An experimental study of Stockwell transform-based feature extraction method for ischemic stroke detection
by P.V. Jayaram; R. Menaka
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 21, No. 1, 2016

Abstract: This work proposes an approach to detect the presence of ischemic lesion in the tissue part of the brain. Accurate classification and segmentation of stroke affected regions are essential for quick diagnosis. Image classification is an important step for high-level processing of automatic brain stroke classification. The proposed method employs Skull Elimination Algorithm (SEA), Central Line Sketching Algorithm (CLSA), Fuzzy C-means (FCM) clustering-based segmentation and Discrete Orthonormal Stockwell Transform (DOST). The skull elimination and CLSAs are the main stages of preprocessing. The skull elimination was mainly adopted for extracting only the tissue part in the brain and the CLSA is used for splitting the Magnetic Resonance Image (MRI) into two equal sections. FCM-based segmentation is mainly used for extracting the lesion part. Then in the next stage DOST is applied into left and right sections of brain image for extracting the features such as mean, median and standard deviation which classifies the normal and abnormal MRI.

Online publication date: Tue, 24-May-2016

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