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020 ▼a 9780438153769
035 ▼a (MiAaPQ)AAI10786362
035 ▼a (MiAaPQ)purdue:22430
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 247004
0820 ▼a 004
1001 ▼a Gulyanon, Sarun.
24510 ▼a Model and Appearance Based Analysis of Neuronal Morphology from Different Microscopy Imaging Modalities.
260 ▼a [S.l.]: ▼b Purdue University., ▼c 2018.
260 1 ▼a Ann Arbor: ▼b ProQuest Dissertations & Theses, ▼c 2018.
300 ▼a 175 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
500 ▼a Includes supplementary digital materials.
500 ▼a Advisers: Gavriil Tsechpenakis
5021 ▼a Thesis (Ph.D.)--Purdue University, 2018.
520 ▼a The neuronal morphology analysis is key for understanding how a brain works. This process requires the neuron imaging system with single-cell resolution
520 ▼a 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
520 ▼a Neuronal mechanisms are related to the morphology dynamics
520 ▼a 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
520 ▼a 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
590 ▼a School code: 0183.
650 4 ▼a Computer science.
690 ▼a 0984
71020 ▼a Purdue University. ▼b Computer Sciences.
7730 ▼t Dissertation Abstracts International ▼g 79-12B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0183
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14997349 ▼n KERIS ▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다.
980 ▼a 201812 ▼f 2019
990 ▼a ***1012033