Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Fundamentals of digital image processing
Fundamentals of digital image processing
Digital image processing
Digital Image Processing
A multidimensional nonlinear edge-preserving filter for magnetic resonance image restoration
IEEE Transactions on Image Processing
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This paper presents development and application of a feature extraction method for magnetic resonance imaging (MRI), without explicit calculation of tissue parameters. A three-dimensional (3-D) feature space representation of the data is generated in which normal tissues are clustered around pre-specified target positions and abnormalities are clustered elsewhere. This is accomplished a linear minimum mean square error transformation of categorical data to target positions. From the 3-D histogram (cluster plot) of the transformed data, clusters are identified and regions of interest (ROIs) for normal and abnormal tissues are defined. These ROIs are used to estimate signature (feature) vectors for each tissue type which in turn are used to segment the MRI scene. The proposed feature space is compared to those generated tissue-parameter-weighted images, principal component images, and angle images, demonstrating its superiority for feature extraction. The method and its performance are illustrated using MRI images of an egg phantom and a human brain.