Ali Özgür Argunşah
Sabancı University
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Featured researches published by Ali Özgür Argunşah.
international conference on pattern recognition | 2010
Ali Özgür Argunşah; Müjdat Çetin
We propose an approach based on Hidden Markov models (HMMs) combined with principal component analysis (PCA) for classification of four-class single trial motor imagery EEG data for brain computer interfacing (BCI) purposes. We extract autoregressive (AR) parameters from EEG data and use PCA to decrease the number of features for better training of HMMs. We present experimental results demonstrating the improvements provided by our approach over an existing HMM-based EEG single trial classification approach as well as over state-of-the-art classification methods.
signal processing and communications applications conference | 2007
Batu Akan; Ali Özgür Argunşah
In the recent years one of the popular ways for human computer interface (HCI) systems are processing of electrophysiological signals. In this project a computer human interface system has been developed for those who cannot speak or use hands (hemiparesis, quadriplegia or ALS) using electrophysiological genereted by the voluntary contradictions of the muscles around the eye. Eye movements are resposible for the movement of the cursor whereas the signals generated by the succesive blinks of the eye are used for simulating decision making such as mouse clicks. The developed system In the project, a text editor has been developed which is operated using EOG signals.
signal processing and communications applications conference | 2015
Muhammad Usman Ghani; Sümeyra Ümmühan Demir Kanık; Ali Özgür Argunşah; Tolga Tasdizen; Devrim Unay; Müjdat Çetin
Functional properties of a neuron are coupled with its morphology, particularly the morphology of dendritic spines. Spine volume has been used as the primary morphological parameter in order the characterize the structure and function coupling. However, this reductionist approach neglects the rich shape repertoire of dendritic spines. First step to incorporate spine shape information into functional coupling is classifying main spine shapes that were proposed in the literature. Due to the lack of reliable and fully automatic tools to analyze the morphology of the spines, such analysis is often performed manually, which is a laborious and time intensive task and prone to subjectivity. In this paper we present an automated approach to extract features using basic image processing techniques, and classify spines into mushroom or stubby by applying machine learning algorithms. Out of 50 manually segmented mushroom and stubby spines, Support Vector Machine was able to classify 98% of the spines correctly.
Journal of Neuroscience Methods | 2017
Muhammad Usman Ghani; Fitsum Mesadi; Sümeyra Ümmühan Demir Kanık; Ali Özgür Argunşah; Anna Felicity Hobbiss; Inbal Israely; Devrim Unay; Tolga Tasdizen; Müjdat Çetin
BACKGROUND Neuronal morphology and function are highly coupled. In particular, dendritic spine morphology is strongly governed by the incoming neuronal activity. The first step towards understanding the structure-function relationships is to classify spine shapes into the main spine types suggested in the literature. Due to the lack of reliable automated analysis tools, classification is mostly performed manually, which is a time-intensive task and prone to subjectivity. NEW METHOD We propose an automated method to classify dendritic spines using shape and appearance features based on challenging two-photon laser scanning microscopy (2PLSM) data. Disjunctive Normal Shape Models (DNSM) is a recently proposed parametric shape representation. We perform segmentation of spine images by applying DNSM and use the resulting representation as shape features. Furthermore, we use Histogram of oriented gradients (HOG) to extract appearance features. In this context, we propose a kernel density estimation (KDE) based framework for dendritic spine classification, which uses these shape and appearance features. RESULTS Our shape and appearance features based approach combined with Neural Network (NN) correctly classifies 87.06% of spines on a dataset of 456 spines. COMPARISON WITH EXISTING METHODS Our proposed method outperforms standard morphological feature based approaches. Our KDE based framework also enables neuroscientists to analyze the separability of spine shape classes in the likelihood ratio space, which leads to further insights about nature of the spine shape analysis problem. CONCLUSIONS Results validate that performance of our proposed approach is comparable to a human expert. It also enable neuroscientists to study shape statistics in the likelihood ratio space.
international symposium on biomedical imaging | 2016
Muhammad Usman Ghani; Fitsum Mesadi; Sümerya Ümmühan Demir Kanık; Ali Özgür Argunşah; Inbal Israely; Devrim Unay; Tolga Tasdizen; Müjdat Çetin
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.
