Browsing by Author "Tasdizen, Tolga"

Now showing 1 - 4 of 4

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.
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.
Citation - WoS: 3
Citation - Scopus: 3
Nonparametric Joint Shape and Feature Priors for Segmentation of Dendritic Spines
(IEEE, 2016) Erdil, Ertunc; Rada, Lavdie; Argunsah, A. Ozgur; Israely, Inbal; Unay, Devrim; Tasdizen, Tolga; Cetin, Mujdat
Multimodal shape density estimation is a challenging task in many biomedical image segmentation problems. Existing techniques in the literature estimate the underlying shape distribution by extending Parzen density estimator to the space of shapes. Such density estimates are only expressed in terms of distances between shapes which may not be sufficient for ensuring accurate segmentation when the observed intensities provide very little information about the object boundaries. In such scenarios, employing additional shape-dependent discriminative features as priors and exploiting both shape and feature priors can aid to the segmentation process. In this paper, we propose a segmentation algorithm that uses nonparametric joint shape and feature priors using Parzen density estimator. The joint prior density estimate is expressed in terms of distances between shapes and distances between features. 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 dendritic spine segmentation in 2-photon microscopy images which involve a multimodal shape density.
Citation - WoS: 1
Citation - Scopus: 5
On Comparison of Manifold Learning Techniques for Dendritic Spine Classification
(IEEE, 2016) Ghani, Muhammad Usman; Argunsach, Ali Ozgur; Israely, Inbal; Unay, Devrim; Tasdizen, Tolga; Cetin, Mujdat
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.