[2]
Fiot JB, Cohen LD, Raniga P, Fripp J. Efficient brain lesion segmentation using multi‐modality tissue‐based feature selection and support vector machines. Int J Numer Methods Biomed Eng 2013; 29(9): 905-15.
[3]
Rekik I, Allassonnière S, Carpenter TK, Wardlaw JM. 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 Clin 2012; 1(1): 164-78.
[4]
Etgen T, Steinich I, Gsottschneider L. Thrombolysis for ischemic stroke in patients with brain tumors. J Stroke Cerebrovasc Dis 2014; 23(2): 361-6.
[5]
Huang FH. Research on classification of remote sensing image based on svm including textural features. Appl Mech Mater 2014; 543-547: 2559-65.
[6]
Ghosh N, Sun Y, Bhanu B, Ashwal S, Obenaus A. Automated detection of brain abnormalities in neonatal hypoxia ischemic injury from MR images. Med Image Anal 2014; 18(7): 1059-69.
[7]
Maier O, Menze BH, von der Gablentz J, et al. ISLES 2015-A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI. Med Image Anal 2017; 35: 250-69.
[8]
Guo Y, Zhou IY, Chan ST, et al. pH-sensitive MRI demarcates graded tissue acidification during acute stroke-pH specificity enhancement with magnetization transfer and relaxation-normalized Amide Proton Transfer (APT) MRI. Neuroimage 2016; 141: 242-9.
[9]
Karthik R, Menaka R. A critical appraisal on wavelet based features from brain MR images for efficient characterization of ischemic stroke injuries. ELCVIA Electronic letters on computer vision and image analysis 2016; 15(3): 1-6.
[10]
Artzi M, Aizenstein O, Jonas-Kimchi T, Myers V, Hallevi H, Bashat DB. FLAIR lesion segmentation: Application in patients with brain tumors and acute ischemic stroke. Eur J Radiol 2013; 82(9): 1512-8.
[11]
Griffis JC, Allendorfer JB, Szaflarski JP. Voxel-based Gaussian naïve Bayes classification of ischemic stroke lesions in individual T1-weighted MRI scans. J Neurosci Methods 2016; 257: 97-108.
[12]
Griffanti L, Zamboni G, Khan A, et al. BIANCA (Brain Intensity AbNormality Classification Algorithm): A new tool for automated segmentation of white matter hyperintensities. Neuroimage 2016; 141: 191-205.
[13]
Rondina JM, Filippone M, Girolami M, Ward NS. Decoding post-stroke motor function from structural brain imaging. Neuroimage Clin 2016; 12: 372-80.
[14]
Bakhshali MA. Segmentation and enhancement of brain MR images using fuzzy clustering based on information theory. Soft Comput 2016; 21(22): 1-8.
[15]
Kaur R, Malik G. An image segmentation using improved FCM watershed algorithm and DBMF. J Image Graphics 2014; 2(2): 106-12.
[16]
Wang L, Li B, Tian LF. Multi-modal medical image fusion using the inter-scale and intra-scale dependencies between image shift-invariant shearlet coefficients. Inf Fusion 2014; 19: 20-8.
[17]
Rajalakshmi N, Prabha VL. A hybrid approach for automatic classification of brain magnetic resonance images using colour-converted clustering segmentation with multi-class support vector machine classifier. Austr J Electric Electron Eng 2013; 10(2): 251-63.
[18]
Saritha M, Joseph KP, Mathew AT. Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network. Patt Recog Lett 2013; 34(16): 2151-6.
[19]
Jegadeeshwaran R, Sugumaran V. Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines. Mech Syst Signal Process 2015; 52: 436-46.
[20]
Hor S, Moradi M. Learning in data-limited multimodal scenarios: Scandent decision forests and tree-based features. Med Image Anal 2016; 34: 30-41.
[21]
Nayak DR, Dash R, Majhi B. Brain MR image classification using two-dimensional discrete wavelet transform and AdaBoost with random forests. Neurocomputing 2016; 177: 188-97.
