Texture-Based Segmentation and Finite Element Mesh Generation for Heterogeneous Biological Image Data

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dc.contributor.advisorMontas, Hubert Jen_US
dc.contributor.authorGudla, Prabhakar Ren_US
dc.contributor.departmentBiological Resources Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2005-08-03T13:51:05Z
dc.date.available2005-08-03T13:51:05Z
dc.date.issued2005-04-12en_US
dc.description.abstractThe design, analysis, and control of bio-systems remain an engineering challenge. This is mainly due to the material heterogeneity, boundary irregularity, and nonlinear dynamics associated with these systems. The recent developments in imaging techniques and stochastic upscaling methods provides a window of opportunity to more accurately assess these bio-systems than ever before. However, the use of image data directly in upscaled stochastic framework can only be realized by the development of certain intermediate steps. The goal of the research presented in this dissertation is to develop a texture-segmentation method and a unstructured mesh generation for heterogeneous image data. The following two new techniques are described and evaluated in this dissertation: 1. A new texture-based segmentation method, using the stochastic continuum concepts and wavelet multi-resolution analysis, is developed for characterization of heterogeneous materials in image data. The feature descriptors are developed to efficiently capture the micro-scale heterogeneity of macro-scale entities. The materials are then segmented at a representative elementary scale at which the statistics of the feature descriptor stabilize. 2. A new unstructured mesh generation technique for image data is developed using a hierarchical data structure. This representation allows for generating quality guaranteed finite element meshes. The framework for both the methods presented in this dissertation, as such, allows them for extending to higher dimensions. The experimental results using these methods conclude them to be promising tools for unifying data processing concepts within the upscaled stochastic framework across biological systems. These are targeted for inclusion in decision support systems where biological image data, simulation techniques and artificial intelligence will be used conjunctively and uniformly to assess bio-system quality and design effective and appropriate treatments that restore system health.en_US
dc.format.extent6206915 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/2395
dc.language.isoen_US
dc.subject.pqcontrolledEngineering, Biomedicalen_US
dc.subject.pqcontrolledAgriculture, Soil Scienceen_US
dc.subject.pqcontrolledEngineering, Generalen_US
dc.subject.pquncontrolledmulti-dimensional waveletsen_US
dc.subject.pquncontrolledtexture segmentationen_US
dc.subject.pquncontrolledfinite element mesh generationen_US
dc.subject.pquncontrolledimage analysisen_US
dc.subject.pquncontrolledupscaled transport modelingen_US
dc.subject.pquncontrolleddecision support systemsen_US
dc.titleTexture-Based Segmentation and Finite Element Mesh Generation for Heterogeneous Biological Image Dataen_US
dc.typeDissertationen_US

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