EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 53 (2023) ITM Web Conf., 53 (2023) 01009 References
Open Access
Issue
ITM Web Conf.
Volume 53, 2023
2nd International Conference on Data Science and Intelligent Applications (ICDSIA-2023)
Article Number 01009
Number of page(s) 15
Section Artificial Intelligence
DOI https://doi.org/10.1051/itmconf/20235301009
Published online 01 June 2023
  1. Md. Mia. Detection of brain stroke from ct scan image. Journal of Dhaka InternationalUniversity, 10(1):42–51 (2018) [Google Scholar]
  2. Anne-Sophie Putzer, Hans Worthmann, Gerrit M. Grosse, Friedrich Goetz, Jens Martens-Lobenhoffer, Meike Dirks, Jan T. Kielstein, Ralf Lichtinghagen, Ulrich Budde, Stefanie M. Bode-Bo¨ger, Karin Weissenborn, and Ramona Schuppner. ADAMTS13 activity is associated with early neurological improvement in acute ischemic stroke patients treated with intravenous thrombolysis. Journal of Thrombosis and Thrombolysis, 49(1):67–74 (2019). [Google Scholar]
  3. Islem Rekik, Ste´phanie Allassonnie`re, Trevor K. Carpenter, and Joanna M. Wardlaw. Medical image analysis methods in MR/CT-imaged acute-subacute ischemic stroke lesion: Segmentation, prediction and insights into dynamic evolution simulation models.a critical appraisal. NeuroImage: Clinical, 1(1):164–178(2012) [Google Scholar]
  4. Muschelli J, Sweeney E., and Crainiceanu C. brainr: Interactive 3 and 4d images of high resolution neuroimage data. The R journal, 6(1):41–48(2014). [CrossRef] [Google Scholar]
  5. Sil C. Van De Leemput, Midas Meijs, Ajay Patel, Frederick J. A. Meijer, Bram Van Ginneken, and Rashindra Manniesing. Multiclass brain tissue segmentation in 4d CT using convolutional neural networks. IEEE Access, 7:51557–51569(2019) [CrossRef] [Google Scholar]
  6. J. A. Gutierrez-Celaya, R. Leder, R. Carrillo, A. Hawayek, J. Hernandez, and E. Sucar. fMRI-based inverse analysis of stroke patients motor functions. In 2011 Pan AmericanHealth Care Exchanges. IEEE, March (2011). [Google Scholar]
  7. R. Peyron, L. Garc´ıa-Larrea, M. C. Gre´goire, P. Convers, A. Richard, F. Lavenne, F. G. Barral, F. Mauguie`re, D. Michel, and B. Laurent. Parietal and cingulate processes incentral pain. a combined positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) study of an unusual case. Pain, 84(1):77–87, January (2000). [CrossRef] [Google Scholar]
  8. Martine T.B. Truijman, Robert M. Kwee, Raf H.M. van Hoof, Evelien Hermeling, Robert J. van Oostenbrugge, Werner H. Mess, Walter H. Backes, Mat J. Daemen, Jan Bucerius, Joachim E. Wildberger, and Marianne Eline Kooi. Combined 18 f-FDG PETCTand DCE-MRI to assess inflammation and microvascularization in atherosclerotic plaques. Stroke, 44(12):3568–3570, December (2013). [CrossRef] [Google Scholar]
  9. Soha Saleh, Sergei V. Adamovich, and Eugene Tunik. Visual feedback discordance mediates changes in brain activity and effective connectivity: A stroke fMRI dynamic causal modeling study. In 2013 2nd International Conference on Advances in Biomedical Engineering. IEEE, September (2013). [Google Scholar]
  10. John A. Onofrey, Lawrence H. Staib, and Xenophon Papademetris. Segmenting the brain surface from CT images with artifacts using locally oriented appearance and dictionary learning. IEEE Transactions on Medical Imaging, 38(2):596–607, February (2019). [CrossRef] [Google Scholar]
  11. Khakon Das, Shankar Prasad Saha, and Kundan Kumar Singh. Detection of epileptiform seizure from pre-ictal part of epileptic EEG recording. In Advances in Systems Analysis, Software Engineering, and High Performance Computing, pages 36–49. IGI Global, (2020). [Google Scholar]
  12. Kyo Noguchi, Toshihide Itoh, Norihito Naruto, Shutaro Takashima, Kortaro Tanaka, and Satoshi Kuroda. A novel imaging technique (x-map) to identify acute ischemic lesions using noncontrast dual-energy computed tomography. Journal of Stroke and Cerebrovascular Diseases, 26(1):34–41, January (2017). [CrossRef] [Google Scholar]
  13. Pramit Ghosh, Debotosh Bhattacharjee, and Mita Nasipuri. Automatic system for plasmodium species identification from microscopic images of blood-smear samples. Journal of Healthcare Informatics Research, 1(2):231–259, November 2017. [CrossRef] [Google Scholar]
  14. P. Ghosh, D. Bhattacharjee, and M. Nasipuri. Intelligent toilet system for non-invasive estimation of blood-sugar level from urine. IRBM, 41(2):94–105, April (2020). [CrossRef] [Google Scholar]
