Enhancing Image Registration Techniques in Medical Imaging Using Machine Learning
Keywords:
Contourlet transform, feature extraction, classifications and segmentation, Adaptive Neuro Fuzzy Inference System (ANFIS) classificationAbstract
This research demonstrates a computer-assisted approach for identifying and segmenting brain tumors, which is based on methods of image registration and classification. The method may be found here. Image registration, transformation using Contourlet, extracting features, feature normalization, feature classification, and feature segmentation are all elements that make up this suggested research. For the purpose of tumor detection and segmentation using brain MRI, we first use a Genetic Algorithm (GA) to optimize the features that have been extracted, and then we use an Adaptive Neuro Fuzzy Inference System's (ANFIS) classification method to classify the features that have been extracted. The method that has been proposed for diagnosing brain cancers is given a quantitative examination in which the method's sensitivity, specificity, segmented precision, precision, accuracy, and Dice similarity coefficient are measured. In addition, the findings of this research provide a strategy for developing a framework for the diagnosis of brain tumors by combining a number of different classification approaches. The clarity of the pictures' low-resolution boundaries may be improved by integrating brain MRI scans taken from a data collection that is freely accessible to the public..
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H. He and Z. Li, "Research on Medical Image Registration Algorithm Based on Double Immunolabeling Technology," 2022 International Conference on Artificial Intelligence and Autonomous Robot Systems (AIARS), Bristol, United Kingdom, 2022, pp. 291-295, doi: 10.1109/AIARS57204.2022.00072
T. Küstner et al., "LAPNet: Non-Rigid Registration Derived in k-Space for Magnetic Resonance Imaging," in IEEE Transactions on Medical Imaging, vol. 40, no. 12, pp. 3686-3697, Dec. 2021, doi: 10.1109/TMI.2021.3096131.
J. -M. Guyader et al., "Groupwise Multichannel Image Registration," in IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 3, pp. 1171-1180, May 2019, doi: 10.1109/JBHI.2018.2844361.
Ahmed Al-Hmouz, Jun Shen, R, Al-Hmouz & Jun Yan, ‘Modeling and Simulation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning’, IEEE Transactions on Learning Technologies, vol. 5, pp. 226-237.
Ahmed, S, Iftekharuddin, KM & Vossough, A 2011, ‘Efficacy of texture, shape, and intensity feature fusion for posterior-fossa tumor segmentation in MRI’, IEEE Transactions on Information Technology in Biomedicine, vol. 15, no. 2, pp. 206-213.
Al-Ansi, A. M. . (2021). Applying Information Technology-Based Knowledge Management (KM) Simulation in the Airline Industry . International Journal of New Practices in Management and Engineering, 10(02), 05–09. https://doi.org/10.17762/ijnpme.v10i02.131
Ain, Q, Jaffar, MA & Choi, TS 2014, ‘Fuzzy anisotropic diffusion based segmentation and texture based ensemble classification of brain tumor’, International Journal of Applied Soft Computing, vol. 21, pp. 330-340.
Alain Pitiot, Arthor W Toga, Nicholas Ayache & Paul Thompson 2002, ‘Texture based MRI segmentation with a two stage hybrid neural classifier’, Proc of IEEE International Joint Conf on Neural Networks, vol. 3, pp. 2053-2058.
Amin, J, Sharif, M & Yasmin, M 2019, ‘A new approach for brain tumor segmentation and classification based on score level fusion using transfer learning’, J Med Syst. vol. 43, P. 326.
Anandgaonkar, G & Sable, G 2014, ‘Brain tumor detection and identification from T1 post contrast MR images using cluster based segmentation’, Int J Sci Res, vol. 3, no. 4, pp. 814-817.
Aslam, A, Khan, E & Beg, MMS 2015, ‘Improved edge detection algorithm for brain tumor segmentation’, Procedia Computer Science, vol. 58, pp. 430-437.
Ayact, R & Amor, NB 2009 ‘Brain tumor segmentation using support vector machines’, Proc. 10th Eur. Conf. (ECSQARU), Berlin, Germany: Springer, vol. 55, no. 90, pp. 736-747
Bandhyopadhyay, SK & Paul, TU 2013, ‘Automatic segmentation of brain tumour from multiple images of brain MRI’, Int J Appl Innovat Eng Manage (IJAIEM), vol. 2, no. 1, pp. 240-248.
Bedi, SS & Rati Khandelwal 2013, ‘Arious image enhancement techniquesA critical review’, International Journal of Advanced Research in Computer and Communication Engineering, vol. 2, no. 3, pp. 1605-1609
Bernal, J, Kushibar, K, Asfaw, DS, Valverde, S Oliver, A, Martí, R & Lladó, X 2018, ‘Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: A review’, Artif. Intell. Med. vol. 95, pp. 64-81
Bhagwat, K, Dhanashri, M & Sayali Shinde 2013, ‘Comparative study of brain tumor detection using K-means, fuzzy C-means and hierarchical clustering algorithms’, International Journal of Scientific and Engineering Research, vol. 4, no. 6, pp. 626-631
Yadav, R. ., & Singh, R. . (2023). A Hyper-parameter Tuning based Novel Model for Prediction of Software Maintainability. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2), 106–113. https://doi.org/10.17762/ijritcc.v11i2.6134
Binaghi, E, Omodei, M, Pedoia, V, Balbi, S, Lattanzi, D & Monti, E 2014, ‘Automatic segmentation of MR brain tumor images using support vector machine in combination with graph cut’, in: IJCCI (NCTA), pp. 152-157..
Demirhan M Törü & Güler, I 2015, ‘Segmentation oftumor and edema along with healthy tissues of brain using wavelets and neural networks’, IEEE J. Biomed. Health Inform., vol. 19, no. 4, pp. 1451-1458
Gauri P Anand Gaonkar & Ganesh S Sable 2013, ‘Detection and identification of brain tumour in brain MR Image using Fuzzy C-Means. Segmentation’, International Journal of Advanced Research in Computer and Communication Engineering, vol. 2 no. 10.
Hamamci, A, Kucuk, N, Karaman, K, Engin, K & Unal, G 2012, ‘Tumorcut: Segmentation of brain tumors on contrast enhanced mr images for radiosurgery applications’, IEEE Trans. Med. Imaging vol. 31, no. 3, pp. 790-804.
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