., Law, B., Orchard, J.J., and Rosenbaum, W.L., 2002, May. Difficulties of T1 brain MRI segmentation techniques. In Medical Imaging 2002: Image Processing, 4684, pp. 1837-1844. SPIE. Doi:10.1117/12.467158
Azam, H., Tariq, H., Shehzad, D., Akbar, S., Shah, H., and Khan, Z.A., 2023. Fully Automated Skull Stripping from Brain Magnetic Resonance Images Using Mask RCNN-Based Deep Learning Neural Networks. Brain Sciences, 13(9), P. 1255. Doi:10.3390/brainsci13091255
Babu, K., Indira, N., Prasad, K.V., and Shameem, S., 2021. An effective brain tumor detection from t1w MR images using active contour segmentation techniques. In Journal of Physics: Conference Series (Vol. 1804, No. 1, p. 012174). IOP Publishing. Doi:10.1088/1742-6596/1804/1/012174
Chang, Y., Jung, C., Ke, P., Song, H., and Hwang, J., 2018. Automatic contrast-limited adaptive histogram equalization with dual gamma correction. IEEE Access, 6, pp. 11782-11792. Doi:10.1109/ACCESS.2018.2797872.
Chen, K., Shen, J., and Scalzo, F., 2018. Skull stripping using confidence segmentation convolution neural network. In Advances in Visual Computing: 13th International Symposium, ISVC 2018, Las Vegas, NV, USA, November 19–21, Proceedings 13, pp. 15-24. Springer International Publishing. Doi:10.1007/978-3-030-03801-4_2.
Da Silva, R.D.C., Jenkyn, T.R., and Carranza, V.A., 2022. Enhanced pre-processing for deep learning in MRI whole brain segmentation using orthogonal moments. Brain Multiphysics, 3, P.100049. Doi: 10.1016/j.brain.2022.100049.
De Oliveira, M., Piacenti-Silva, M., da Rocha, F.C.G., Santos, J.M., Cardoso, J.D.S., and Lisboa-Filho, P.N., 2022. Lesion Volume Quantification Using Two Convolutional Neural Networks in MRIs of Multiple Sclerosis Patients. Diagnostics, 12(2), P. 230. Doi:10.3390/diagnostics12020230.
Dey, R. and Hong, Y., 2018. CompNet: Complementary segmentation network for brain MRI extraction. In Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part III 11, pp. 628-636. Springer International Publishing.
Fatima, A., Shahid, A.R., Raza, B., Madni, T.M. and Janjua, U.I., 2020. State-of-the-art traditional to the machine and deep-learning-based skull stripping techniques, models, and algorithms. Journal of Digital Imaging, 33, pp.1443-1464. Doi: 10.1007/s10278-020-00367-5.
Gao, Y., Li, J., Xu, H., Wang, M., Liu, C., Cheng, Y., Li, M., Yang, J., and Li, X., 2019. A multi-view pyramid network for skull stripping on neonatal T1-weighted MRI. Magnetic resonance imaging, 63, pp. 70–79. Doi:10.1016/j.mri.2019.08.025
Goodfellow, I., Bengio, Y., and Courville, A., 2016. Deep learning. MIT Press.
Ghadi, N.M., and Salman, N.H., 2022. Deep Learning-Based Segmentation and Classification Techniques for Brain Tumor MRI: A Review. Journal of Engineering, 28(12), pp. 93-112. Doi:10.31026/j.eng.2022.12.07.
Hazra, D. and Byun, Y., 2020. Brain tumor detection using skull stripping and U-net architecture. International Journal of Machine Learning and Computing, 10(2), pp.400-405. Doi:10.18178/ijmlc.2020.10.2.949
Hwang, H., Rehman, H.Z.U., and Lee, S., 2019. 3D U-Net for skull stripping in brain MRI. Applied Sciences, 9(3), P.569. Doi:10.3390/app9030569.
