Automated approach for detection of ischemic stroke using Delaunay Triangulation in brain MRI images

• Published in: Computers in biology and medicine Vol. 103; pp. 116 - 129
• Main Authors: Subudhi, Asit, Acharya, U. Rajendra, Dash, Manasa, Jena, Subhransu, Sabut, Sukanta
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.12.2018; Elsevier Limited
• Subjects: Accuracy; Algorithms; Artificial intelligence; Automation; Brain; Classifiers; Clustering; Delaunay triangulation; Feature extraction; FODPSO; Identification; Image detection; Image processing; Image segmentation; Internal Medicine; Ischemia; Ischemic stroke; Lesions; Magnetic resonance imaging; Medical imaging; Methods; Morphology; Neuroimaging; Other; Particle swarm optimization; Random forest; Segmentation; Similarity; Stroke; Support vector machines; Delaunay triangulation; Random forest; Ischemic stroke; Segmentation; FODPSO
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2018.10.016

It is difficult to develop an accurate algorithm to detect the stroke lesions using magnetic resonance imaging (MRI) images due to variation in different lesion sizes, variation in morphological structure, and similarity in intensity of lesion with normal brain in three types of stroke, namely partial anterior circulation syndrome (PACS), lacunar syndrome (LACS) and total anterior circulation stroke (TACS). In this paper, we have integrated the advantages of Delaunay triangulation (DT) and fractional order Darwinian particle swarm optimization (FODPSO), called DT-FODPSO technique for automatic segmentation of the structure of the stroke lesion. The approach was validated on 192 MRI images obtained from different stroke subjects. Statistical and morphological features were extracted and classified according to the Oxfordshire community stroke project (OCSP) using support vector machine (SVM) and random forest (RF) classifiers. The method effectively detected the stroke lesions and achieved promising results with an average sensitivity of 0.93, accuracy of 0.95, JI of 0.89 and Dice similarity index of 0.93 using RF classifier. These promising results indicates the DT based optimized approach is efficient in detecting ischemic stroke and it can aid the neuro-radiologists to validate their routine screening. •Detection of ischemic stroke lesion in DWI sequence of MRI of brain.•The combined Delaunay triangulation and Fractional order-DPSO is used to segment the brain lesion.•The GLCM based informative features were extracted and classified with Random Forest.•Performance was evaluated in terms of sensitivity, specificity, Jaccard index, dice similarity index.