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

Electronic Letters on Computer Vision and Image Analysis: ELCVIA

Karthik R¹ 

[1] VIT University

关键词: Ischemic Stroke;    Watershed transformation;    Discrete Wavelet;    Feature statistics;   

DOI  :  10.5565/rev/elcvia.804

学科分类：计算机科学（综合）

来源: ELCVIA

【 摘 要 】

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 for death, after heart attack and cancer. Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) were the vital major imaging techniques used for diagnosing this disorder. While the CT imaging can be used at the primary stage, MRI proves to be a standard 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 lesions structures. This research basically 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 obtained. Experiments indicate the feature statistics obtained from daubechies and de-meyer wavelets were able to clearly distinguish between the typical brain tissues and abnormal lesion structures.

【 授权许可】

CC BY-NC-ND