Automatic detection of ischemic stroke using higher order spectra features in brain MRI images

Abstract

The gravity of ischemic stroke is the key factor in deciding upon the optimum therapeutic intervention. Ischemic stroke can be divided into three main groups: lacunar syndrome (LACS), partial anterior circulation syndrome (PACS), and total anterior circulation stroke (TACS), where the corresponding severity is mild, medium, and high, respectively. Herein, a unique method for the automatic detection of ischemic stroke severity is presented. The proposed system is based upon the extraction of higher order bispectrum entropy and its phase features from brain MRI (Magnetic Resonance Imaging) images. For classification, which is used to establish stroke severity, a support vector machine was incorporated into the design. The developed technique effectively detected the stroke lesion, and achieved a sensitivity, specificity, accuracy, and positive predictive value equal to 96.4%, 100%, 97.6% and 100%, respectively. The results were obtained without the need for manual intervention. This design is advantageous over state-of-the-art automated stroke severity detection systems, which require the reading neuroradiologist to manually determine the region of interest. Hence, the method is efficacious for delivering decision support in the diagnosis of ischemic stroke severity, thereby aiding the neuroradiologist in routine screening procedures.