Optimal linear transformation for MRI feature extraction.

Optimal linear transformation for MRI feature extraction. Research Abstract Details 

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Optimal linear transformation for MRI feature extraction. Abstract Text:

H Soltanian-Zadeh,J P Windham,D J Peck,

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 prespecified target positions and abnormalities are clustered elsewhere. 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, clusters are identified and regions of interest (ROI's) for normal and abnormal tissues are defined. These ROI's are used to estimate signature (prototype) 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 and scene segmentation. Its relationship with discriminant analysis is discussed. The method and its performance are illustrated using a computer simulation and MRI images of an egg phantom and a human brain.

Optimal linear transformation for MRI feature extraction. Publishing Authors By Initials

H Soltanian-Zadeh,JP Windham,DJ Peck,

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Optimal linear transformation for MRI feature extraction. Journal Published:

PUBLICATION TYPE: Journal Article

Journal: IEEE transactions on medical imaging

VOLUME: 15

Page Numbers: 749-67

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ISSN: 0278-0062

DAY: 24

MONTH: 01

YEAR: 1996

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LANGUAGE: eng

NlmUniqueID: 8310780

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Grant and Affiliation Information for Optimal linear transformation for MRI feature extraction.

AFFILIATION: Dept. of Diagnostic Radiol. & Med. Imaging, Henry Ford Hospital, Detroit, MI.

Country: United States

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MEDLINETA: IEEE Trans Med Imaging

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