Browsing by Author "Argunsah, Ali Ozgur"

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Citation - WoS: 3
Citation - Scopus: 5
Dendritic Spine Shape Analysis Using Disjunctive Normal Shape Models
(IEEE, 2016) Ghani, Muhammad Usman; Mesadi, Fitsum; Kanik, Sumeyra Demir; Argunsah, Ali Ozgur; Israely, Inbal; Unay, Devrim; Tasdizen, Tolga
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.
Enhancing Two-Photon Images for Anatomical Visualisation Using Super-Resolution
(IEEE, 2022) Aydeniz, Burhan; Metin, Safa Can; Turkan, Mehmet; Unay, Devrim; Karayannis, Theofanis; Argunsah, Ali Ozgur
Two-photon Laser Scanning Microscopy (2P-LSM) is a technique used to image the living tissue with relatively high spatio-temporal resolution. However, the time-series images are often corrupted with Poisson-Gaussian noise and deteriorated with motion artifacts. This paper deals with the problem of enhancing 2P-LSM images to reconstruct high quality and high spatial-resolution outputs using the observed time-series stack of low-resolution images. The proposed technique consists of several components including noise filtering, image registration, cell detection and focus measurement, and clustering for a joint denoising and super-resolution. Extensive experiments demonstrate that the proposed method results in gratifying output images containing apparent and clear cell forms at different focus levels.
Citation - WoS: 12
Citation - Scopus: 14
An interactive time series image analysis software for dendritic spines
(Nature Portfolio, 2022) Argunsah, Ali Ozgur; Erdil, Ertunc; Ghani, Muhammad Usman; Ramiro-Cortes, Yazmin; Hobbiss, Anna F.; Karayannis, Theofanis; Cetin, Mujdat
Live fluorescence imaging has demonstrated the dynamic nature of dendritic spines, with changes in shape occurring both during development and in response to activity. The structure of a dendritic spine correlates with its functional efficacy. Learning and memory studies have shown that a great deal of the information stored by a neuron is contained in the synapses. High precision tracking of synaptic structures can give hints about the dynamic nature of memory and help us understand how memories evolve both in biological and artificial neural networks. Experiments that aim to investigate the dynamics behind the structural changes of dendritic spines require the collection and analysis of large time-series datasets. In this paper, we present an open-source software called SpineS for automatic longitudinal structural analysis of dendritic spines with additional features for manual intervention to ensure optimal analysis. We have tested the algorithm on in-vitro, in-vivo, and simulated datasets to demonstrate its performance in a wide range of possible experimental scenarios.
Citation - WoS: 17
Citation - Scopus: 17
Nonparametric Joint Shape and Feature Priors for Image Segmentation
(IEEE-Inst Electrical Electronics Engineers Inc, 2017) Erdil, Ertunc; Ghani, Muhammad Usman; Rada, Lavdie; Argunsah, Ali Ozgur; Unay, Devrim; Tasdizen, Tolga; Cetin, Mujdat
In many image segmentation problems involving limited and low-quality data, employing statistical prior information about the shapes of the objects to be segmented can significantly improve the segmentation result. However, defining probability densities in the space of shapes is an open and challenging problem, especially if the object to be segmented comes from a shape density involving multiple modes ( classes). Existing techniques in the literature estimate the underlying shape distribution by extending Parzen density estimator to the space of shapes. In these methods, the evolving curve may converge to a shape from a wrong mode of the posterior density when the observed intensities provide very little information about the object boundaries. In such scenarios, employing both shape-and class-dependent discriminative feature priors can aid the segmentation process. Such features may involve, e.g., intensity-based, textural, or geometric information about the objects to be segmented. In this paper, we propose a segmentation algorithm that uses nonparametric joint shape and feature priors constructed by Parzen density estimation. We incorporate the learned joint shape and feature prior distribution into a maximum a posteriori estimation framework for segmentation. The resulting optimization problem is solved using active contours. We present experimental results on a variety of synthetic and real data sets from several fields involving multimodal shape densities. Experimental results demonstrate the potential of the proposed method.