  15. Khakon Das, Mausumi Maitra, Punit Sharma, and Minakshi Banerjee. Early started hybrid denoising technique for medical images. In Recent Trends in Signal and ImageProcessing, pages 131–140. Springer Singapore, May 2018. [Google Scholar]
  16. Hanafy M. Ali. MRI medical image denoising by fundamental filters. In HighResolution Neuroimaging Basic Physical Principles and Clinical Applications. InTech, March (2018). [Google Scholar]
  17. Shivangi Gupta, Archit Mishra, and Menaka R. Ischemic stroke detection using image processing and ANN. In 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies. IEEE, May (2014). [Google Scholar]
  18. Dylan E. Bruening, Shazia Dharssi, Ronald M. Lazar, Randolph S. Marshall, and Iris Asllani. Improved partial volume correction method for detecting brain activation in disease using arterial spin labeling (ASL) fMRI. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, August (2015). [Google Scholar]
  19. Shu Qi Chen, De Chun Cai, Ji Xin Chen, Han Yang, and Lian sheng Liu. Altered brain regional homogeneity following contralateral acupuncture at quchi (LI 11) and zusanli (ST 36) in ischemic stroke patients with left hemiplegia: An fMRI study. Chinese Journal of Integrative Medicine, 26(1):20–25, November (2019). [Google Scholar]
  20. Khakon Das, Mausumi Maitra, Minakshi Banerjee, and Punit Sharma. Embedded implementation of early started hybrid denoising technique for medical images with optimized loop. In Advances in Intelligent Systems and Computing, pages 295–308. Springer Singapore, July (2019). [Google Scholar]
  21. Juan Jose´ Vaquero and Paul Kinahan. Positron emission tomography: Current challenges and opportunities for technological advances in clinical and preclinical imagingsystems. Annual Review of Biomedical Engineering, 17(1):385–414, December (2015). [CrossRef] [Google Scholar]
  22. Khakon Das, Dipankar Khorat, and Samarendra Kumar Sharma. An embedded system for gray matter segmentation of PETimage. In Advances in Intelligent Systems and Computing, pages 145–157. Springer Singapore, (2020). [Google Scholar]
  23. Hsiao-Ying Wey, Virendra R Desai, and Timothy Q Duong. A review of current imaging methods used in stroke research. [Google Scholar]
  24. Neurological Research, 35(10):1092–1102, November (2013). [CrossRef] [Google Scholar]
  25. Khakon Das, Debashis Daschakladar, Partha Pratim Roy, Atri Chatterjee, and Shankar Prasad Saha. Epileptic seizure prediction by the detection of seizure waveform from the pre-ictal phase of EEG signal. Biomedical Signal Processing and Control, 57:101720, March (2020). [CrossRef] [Google Scholar]
  26. Jonathan Rubin and S. Mazdak Abulnaga. Ct-to-mr conditional generative adversarial networks for ischemic stroke lesion segmentation, (2019). [Google Scholar]
  27. Ramesh Sahathevan, Thomas Linden, Victor L. Villemagne, Leonid Churilov, John V. Ly, Christopher Rowe, Geoffrey Donnan, and Amy Brodtmann. Positron emission tomographic imaging in stroke. Stroke, 47(1):113–119, January 2016. [CrossRef] [Google Scholar]
  28. Arko Barman, Mehmet E. Inam, Songmi Lee, Sean Savitz, Sunil Sheth, and Luca Giancardo. Determining ischemic stroke from CT-angiography imaging using symmetrysensitive convolutional networks. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). IEEE, April (2019). [Google Scholar]
  29. Sujesh Sreedharan, KM Arun, PN Sylaja, Chandrasekharan Kesavadas, and Ranganatha Sitaram. Functional connectivity of language regions of stroke patients with expressive aphasia during real-time functional magnetic resonance imaging based neurofeedback. Brain Connectivity, 9(8):613–626, October (2019). [CrossRef] [Google Scholar]
  30. Hulin Kuang, Bijoy K. Menon, and Wu Qiu. Segmenting hemorrhagic and ischemic infarct simultaneously from follow-up noncontrast CT images in patients with acute ischemic stroke. IEEE Access, 7:39842–39851 (2019). [CrossRef] [Google Scholar]
  31. Hyunna Lee, Eun-Jae Lee, Sungwon Ham, Han-Bin Lee, Ji Sung Lee, Sun U. Kwon, Jong S. Kim, Namkug Kim, and Dong-Wha Kang. Machine learning approach to iden-tify stroke within 4.5 hours. Stroke, 51(3):860–866, March (2020). [CrossRef] [Google Scholar]
  32. Zhaoying Bian, Dong Zeng, Tianwu Xie, Jing Huang, Qianjin Feng, Jianhua Ma, and Habib Zaidi. Spatio-temporal constrained adaptive sinogram restoration for low-dose dynamic cerebral perfusion CT imaging. In 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC). IEEE, November (2018). [Google Scholar]
