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Model and Appearance Based Analysis of Neuronal Morphology from Different Microscopy Imaging Modalities
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Model and Appearance Based Analysis of Neuronal Morphology from Different Microscopy Imaging Modalities. |
| 개인저자 | Gulyanon, Sarun. |
| 단체저자명 | Purdue University. Computer Sciences. |
| 발행사항 | [S.l.]: Purdue University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 175 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-12B(E). Dissertation Abstract International |
| ISBN | 9780438153769 |
| 학위논문주기 | Thesis (Ph.D.)--Purdue University, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Includes supplementary digital materials. Advisers: Gavriil Tsechpenakis |
| 요약 | The neuronal morphology analysis is key for understanding how a brain works. This process requires the neuron imaging system with single-cell resolution |
| 요약 | Modeling of the structure and dynamics of neuronal circuits creates understanding about how connectivity patterns are formed within a motor circuit and determining whether the connectivity map of neurons can be deduced by estimations of neuronal |
| 요약 | Neuronal mechanisms are related to the morphology dynamics |
| 요약 | Lastly, modeling the link between structural and functional development depicts the correlation between neuron growth and protein interactions. This requires the morphology analysis of different imaging modalities. It can be solved using the par |
| 요약 | Our method follows the global-to-local approach to solve both part-wise segmentation and registration across modalities. Our methods address common issues in automated morphology analysis from extracting morphological features to tracking neuron |
| 일반주제명 | Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
