Karhunen Loeve enhanced synthetic discriminant functions with application to the protein structure identification in cryo-electron microscopic images

Vahid R. Riasati; Hui Zhou

doi:10.1117/12.547152

12 April 2004 Karhunen Loeve enhanced synthetic discriminant functions with application to the protein structure identification in cryo-electron microscopic images

Vahid R. Riasati, Hui Zhou

Author Affiliations +

Proceedings Volume 5434, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2004; (2004) https://doi.org/10.1117/12.547152
Event: Defense and Security, 2004, Orlando, Florida, United States

Abstract

In this paper we apply a modified synthetic discriminant function, SDF, based on Karhunen Loeve Transform to the recognition and identification of protein images formed from a cryo-electron microscopic imaging process. In the SDF filter synthesis, the use of whole image often presents a redundancy of features. Essentially, the Karhunen Loeve Transform is used as the means to incorporate training images in an SDF filter synthesis scenario. This method has the advantage of utilizing linearly independent training images, as the Karhunen Loeve Transform is the optimal method of decorrelating images. The transform establishes a new coordinate system. The axes of the new system are in the direction of the eigenvectors of the covariance matrix of the data population, and origin is set at the center of the data population. The principle component images resulted from such a realignment of the data provides us with the means for a new set of training images in a synthetic discriminant function filter, as the KLTSDF. We present the results of the application of this modified filter to a protein structure recognition problem.

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Vahid R. Riasati and Hui Zhou "Karhunen Loeve enhanced synthetic discriminant functions with application to the protein structure identification in cryo-electron microscopic images", Proc. SPIE 5434, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2004, (12 April 2004); https://doi.org/10.1117/12.547152

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