Feature Extraction and Fusion for Protein Structure Identification in Cryo-Electron Microscopic Images Using Independent Component Analysis and the Projection-Slice Synthetic Filter

摘要

In this paper we utilize the Projection-Slice Synthetic Discriminant Function Filters, PSDF, in concert with an Independent Component Analysis technique to simultaneously reduce the data set that represents each of the training images and to emphasize subtle differences in each of the training images. These differences are encoded into the PSDF in order to improve the filter sensitivity for the recognition and identification of protein images formed from a cryo-electron microscopic imaging process. The PSDF and Independent Component Analysis provide a premise not only for the identification of the class of structures under consideration, but also for detecting the orientation of the structures in these images. 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 a component analysis and eigen values conditioning in forming the filter provides an enhanced approach of de-correlating 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.