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Automatic and interactive segmentations using deformable and graphical models
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Uzunbas, Mustafa Gokhan. Automatic and interactive segmentations using deformable and graphical models. Retrieved from https://doi.org/doi:10.7282/T3W37Z2B

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TitleAutomatic and interactive segmentations using deformable and graphical models
NameUzunbas, Mustafa Gokhan (author); Metaxas, Dimitris (chair); Elgammal, Ahmed (internal member); Bekris, Kostas (internal member); Shen, Dinggang (outside member); Rutgers University; Graduate School - New Brunswick
Date Created2015
Other Date2015-01 (degree)
SubjectComputer Science, Image segmentation, Image analysis, Electron microscopy
Extent1 online resource (xvii, 94 p. : ill.)
DescriptionImage segmentation i.e. dividing an image into regions and categories is a classic yet still challenging problem. The key to success is to use/develop the right method for the right appli- cation. In this dissertation, we aim to develop automatic and interactive segmentation methods for different types of tissues that are acquired at different scales and resolutions from different medical imaging modalities such as Magnetic Resonance (MR), Computed Tomography (CT) and Electron Microscopy (EM) imaging. First, we developed an automated segmentation method for segmenting multiple organs simultaneously from MR and CT images. We propose a hybrid method that takes advantage of two well known energy-minimization-based approaches combined in a unified framework. We validate this proposed method on cardiac four-chamber segmentation from CT and knee joint bones segmentation from MR images. We compare our method with other existing techniques and show certain improvements and advantages. Second, we developed a graph partitioning algorithm for characterizing neuronal tissue structurally and contextually from EM images. We propose a multistage decision mechanism that utilizes differential geometric properties of objects in a cellular processing context. Our results indicate that this proposed approach can successfully partition images into structured segments with minimal expert supervision and can potentially form a basis for a larger scale volumetric data interpretation. We compare our method with other proposed methods in a workshop challenge and show promising results. Third, we developed an efficient learning-based method for segmentation of neuron struc- tures from 2D and 3D EM images. We propose a graphical-model-based framework to do inference on hierarchical merge-tree of image regions. In particular, we extract the hierarchy of regions in the low level, design 2D and 3D discriminative features to extract higher level information and utilize a Conditional Random Field based parameter learning on top of it. The effectiveness of the proposed method in 2D is demonstrated by comparing our method with other methods in a workshop challenge. Our method outperforms all participant methods ex- cept one. In 3D, we compare our method to existing methods and show that the accuracy of our results are comparable to state-of-the-art while being much more efficient. Finally, we extended our inference algorithm to a proofreading framework for manual cor- rections of automatic segmentation results. We propose a very efficient and easy-to-use inter- face for high resolution 3D EM images. In particular, we utilize the probabilistic confidence level of the graphical model to guide the user during interaction. We validate the effective- ness of this framework by robot simulations and demonstrate certain advantages compared to baseline methods.
NotePh.D.
NoteIncludes bibliographical references
Noteby Mustafa Gokhan Uzunbas
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/T3W37Z2B
Languageeng
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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