Biomedical Engineering and Bioengineering Commons^™

Application And Histology-Driven Refinement Of Active Contour Models To Functional Region And Nerve Delineation: Towards A Digital Brainstem Atlas, Nimal Patel, Sharmin Sultana, Tanweer Rashid, Dean Krusienski, Michel A. Audette

Electrical & Computer Engineering Faculty Publications

This paper presents a methodology for the digital formatting of a printed atlas of the brainstem and the delineation of cranial nerves from this digital atlas. It also describes on-going work on the 3D resampling and refinement of the 2D functional regions and nerve contours. In MRI-based anatomical modeling for neurosurgery planning and simulation, the complexity of the functional anatomy entails a digital atlas approach, rather than less descriptive voxel or surface-based approaches. However, there is an insufficiency of descriptive digital atlases, in particular of the brainstem. Our approach proceeds from a series of numbered, contour-based sketches coinciding with slices …

A Comparative Study Of Two Prediction Models For Brain Tumor Progression, Deqi Zhou, Loc Tran, Jihong Wang, Jiang Li, Karen O. Egiazarian (Ed.), Sos S. Agaian (Ed.), Atanas P. Gotchev (Ed.)

Electrical & Computer Engineering Faculty Publications

MR diffusion tensor imaging (DTI) technique together with traditional T1 or T2 weighted MRI scans supplies rich information sources for brain cancer diagnoses. These images form large-scale, high-dimensional data sets. Due to the fact that significant correlations exist among these images, we assume low-dimensional geometry data structures (manifolds) are embedded in the high-dimensional space. Those manifolds might be hidden from radiologists because it is challenging for human experts to interpret high-dimensional data. Identification of the manifold is a critical step for successfully analyzing multimodal MR images.

We have developed various manifold learning algorithms (Tran et al. 2011; Tran et al. …

Evaluation Of The Effects Of A Plasma Activated Medium On Cancer Cells, S. Mohades, M. Laroussi, J. Sears, N. Barekzi, H. Razavi

Electrical & Computer Engineering Faculty Publications

The interaction of low temperature plasma with liquids is a relevant topic of study to the field of plasma medicine. This is because cells and tissues are normally surrounded or covered by biological fluids. Therefore, the chemistry induced by the plasma in the aqueous state becomes crucial and usually dictates the biological outcomes. This process became even more important after the discovery that plasma activated media can be useful in killing various cancer cell lines. Here, we report on the measurements of concentrations of hydrogen peroxide, a species known to have strong biological effects, produced by application of plasma to …

Killing Adherent And Nonadherent Cancer Cells With The Plasma Pencil, Mounir Laroussi, Soheila Mohades, Nazir Barekzi

Electrical & Computer Engineering Faculty Publications

The application of low temperature plasmas in biology and medicine may lead to a paradigm shift in the way various diseases can be treated without serious side effects. Low temperature plasmas generated in gas mixtures that contain oxygen or air produce several chemically reactive species that have important biological implications when they interact with eukaryotic or prokaryotic cells. Here, a review of the effects of low temperature plasma generated by the plasma pencil on different cancerous cells is presented. Results indicate that plasma consistently shows a delayed killing effect that is dose dependent. In addition, there is some evidence that …

Adaptive Graph Construction For Isomap Manifold Learning, Loc Tran, Zezhong Zheng, Guoquing Zhou, Jiang Li, Karen O. Egiazarian (Ed.), Sos S. Agaian (Ed.), Atanas P. Gotchev (Ed.)

Electrical & Computer Engineering Faculty Publications

Isomap is a classical manifold learning approach that preserves geodesic distance of nonlinear data sets. One of the main drawbacks of this method is that it is susceptible to leaking, where a shortcut appears between normally separated portions of a manifold. We propose an adaptive graph construction approach that is based upon the sparsity property of the ℓ₁ norm. The ℓ₁ enhanced graph construction method replaces k-nearest neighbors in the classical approach. The proposed algorithm is first tested on the data sets from the UCI data base repository which showed that the proposed approach performs better than …