Recognition of brain stroke shape using multiscale morphological image processing

References

• Mukhopadhyay S, Chanda B. Multiscale morphological segmentation of gray-scale images. IEEE Trans Image Process. 2003;12(5):533–549.
   PubMed Web of Science ®Google Scholar
• Ray A, Bandyopadhyay SK. Automatic detection of ischemic stroke lesion using textural analysis from brain CT images. Euro J Biomed Pharm Sci. 2016;3(10):82–288.
   Google Scholar
• Kumar, D. V., & Krishniah, V. J. R. (2016, October). An automated framework for stroke and hemorrhage detection using decision tree classifier. In 2016 International Conference on Communication and Electronics Systems (ICCES). IEEE. p. 1–6.
   Google Scholar
• Perez Malla CU, Valdes Hernandez MDC, Rachmadi MF, et al. Evaluation of enhanced learning techniques for segmenting ischaemic stroke lesions in brain magnetic resonance perfusion images using a convolutional neural network scheme. Front Neuroinform. 2019;13:33.
   PubMed Web of Science ®Google Scholar
• Rajinikanth V, Dey N. Magnetic resonance imaging: recording, reconstruction and assessment. Chennai: Elsevier; 2022.
   Google Scholar
• Macenko, M., Celenk, M., & Ma, L. (2006, August). Lesion detection using morphological watershed segmentation and model based inverse filtering. In 18th International Conference on Pattern Recognition (ICPR’06) (Vol. 4). IEEE. p. 679–682.
   Google Scholar
• Lee, M. S., Chin, C. L., Lee, Y. W., Lee, C. Y., & Chen, Y. R. (2012, July). Increasing visual perception brain stroke detection system. In 2012 International Conference on Computing, Measurement, Control and Sensor Network. IEEE. p. 429–432.
   Google Scholar
• Kakhandaki N, Kulkarni SB. A novel framework for detection and classification of brain hemorrhage.
   Google Scholar
• Anupama CSS, Sivaram M, Lydia EL, et al. Synergic deep learning model-based automated detection and classification of brain intracranial hemorrhage images in wearable networks. Pers Ubiquitous Comput. 2020;26:1–10.
   Web of Science ®Google Scholar
• Swiebocka-Wiek, J. (2016, September). Skull stripping for MRI images using morphological operators. In IFIP International Conference on Computer Information Systems and Industrial Management. Springer, Cham. p. 172–182.
   Google Scholar
• Sangiorgi S, De Benedictis A, Protasoni M, et al. Early-stage microvascular alterations of a new model of controlled cortical traumatic brain injury: 3D morphological analysis using scanning electron microscopy and corrosion casting. J Neurosurg. 2013;118(4):763–774.
   PubMed Web of Science ®Google Scholar
• Vancleef K, Wagemans J, Humphreys GW. Impaired texture segregation but spared contour integration following damage to right posterior parietal cortex. Exp Brain Res. 2013;230(1):41–57.
   PubMed Web of Science ®Google Scholar
• Vo KD, Santiago F, Lin W, et al. MR imaging enhancement patterns as predictors of hemorrhagic transformation in acute ischemic stroke. Am J Neuroradiol. 2003;24(4):674–679.
   PubMed Web of Science ®Google Scholar
• Nogueira K, Chanussot J, Dalla Mura M, et al. An introduction to deep morphological networks. IEEE Access. 2021;9:114308–114324.
   Google Scholar
• Spellicy S, Scheulin K, Baker EW, et al. Semi-automated cell and tissue analyses reveal regionally specific morphological alterations of immune and neural cells in a porcine middle cerebral artery occlusion model of stroke. Front Cell Neurosci. 2020;14:489.
   Web of Science ®Google Scholar
• Mirza, S., & Gokhale, S.. Neuroimaging in intracerebral hemorrhage. In: Amit Agrawal, editor. Hemorrhagic stroke – an update. Dallas: IntechOpen; 2017. p. 1–80.
   Google Scholar
• Raghavendra U, Gudigar A, Vidhya V, et al. Novel and accurate non-linear index for the automated detection of haemorrhagic brain stroke using CT images. Complex Intelligent Syst. 2021;7(2):929–940.
   Web of Science ®Google Scholar
• Mondal R, Dey MS, Chanda B. Image restoration by learning morphological opening-closing network. Math Morphol Theory Appl. 2020;4(1):87–107.
   Google Scholar
• Karthik R, Menaka R. A multi-scale approach for detection of ischemic stroke from brain MR images using discrete curvelet transformation. Measurement (Mahwah, NJ). 2017;100:223–232.
   Google Scholar
• Davis, V., & Devane, S. (2017, December). Diagnosis & classification of brain hemorrhage. In 2017 International Conference on Advances in Computing, Communication and Control (ICAC3). IEEE. p. 1–6.
   Google Scholar
• Yu, D., Jin, J. S., Luo, S., Lai, W., Park, M., & Pham, T. D. (2010, August). Shape analysis and recognition based on skeleton and morphological structure. In 2010 Seventh International Conference on Computer Graphics, Imaging and Visualization. IEEE. p. 118–123.
   Google Scholar
• Said KAM, Jambek AB, Sulaiman N. A study of image processing using morphological opening and closing processes. Int J Control Theory Appl. 2016;9(31):15–21.
   Google Scholar
• Sang H, Xiang L, Chen S, et al. Image recognition based on multiscale pooling deep convolution neural networks. Complexity. 2020;2020:13.
   Web of Science ®Google Scholar
• Teng L, Li H, Karim S. DMCNN: a deep multiscale convolutional neural network model for medical image segmentation. J Healthcare Eng. 2019;2019:10.
   Web of Science ®Google Scholar
• Muthu, R., Rani, C., Saritha, S., & Pearl Mary, S.. Morphological operations in medical image pre-processing International Conference on Advanced Computing and Communication Systems, 2017. p. 2065–2070.
   Google Scholar
• Shen Y, Zhong X, Shih FY. Deep morphological neural networks. arXiv Preprint ArXiv. 2019;1909:01532.
   Google Scholar
• Mellouli D, Hamdani TM, Sanchez-Medina JJ, et al. Morphological convolutional neural network architecture for digit recognition. IEEE Trans Neural Netw Learn Syst. 2019;30(9):2876–2885.
   PubMed Web of Science ®Google Scholar
• Franchi G, Fehri A, Yao A. Deep morphological networks. Pattern Recognit. 2020;102:107246.
   Web of Science ®Google Scholar
• Zhang L, Li W, Liu C, et al. Automatic fish counting method using image density grading and local regression. Comput Electron Agric. 2020;179:105844.
   Web of Science ®Google Scholar
• Li C, Ma C, Lu WW. Morphological operation residual blocks: enhancing 3D morphological feature representation in convolutional neural networks for semantic segmentation of medical images. arXiv Preprint ArXiv. 2021;2103:04026.
   Google Scholar
• Tursynova AT, Omarov BS, Postolache OA, et al. Convolutional deep learning neural network for stroke image recognition. Вестник КазНУ. Серия математика, механика, информатика. 2021;112(4):110–115.
   Google Scholar
• Liu L, Chen S, Zhang F, et al. Deep convolutional neural network for automatically segmenting acute ischemic stroke lesion in multi-modality MRI. Neural Comput Appl. 2020;32(11):6545–6558.
   Web of Science ®Google Scholar
• Park S, Kim BK, Han MK, et al. (2021). Deep learning for prediction of mechanism in acute ischemic stroke using brain MRI.
   Google Scholar