Computational Aspects of Pathology Image Classification and Retrieval

We are investigating the role of high performance computing for support of a comprehensive pathology image atlas. The primary computing component is a database access mechanism providing retrieval by content based image matching (CBIR) along with traditional term based queries. An organization based on information theoretic and Bayesian principles using decision trees and signature files is being developed. The essential role of HPC is the discovery, selection, and optimization of medically useful image feature sets via genetic algorithm and simulated annealing methods. This paper outlines the problem area along with aspects of the underlying theoretical basis and distinguishing computing characteristics. Efficiency of key portions of the computations can be greatly improved by using parallelism within the computer word length using bit counting instructions to implement voting and multimedia style instruction sets for low level image processing.

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