Purdue University

About: Purdue University is a education organization based out in West Lafayette, Indiana, United States. It is known for research contribution in the topics: Population & Heat transfer. The organization has 73219 authors who have published 163563 publications receiving 5775236 citations. The organization is also known as: Purdue & Purdue-West Lafayette.

A three-dimensional statistical approach to improved image quality for multislice helical CT.

Abstract: Multislice helical computed tomography scanning offers the advantages of faster acquisition and wide organ coverage for routine clinical diagnostic purposes. However, image reconstruction is faced with the challenges of three-dimensional cone-beam geometry, data completeness issues, and low dosage. Of all available reconstruction methods, statistical iterative reconstruction (IR) techniques appear particularly promising since they provide the flexibility of accurate physical noise modeling and geometric system description. In this paper, we present the application of Bayesian iterative algorithms to real 3D multislice helical data to demonstrate significant image quality improvement over conventional techniques. We also introduce a novel prior distribution designed to provide flexibility in its parameters to fine-tune image quality. Specifically, enhanced image resolution and lower noise have been achieved, concurrently with the reduction of helical cone-beam artifacts, as demonstrated by phantom studies. Clinical results also illustrate the capabilities of the algorithm on real patient data. Although computational load remains a significant challenge for practical development, superior image quality combined with advancements in computing technology make IR techniques a legitimate candidate for future clinical applications.

A distance measure between attributed relational graphs for pattern recognition

Abstract: A method to determine a distance measure between two nonhierarchical attributed relational graphs is presented. In order to apply this distance measure, the graphs are characterised by descriptive graph grammars (DGG). The proposed distance measure is based on the computation of the minimum number of modifications required to transform an input graph into the reference one. Specifically, the distance measure is defined as the cost of recognition of nodes plus the number of transformations which include node insertion, node deletion, branch insertion, branch deletion, node label substitution and branch label substitution. The major difference between the proposed distance measure and the other ones is the consideration of the cost of recognition of nodes in the distance computation. In order to do this, the principal features of the nodes are described by one or several cost functions which are used to compute the similarity between the input nodes and the reference ones. Finally, an application of this distance measure to the recognition of lower case handwritten English characters is presented.

Privacy-preserving distributed mining of association rules on horizontally partitioned data

Abstract: Data mining can extract important knowledge from large data collections ut sometimes these collections are split among various parties. Privacy concerns may prevent the parties from directly sharing the data and some types of information about the data. We address secure mining of association rules over horizontally partitioned data. The methods incorporate cryptographic techniques to minimize the information shared, while adding little overhead to the mining task.

A generalized Gaussian image model for edge-preserving MAP estimation

Abstract: The authors present a Markov random field model which allows realistic edge modeling while providing stable maximum a posterior (MAP) solutions. The model, referred to as a generalized Gaussian Markov random field (GGMRF), is named for its similarity to the generalized Gaussian distribution used in robust detection and estimation. The model satisfies several desirable analytical and computational properties for map estimation, including continuous dependence of the estimate on the data, invariance of the character of solutions to scaling of data, and a solution which lies at the unique global minimum of the a posteriori log-likelihood function. The GGMRF is demonstrated to be useful for image reconstruction in low-dosage transmission tomography. >