Enhanced Projection Slice Theorem Synthetic Discriminant Functions Based on the Karhunen-Loeve Transform with Application to the Protein Structure Identification in Cryo-Electron Microscopic Images

摘要

In this paper we utilize the Karhunen-Love Transform with the Projection-Slice Synthetic Discriminant Function Filters, KLPSDF to reduce the data set that represents each of the training images and to emphasize the subtle differences in each of the training images. These differences are encoded into the PSDF in order to improve the filter sensitivity to the recognition and identification of protein images formed from a cryo-electron microscopic imaging process. The PSDF has been shown to improve the performance of the SDF on specific images in previous papers. The protein structures found in cryo-electron microscopic imaging represent a class of objects that have low resolution and contrast and subtle variation. This poses a challenge in design of filters to recognize these structures due to false targets that often have similar characteristics as the protein structures. The incorporation of the KLT in forming the filter provides an optimal method of decorrelating images prior to their incorporation into the filter. We present our method of filter synthesis and the results of the application of this modified filter to a protein structure recognition problem.