Archive | 2009
Hüseyin Abut; Hakan Erdogan; Aytül Erçil; Baran Çürüklü; Hakkı Can Koman; Fatih Taş; Ali Özgür Argunşah; Serhan Cosar; Batu Akan; Harun Karabalkan; Emrecan Çökelek; Rahmi Fıçıcı; Volkan Sezer; Serhan Danis; Mehmet Karaca; Mehmet Abbak; Mustafa Gökhan Uzunbas; Kayhan Eritmen; Mümin Imamoğlu; Cagatay Karabat
In this chapter, we present data collection activities and preliminary research findings from the real-world database collected with “UYANIK,” a passenger car instrumented with several sensors, CAN-Bus data logger, cameras, microphones, data acquisitions systems, computers, and support systems. Within the shared frameworks of Drive-Safe Consortium (Turkey) and the NEDO (Japan) International Collaborative Research on Driving Behavior Signal Processing, close to 16 TB of driver behavior, vehicular, and road data have been collected from more than 100 drivers on a 25 km route consisting of both city roads and The Trans-European Motorway (TEM) in Istanbul, Turkey. Challenge of collecting data in a metropolis with around 12 million people and famous with extremely limited infrastructure yet driving behavior defying all rules and regulations bordering madness could not be “painless.” Both the experience gained and the preliminary results from still on-going studies using the database are very encouraging and give comfort.
international symposium on biomedical imaging | 2016
Muhammad Usman Ghani; Ali Özgür Argunşah; Inbal Israely; Devrim Unay; Tolga Tasdizen; Müjdat Çetin
Dendritic spines are one of the key functional components of neurons. Their morphological changes are correlated with neuronal activity. Neuroscientists study spine shape variations to understand their relation with neuronal activity. Currently this analysis performed manually, the availability of reliable automated tools would assist neuroscientists and accelerate this research. Previously, morphological features based spine analysis has been performed and reported in the literature. In this paper, we explore the idea of using and comparing manifold learning techniques for classifying spine shapes. We start with automatically segmented data and construct our feature vector by stacking and concatenating the columns of images. Further, we apply unsupervised manifold learning algorithms and compare their performance in the context of dendritic spine classification. We achieved 85.95% accuracy on a dataset of 242 automatically segmented mushroom and stubby spines. We also observed that ISOMAP implicitly computes prominent features suitable for classification purposes.
european conference on computer vision | 2016
Muhammad Usman Ghani; Ertunc Erdil; Sümeyra Ümmühan Demir Kanık; Ali Özgür Argunşah; Anna Felicity Hobbiss; Inbal Israely; Devrim Unay; Tolga Tasdizen; Müjdat Çetin
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.
IEEE Transactions on Image Processing | 2017
Ertunc Erdil; Muhammad Usman Ghani; Lavdie Rada; Ali Özgür Argunşah; Devrim Unay; Tolga Tasdizen; Müjdat Çetin
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.
signal processing and communications applications conference | 2013
Ertunc Erdil; Ali Özgür Argunşah; Devrim Unay; Müjdat Çetin
Analysing morphological and volumetric properties of dendritic spines from 2-photon microscopy images has been of interest to neuroscientists in recent years. Developing robust and reliable tools for automatic analysis depends on the segmentation quality. In this paper, we propose a new segmentation algorithm for dendritic spine segmentation based on watershed and active contour methods. First, our proposed method coarsely segments the dendritic spine area using the watershed algorithm. Then, these results are further refined using a region-based active contour approach. We compare our results and the results of existing methods in the literature to manual delineations of a domain expert. Experimental results demonstrate that our proposed method produces more accurate results than the existing algorithms proposed for dendritic spine segmentation.