A critical appraisal on wavelet based features from brain MR images for efficient characterization of ischemic stroke injuries

Abstract

Ischemic stroke is a severe neuro disorder typically characterized by a block inside a blood vessel supplying blood to the brain. It remains the third leading cause of death, after a heart attack and cancer. Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) were the major imaging modalities used in diagnosing this disorder. While the CT imaging can be used at the primary stage, MRI proves to be a necessary aid for progressive diagnostic planning in the treatment of stroke injuries. Developing a fully automatic approach for lesion segmentation is a challenging issue due to the complex nature of the lesion structures. This research aims at examining the properties of such complex structures. It analyses the characteristics of the normal brain tissues and abnormal lesion structures using a three-level wavelet decomposition procedure. Four different wavelet functions namely Daubechies, Symlet, Coiflet and De-Meyer were applied to the different datasets and the resulting observations were examined based on their feature statistics. Experiments indicate that the feature statistics obtained using the Daubechies and the De-Meyer wavelets were able to distinguish between normal brain tissues and abnormal lesion structures.

Selection of Wavelet Basis Function for Image Compression – A Review

“…R and Menaka. R analysed the characteristics of ischemic stroke injuries using wavelets [43]. Four different wavelet functions namely Daubechies, Symlet, Coiflet and De-Meyer were applied to the different datasets and the resulting observations were examined based on their feature statistics.…”