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We present a rule-based framework for the development of scalable parallel high performance simulations for a broad class of scientific applications (with particular emphasis on continuum mechanics). We take a pragmatic approach to our programming abstractions by implementing structures that are used frequently and have common high performance implementations on distributed memory architectures. The resulting framework borrows heavily from rule-based systems for relational database models, however limiting the scope to those parts that have obvious high performance implementation. Using our approach, we demonstrate predictable performance behavior and efficient utilization of large scale distributed memory architectures on problems of significant complexity involving multiple disciplines.

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Classification of breast masses in greyscale ultrasound images is undertaken using a multiparameter approach. Five parameters reflecting the non-Rayleigh nature of the backscattered echo were used. These parameters, based mostly on the Nakagami and K distributions, were extracted from the envelope of the echoes at the site, boundary, spiculated region and shadow of the mass. They were combined to create a linear discriminant. The performance of this discriminant for the classification of breast masses was studied using a data set consisting of 70 benign and 29 malignant cases. The Az value for the discriminant was 0.96 +/- 0.02, showing great promise in the classification of masses into benign and malignant ones. The discriminant was combined with the level of suspicion values of the radiologist leading to an Az value of 0.97 +/- 0.014. The parameters used here can be calculated with minimal clinical intervention, so the method proposed here may therefore be easily implemented in an automated fashion. These results also support the recent reports suggesting that ultrasound may help as an adjunct to mammography in breast cancer diagnostics to enhance the classification of breast masses.

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