Stroke Tissue Pattern Recognition Based on CT Texture Analysis

The main objective of this paper is a texture based solution to the problem of acute stroke tissue recognition on computed tomography images. Our proposed method of early stroke indication was based on two fundamental steps: i) segmentation of potential areas with distorted brain tissue (selection of regions of interest), ii) acute stroke tissue recognition by extracting and then classifying a set of well-di erentating features. The proposed solution used various numerical image descriptors determined in several image transformation domains: 2D Fourier domain, polar 2D Fourier domain, and multiscale domains (i.e., wavelet, complex wavelet, and contourlet domain). The obtained results indicate the possibility of relatively e ective detection of early stroke symptoms in CT images. The selected normal or pathological blocks were classi ed by LogitBoost with the accuracy close to 75% with the use of adjusted cross-validation procedure.