Browsing by Author "Erdil E."

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3D dendritic spine segmentation using nonparametric shape priors
(Institute of Electrical and Electronics Engineers Inc., 2017) Bocugoz E.; Erdil E.; Argunsah A.O.; Unay D.; Cetin M.
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. © 2017 IEEE.
Citation - WoS: 2
Citation - Scopus: 3
Combining Nonparametric Spatial Context Priors With Nonparametric Shape Priors for Dendritic Spine Segmentation in 2-Photon Microscopy Images
(IEEE Computer Society, 2019) Erdil E.; Ozgurargunsah A.; Tasdizen T.; Unay D.; Cetin M.
Data driven segmentation is an important initial step of shape prior-based segmentation methods since it is assumed that the data term brings a curve to a plausible level so that shape and data terms can then work together to produce better segmentations. When purely data driven segmentation produces poor results, the final segmentation is generally affected adversely. One challenge faced by many existing data terms is due to the fact that they consider only pixel intensities to decide whether to assign a pixel to the foreground or to the background region. When the distributions of the foreground and back-ground pixel intensities have significant overlap, such data terms become ineffective, as they produce uncertain results for many pixels in a test image. In such cases, using prior information about the spatial context of the object to be segmented together with the data term can bring a curve to a plausible stage, which would then serve as a good initial point to launch shape-based segmentation. In this paper, we propose a new segmentation approach that combines nonparametric context priors with a learned-intensity-based data term and nonparametric shape priors. We perform experiments for dendritic spine segmentation in both 2 D and 3 D 2-photon microscopy images. The experimental results demonstrate that using spatial context priors leads to significant improvements. © 2019 IEEE.
Coupled Shape Priors for Dynamic Segmentation of Dendritic Spines
(Institute of Electrical and Electronics Engineers Inc., 2017) Atabakilachini N.; Erdil E.; Argunsah A.O.; Rada L.; Unay D.; Cetin M.
Segmentation of biomedical images is a challenging task, especially when there is low quality or missing data. The use of prior information can provide significant assistance for obtaining more accurate results. In this paper we propose a new approach for dendritic spine segmentation from microscopic images over time, which is motivated by incorporating shape information from previous time points to segment a spine in the current time point. In particular, using a training set consisting of spines in two consecutive time points to construct coupled shape priors, and given the segmentation in the previous time point, we can improve the segmentation process of the spine in the current time point. Our approach has been evaluated on 2-photon microscopy images of dendritic spines and its effectiveness has been demonstrated by both visual and quantitative results. © 2017 IEEE.
Citation - Scopus: 5
Dendritic Spine Shape Analysis: a Clustering Perspective
(Springer Verlag, 2016) Ghani M.U.; Erdil E.; Kanık S.D.; Argunşah A.Ö.; Hobbiss A.F.; Israely I.; Ünay D.
Functional properties of neurons are strongly coupled with their morphology. Changes in neuronal activity alter morphological characteristics of dendritic spines. First step towards understanding the structure-function relationship is to group spines into main spine classes reported in the literature. Shape analysis of dendritic spines can help neuroscientists understand the underlying relationships. Due to unavailability of reliable automated tools, this analysis is currently performed manually which is a time-intensive and subjective task. Several studies on spine shape classification have been reported in the literature, however, there is an on-going debate on whether distinct spine shape classes exist or whether spines should be modeled through a continuum of shape variations. Another challenge is the subjectivity and bias that is introduced due to the supervised nature of classification approaches. In this paper, we aim to address these issues by presenting a clustering perspective. In this context, clustering may serve both confirmation of known patterns and discovery of new ones. We perform cluster analysis on two-photon microscopic images of spines using morphological, shape, and appearance based features and gain insights into the spine shape analysis problem. We use histogram of oriented gradients (HOG), disjunctive normal shape models (DNSM), morphological features, and intensity profile based features for cluster analysis.We use x-means to perform cluster analysis that selects the number of clusters automatically using the Bayesian information criterion (BIC). For all features, this analysis produces 4 clusters and we observe the formation of at least one cluster consisting of spines which are difficult to be assigned to a known class. This observation supports the argument of intermediate shape types. © Springer International Publishing Switzerland 2016.
Industrial Dynamics, Innovation Policy, and Economic Growth Through Technological Advancements
(IGI Global, 2012) Yetkiner, İ Hakan; Pamukcu M.T.; Erdil E.
By harnessing technological progress, good innovation policies can help enhance economic growth. New research offers additional insights into the design and application of such innovative policies. Industrial Dynamics, Innovation Policy, and Economic Growth through Technological Advancements examines the nature of the process of technological change in different sectors of an array of countries, analyzing the impact of innovation as well as research and development activities on different outcomes in different fields and assessing the design and impact of policies aimed at enhancing innovativeness of firms. The analyses and findings of the studies in this book contribute to the advancement of knowledge in the field of industrial dynamics, innovation policies, and economic growth. © 2013, by IGI Global. All rights reserved.
Preface
(IGI Global, 2012) Yetkiner, İ Hakan; Pamukcu M.T.; Erdil E.
[No abstract available]