Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 3 of 3
Full-Text Articles in Engineering
Incorporating Priors For Medical Image Segmentation Using A Genetic Algorithm, Payel Ghosh, Melanie Mitchell, James A. Tanyi, Arthur Y. Hung
Incorporating Priors For Medical Image Segmentation Using A Genetic Algorithm, Payel Ghosh, Melanie Mitchell, James A. Tanyi, Arthur Y. Hung
Computer Science Faculty Publications and Presentations
Medical image segmentation is typically performed manually by a physician to delineate gross tumor volumes for treatment planning and diagnosis. Manual segmentation is performed by medical experts using prior knowledge of organ shapes and locations but is prone to reader subjectivity and inconsistency. Automating the process is challenging due to poor tissue contrast and ill-defined organ/tissue boundaries in medical images. This paper presents a genetic algorithm for combining representations of learned information such as known shapes, regional properties and relative position of objects into a single framework to perform automated three-dimensional segmentation. The algorithm has been tested for prostate segmentation …
A Verified Information-Flow Architecture, Arthur Azevedo De Amorim, Nathan Collins, André Dehon, Delphine Demange, Cătălin Hriţcu, David Pichardie, Benjamin C. Pierce, Randy Pollack, Andrew Tolmach
A Verified Information-Flow Architecture, Arthur Azevedo De Amorim, Nathan Collins, André Dehon, Delphine Demange, Cătălin Hriţcu, David Pichardie, Benjamin C. Pierce, Randy Pollack, Andrew Tolmach
Computer Science Faculty Publications and Presentations
SAFE is a clean-slate design for a highly secure computer system, with pervasive mechanisms for tracking and limiting information flows. At the lowest level, the SAFE hardware supports fine-grained programmable tags, with efficient and flexible propagation and combination of tags as instructions are executed. The operating system virtualizes these generic facilities to present an information-flow abstract machine that allows user programs to label sensitive data with rich confidentiality policies. We present a formal, machine-checked model of the key hardware and software mechanisms used to dynamically control information flow in SAFE and an end-to-end proof of noninterference for this model. We …
Sparse Encoding Of Binocular Images For Depth Inference, Sheng Y. Lundquist, Dylan M. Paiton, Peter F. Schultz, Garrett T. Kenyon
Sparse Encoding Of Binocular Images For Depth Inference, Sheng Y. Lundquist, Dylan M. Paiton, Peter F. Schultz, Garrett T. Kenyon
Computer Science Faculty Publications and Presentations
Sparse coding models have been widely used to decompose monocular images into linear combinations of small numbers of basis vectors drawn from an overcomplete set. However, little work has examined sparse coding in the context of stereopsis. In this paper, we demonstrate that sparse coding facilitates better depth inference with sparse activations than comparable feed-forward networks of the same size. This is likely due to the noise and redundancy of feed-forward activations, whereas sparse coding utilizes lateral competition to selectively encode image features within a narrow band of depths.