Browsing by Author "Kanik, Sumeyra Demir"

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Citation - WoS: 36
Citation - Scopus: 36
Dendritic Spine Classification Using Shape and Appearance Features Based on Two-Photon Microscopy
(Elsevier, 2017) Ghani, Muhammad Usman; Mesadi, Fitsum; Kanik, Sumeyra Demir; Argunsah, Ali Oezguer; Hobbiss, Anna Felicity; Israely, Inbal; Unay, Devrim
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. (C) 2017 Elsevier B.V. All rights reserved.
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.