MRI feature extraction using a linear transformation

Hamid Soltanian-Zadeh; Joe P. Windham; Donald J. Peck

doi:10.1117/12.154536

14 September 1993 MRI feature extraction using a linear transformation

Hamid Soltanian-Zadeh, Joe P. Windham, Donald J. Peck

Proceedings Volume 1898, Medical Imaging 1993: Image Processing; (1993) https://doi.org/10.1117/12.154536
Event: Medical Imaging 1993, 1993, Newport Beach, CA, United States

Abstract

We present development and application of a feature extraction method for magnetic resonance imaging (MRI), without explicit calculation of tissue parameters. We generate a three-dimensional (3-D) feature space representation of the data, in which normal tissues are clustered around pre-specified target positions and abnormalities are clustered somewhere else. This is accomplished by a linear minimum mean square error transformation of categorical data to target positions. From the 3-D histogram (cluster plot) of the transformed data, we identify clusters and define regions of interest (ROIs) for normal and abnormal tissues. There 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 by tissue-parameter-weighted images, principal component images, and angle images, demonstrating its superiority for feature extraction. The method and its performance are illustrated using a computer simulation and MRI images of an egg phantom and a human brain.

Citation Download Citation

Hamid Soltanian-Zadeh, Joe P. Windham, and Donald J. Peck "MRI feature extraction using a linear transformation", Proc. SPIE 1898, Medical Imaging 1993: Image Processing, (14 September 1993); https://doi.org/10.1117/12.154536

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