  33. Zhaoying Bian, Dong Zeng, Tianwu Xie, Jing Huang, Qianjin Feng, Jianhua Ma, and Habib Zaidi. Spatio-temporal constrained adaptive sinogram restoration for low-dose dynamic cerebral perfusion CT imaging. In 2018 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC). IEEE, November (2018). [Google Scholar]
  34. Lin Cheng, Zhiyuan Wu, Yi Fu, Fei Miao, Junfeng Sun, and Shanbao Tong. Reorganization of functional brain networks during the recovery of stroke: A functional MRI study. In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, August (2012). [Google Scholar]
  35. R. Pineiro, S. Pendlebury, H. Johansen-Berg, and P.M. Matthews. Altered hemodynamic responses in patients after subcortical stroke measured by functional MRI. Stroke, 33(1):103–109, January (2002). [CrossRef] [Google Scholar]
  36. Kelley C. Mazzetto-Betti, Renata F. Leoni, Octavio M. Pontes-Neto, Antonio C. Santos, Joao P. Leite, Afonso C. Silva, and Draulio B. de Araujo. The stability of the blood oxygenation level-dependent functional MRI response to motor tasks is altered in patients with chronic ischemic stroke. Stroke, 41(9):1921–1926, September (2010). [CrossRef] [Google Scholar]
  37. Qian Chen, Wei Wang, Yu-Chen Chen, Guozhong Chen, Ling Ni, Danfeng Zhang, Junshan Zhou, and Xin Dao Yin. Perithrombus vascular hyperintensity sign: detection of intracranial thrombus location and length in acute ischemic stroke. Japanese Journal ofRadiology, 38(6):516–523, March (2020). [Google Scholar]
  38. Susanne Wildermuth, Michael Knauth, Tobias Brandt, Ralph Winter, Klaus Sartor, and Werner Hacke. Role of CT angiography inpatient selection for thrombolytic therapy inacute hemispheric stroke. Stroke, 29(5):935–938, May (1998). [CrossRef] [Google Scholar]
  39. Adrian Petzl, Michael Derndorfer, Georgios Kollias, Kgomotso Moroka, Josef Aichinger, Helmut Pu¨rerfellner, and Martin Martinek. Cerebral thromboembolic risk in atrial fibrillation ablation: a direct comparison of vitamin k antagonists versus nonvitamin k-dependent oral anticoagulants. Journal of Interventional Cardiac Electrophysiology, March (2020). [Google Scholar]
  40. Thomas Gu, Richard I. Aviv, Allan J. Fox, and Elias Johansson. Symptomatic carotid near-occlusion causes a high risk of recurrent ipsilateral ischemic stroke. Journal of Neurology, 267(2):522–530, November (2019). [Google Scholar]
  41. Yannan Yu, Danfeng Guo, Min Lou, David Liebeskind, and Fabien Scalzo. Prediction of hemorrhagic transformation severity inacute stroke from source perfusion MRI. IEEE Transactions on Biomedical Engineering, 65(9):2058–2065, September (2018). [CrossRef] [Google Scholar]
  42. Luminita Moraru, Simona Moldovanu, Lucian Traian Dimitrievici, Fuqian Shi, Amira S. Ashour, and Nilanjan Dey. Quantitative diffusion tensor magnetic resonance imaging signal characteristics in the human brain: A hemispheres analysis. IEEESensors Journal, 17(15):4886–4893, August (2017). [CrossRef] [Google Scholar]
  43. Merve Fritsch, Thomas Krause, Fabian Klostermann, Kersten Villringer, Manuela Ihrke, and Christian H. Nolte. Thalamic aphasia after stroke is associated with left anterior lesion location. Journal of Neurology, 267(1):106–112, Sep (2019). [Google Scholar]
  44. Yueqiao Xu, Meng Qi, Ning Wang, Lidan Jiang, Wenjin Chen, Xin Qu, and Weitao Cheng. The effect of remote ischemic conditioning on blood coagulation function and cerebral blood flow in patients with aneurysmal subarachnoid hemorrhage. Neurologi-cal Sciences, 41(2):335–340, October (2019). [Google Scholar]
  45. Joel Elliot Dane Stanton, Arvind Chandratheva, Duncan Wilson, Isabel Charlotte Hostettler, Saiful Islam, and David John Werring. Clinical features distinguish cerebral amyloid angiopathy-associated convexity subarachnoid haemorrhage from suspected TIA. Journal of Neurology, 267(1):133–137, October (2019). [Google Scholar]
  46. Giovanni Furlanis, Milosˇ Ajcˇevic´, Alex Buoite Stella, Tommaso Cillotto, Paola Caruso, Mariana Ridolfi, Maria Assunta Cova, Marcello Naccarato, and Paolo Manganotti. Wake-up stroke: thrombolysis reduces ischemic lesion volume and neurological deficit. Journal of Neurology, 267(3):666–673, November (2019). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.