Dendritic spine shape analysis using disjunctive normal shape models

Ghani, Muhammad Usman and Mesadi, Fitsum and Demir Kanık, Sümerya Ümmühan and Argunşah, Ali Özgür and Israely, Inbal and Ünay, Devrim and Taşdizen, Tolga and Çetin, Müjdat (2016) Dendritic spine shape analysis using disjunctive normal shape models. In: IEEE International Symposium on Biomedical Imaging, Prague, Czech Republic

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Analysis of dendritic spines is an essential task to understand the functional behavior of neurons. Their shape variations are known to be closely linked with neuronal activities. Spine shape analysis in particular, can assist neuroscientists to identify this relationship. A novel shape representation has been proposed recently, called Disjunctive Normal Shape Models (DNSM). DNSM is a parametric shape representation and has proven to be successful in several segmentation problems. In this paper, we apply this parametric shape representation as a feature extraction algorithm. Further, we propose a kernel density estimation (KDE) based classification approach for dendritic spine classification. We evaluate our proposed approach on a data set of 242 spines, and observe that it outperforms the classical morphological feature based approach for spine classification. Our probabilistic framework also provides a way to examine the separability of spine shape classes in the likelihood ratio space, which leads to further insights about the nature of the shape analysis problem in this context.
Item Type: Papers in Conference Proceedings
Uncontrolled Keywords: Disjunctive Normal Shape Model, Spine Classification, Shape analysis, Kernel density estimation, microscopy, neuroimaging
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Q Science > QP Physiology
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Electronics
Faculty of Engineering and Natural Sciences
Depositing User: Müjdat Çetin
Date Deposited: 14 Jun 2016 14:29
Last Modified: 26 Apr 2022 09:22

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