Browsing by Author "Ghani, Muhammad Usman"

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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.
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.
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.