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3D dendritic spine segmentation using nonparametric shape priors (3B dendritik dikenlerin parametrik olmayan şekil ön bilgisi kullanılarak bölütlenmesi)

Böcügöz, Erdem and Erdil, Ertunç and Argunşah, Ali Özgür and Ünay, Devrim and Çetin, Müjdat (2017) 3D dendritic spine segmentation using nonparametric shape priors (3B dendritik dikenlerin parametrik olmayan şekil ön bilgisi kullanılarak bölütlenmesi). In: 25th Signal Processing and Communications Applications Conference (SIU 2017), Antalya, Turkey

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Official URL: http://dx.doi.org/10.1109/SIU.2017.7960482

Abstract

Analyzing morphological and structural changes of dendritic spines in 2-photon microscopy images in time is important for neuroscience researchers. Correct segmentation of dendritic spines is an important step of developing robust and reliable automatic tools for such analysis. In this paper, we propose an approach for segmentation of 3D dendritic spines using nonparametric shape priors. The proposed method learns the prior distribution of shapes through Parzen density estimation on the training set of shapes. Then, the posterior distribution of shapes is obtained by combining the learned prior distribution with a data term in a Bayesian framework. Finally, the segmentation result that maximizes the posterior is found using active contours. Experimental results demonstrate that using nonparametric shape priors leads to better 3D dendritic spine segmentation results.

Item Type:Papers in Conference Proceedings
Uncontrolled Keywords:3D dendritic spine segmentation; level sets; nonparametric shape priors; Parzen density estimator
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
ID Code:33851
Deposited By:Müjdat Çetin
Deposited On:11 Sep 2017 12:48
Last Modified:11 Sep 2017 12:48

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