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Current Medical Imaging

Author(s): Sunil Babu Melingi* and V. Vijayalakshmi

DOI: 10.2174/1573405614666180209150338

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A Hybrid Approach for Sub-Acute Ischemic Stroke Lesion Segmentation Using Random Decision Forest and Gravitational Search Algorithm

Page: [170 - 183] Pages: 14

  • * (Excluding Mailing and Handling)

Abstract

Background: The sub-acute ischemic stroke is the most basic illnesses reason for death on the planet. We evaluate the impact of segmentation technique during the time of breaking down the capacities of the cerebrum.

Objective: The main objective of this paper is to segment the ischemic stroke lesions in Magnetic Resonance (MR) images in the presence of other pathologies like neurological disorder, encephalopathy, brain damage, Multiple sclerosis (MS).

Methods: In this paper, we utilize a hybrid way to deal with segment the ischemic stroke from alternate pathologies in magnetic resonance (MR) images utilizing Random Decision Forest (RDF) and Gravitational Search Algorithm (GSA). The RDF approach is an effective machine learning approach.

Results: The RDF strategy joins two parameters; they are; the number of trees in the forest and the number of leaves per tree; it runs quickly and proficiently when dealing with vast data. The GSA algorithm is utilized to optimize the RDF data for choosing the best number of trees and the number of leaves per tree in the forest.

Conclusion: This paper provides a new hybrid GSA-RDF classifier technique to segment the ischemic stroke lesions in MR images. The experimental results demonstrate that the proposed technique has the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Bias Error (MBE) ranges are 16.5485 %, 7.2654 %, and 2.4585 %individually. The proposed RDF-GSA algorithm has better precision and execution when compared with the existing ischemic stroke segmentation method.

Keywords: Sub acute ischemic stroke, MR images, stroke segmentation, hybrid GSA -RDF algorithm, bagger algorithm, cerebrum.

Graphical Abstract

[1]
Madhukumar S, Santhiyakumari N. A novel segmentation and contouring scheme to assist accurate brain lesion classification. J Biomed Eng Med Imaging 2014; 1(6)Available from;. http://scholarpublishing.org/index.php/JBEMi/article/view/546
[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.