Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.