[22]
Murphy K, van der Aa NE, Negro S, et al. Automatic quantification of ischemic injury on diffusion-weighted MRI of neonatal hypoxic ischemic encephalopathy. Neuroimage Clin 2017; 14: 222-32.
[23]
Payabvash S, Taleb S, Benson JC, McKinney AM. Interhemispheric asymmetry in distribution of infarct lesions among acute ischemic stroke patients presenting to hospital. J Stroke Cerebrovasc Dis 2016; 25(10): 2464-9.
[24]
François C, Ripollés P, Bosch L, et al. Language learning and brain reorganization in a 3.5-year-old child with left perinatal stroke revealed using structural and functional connectivity. Cortex 2016; 77: 95-118.
[25]
Galimzianova A, Pernuš F, Likar B, Špiclin Ž. Stratified mixture modeling for segmentation of white-matter lesions in brain MR images. Neuroimage 2016; 124: 1031-43.
[26]
Venkatesan AS, Parthiban L. A novel nature inspired fuzzy tsallis entropy segmentation of magnetic resonance images. Neuroquantology 2014; 12(2)
[27]
Murphy K, van der Aa NE, Negro S, et al. Automatic quantification of ischemic injury on diffusion-weighted MRI of neonatal hypoxic ischemic encephalopathy. Neuroimage Clin 2017; 14: 222-32.
[28]
Rajini NH, Bhavani R. Computer aided detection of ischemic stroke using segmentation and texture features. Measurement 2013; 46(6): 1865-74.
[29]
Karthik R, Menaka R. A multi-scale approach for detection of ischemic stroke from brain MR images using discrete curvelet transformation. Measurement 2017; 100: 223-32.
[30]
Hachaj T, Ogiela MR. Application of neural networks in detection of abnormal brain perfusion regions. Neurocomputing 2013; 122: 33-42.
[31]
Maier O, Wilms M, von der Gablentz J, Krämer UM, Münte TF, Handels H. Extra tree forests for sub-acute ischemic stroke lesion segmentation in MR sequences. J Neurosci Methods 2015; 240: 89-100.
[32]
Rekik I, Allassonnière S, Carpenter TK, Wardlaw JM. Using longitudinal metamorphosis to examine ischemic stroke lesion dynamics on perfusion-weighted images and in relation to final outcome on T2-w images. Neuroimage Clin 2014; 5: 332-40.
[33]
Chyzhyk D, Dacosta-Aguayo R, Mataró M, Graña M. An active learning approach for stroke lesion segmentation on multimodal MRI data. Neurocomputing 2015; 150: 26-36.
[34]
Mitra J, Bourgeat P, Fripp J, et al. Lesion segmentation from multimodal MRI using random forest following ischemic stroke. Neuroimage 2014; 98: 324-35.
[35]
Nabizadeh N, John N, Wright C. Histogram-based gravitational optimization algorithm on single MR modality for automatic brain lesion detection and segmentation. Expert Syst Appl 2014; 41(17): 7820-36.
[36]
Rashedi E, Nezamabadi-Pour H, Saryazdi S. GSA: a gravitational search algorithm. Inf Sci 2009; 179(13): 2232-48.
[37]
Babajide Mustapha I, Saeed F. Bioactive molecule prediction using extreme gradient boosting. Molecules 2016; 21(8): 983.
[38]
Ibrahim IA, Khatib T. A novel hybrid model for hourly global solar radiation prediction using random forests technique and firefly algorithm. Energy Convers Manage 2017; 138: 413-25.
[39]
Liao L, Lin T, Li B. MRI brain image segmentation and bias field correction based on fast spatially constrained kernel clustering approach. Pattern Recognit Lett 2008; 29(10): 1580-8.
[40]
Thanellas A, Pollari M, Alhonnoro T, Lilja M. Brain extraction from MR images using a combination of segmentation fusion and marker-controlled watershed transform. In: 2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room- Temperature Semiconductor Detector Workshop (NSS/MIC/ RTSD), 2016; pp. 1-4.