Joodi, M. A., Saleh, M. H. and Khadhim, D. J., 2023. Proposed Face Detection Classification Model Based on Amazon Web Services Cloud (AWS), Journal of Engineering, 29(4), pp. 176–206. Doi:10.31026/j.eng.2023.04.12
Kalavathi, P., and Prasath, V.S., 2016. Methods on skull stripping of MRI head scan images—a review. Journal of digital imaging, 29(3), pp.365-379. Doi:10.1007/s10278-015-9847-8 .
Li, Y., Dan, R., Wang, S., Cao, Y., Luo, X., Tan, C., Jia, G., Zhou, H., Wang, Y., and Wang, L., 2022. Source-free domain adaptation for multi-site and lifespan brain skull stripping. arXiv preprint arXiv:2203.04299. Doi:10.48550/arXiv.2203.04299
Moazami, S., Ray, D., Pelletier, D., and Oberai, A.A., 2022. Probabilistic Brain Extraction in MR Images via Conditional Generative Adversarial Networks. bioRxiv, pp. 1-33. Doi:10.1101/2022.03.14.484346
Mahdi, S.S. and Mahmood, R.S., 2014. MR Brain Image Segmentation Using Spatial Fuzzy C-Means Clustering Algorithm. Journal of Engineering, 20(09), pp.78-89. Doi:10.31026/j.eng.2014.09.06.
Musa, P., Al Rafi, F., and Lamsani, M., 2018. A Review: Contrast-Limited Adaptive Histogram Equalization (CLAHE) methods to help the application of face recognition. In 2018 third international conference on informatics and computing (ICIC) (pp. 1-6). IEEE.
Nayyef, R.H. and Al-Tamimi, M.S., 2022. A comparative study and overview on the magnetic resonance images skull stripping methods and their correspondence techniques. International Journal of Nonlinear Analysis and Applications, 13(1), pp.3783-3802. Doi:10.22075/ijnaa.2022.6164
Oudah, N., Esmaile, M. F. and Abdulredaa, E., 2018. Optical Character Recognition Using Active Contour Segmentation, Journal of Engineering, 24(1), pp. 146–158. Doi:10.31026/j.eng.2018.01.10.
Powers, D.M., 2020. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv preprint arXiv:2010.16061.Doi:10.48550/arXiv.2010.16061.
Qi, L.J., Alias, N. and Johar, F., 2020. Detection of brain tumour in 2D MRI: implementation and critical review of clustering-based image segmentation methods. Onkologia i Radioterapia, (2).
Rehman, H.Z.U., Hwang, H., and Lee, S., 2020. Conventional and Deep Learning Methods for Skull Stripping in Brain MRI. Applied Sciences, 10(5), P. 1773. Doi:10.3390/app10051773.
Ronneberger, O., Fischer, P., and Brox, T., 2015. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18 (pp. 234-241). Springer International Publishing. Doi:10.1007/978-3-319-24574-4_28.
Tan, M., and Le, Q., 2019. Efficient net: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, May, PMLR, pp. 6105-6114.
Doi:10.48550/arXiv.1905.11946.
Tariq, H.U.M.E.R.A., Muqeet, A.B.D.U.L., Burney, A.Q.I.L., Hamid, M.A. And Azam, H., 2017. Otsu’s segmentation: review, visualization, and analysis in context of axial brain MR slices. Journal of Theoretical and Applied Information Technology, 95(22), pp.6042-6055.
Unal, B., and Akoglu, A., 2016, August. Resource efficient real-time processing of contrast limited adaptive histogram equalization. In 2016 26th International Conference on Field Programmable Logic and Applications (FPL), pp. 1-8, IEEE. Doi:10.1109/FPL.2016.7577362.
Ullah, Z., Lee, S.H., and An, D., 2020. Histogram Equalization based Enhancement and MR Brain Image Skull Stripping using Mathematical Morphology. International Journal of Advanced Computer Science and Applications (IJACSA), 11(3). Doi:10.14569/IJACSA.2020.0110372.
Wang, J., Sun, Z., Ji, H., Zhang, X., Wang, T., and Shen, Y., 2019. A fast 3D brain extraction and visualization framework using active contour and modern OpenGL pipelines. IEEE Access, 7, pp.156097-156109. Doi:10.1109/ACCESS.2019.2948621.