Araştırma Çıktıları | WoS | Scopus | TR-Dizin | PubMed

Permanent URI for this communityhttps://hdl.handle.net/20.500.14719/1741

Browse

Filters

2013 - 20175

Settings

Start date: 2010End date: 2019Type: search.filters.itemtype.Proceedings PaperSubject: search.filters.subject.TelecommunicationsAuthor: search.filters.author.Cetin, Mujdat

Search Results

Now showing 1 - 5 of 5
  • No Thumbnail Available
    PublicationMetadata only
    Dendritic Spine Shape Classification from Two-Photon Microscopy Images
    (IEEE, 2015) Ghani, Muhammad Usman; Kanik, Sumeyra Demir; Argunsah, Ali Ozgur; Tasdizen, Tolga; Unay, Devrim; Cetin, Mujdat; Sabanci University; Fundacao Champalimaud; Utah System of Higher Education; University of Utah; Bahcesehir University
    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.
  • No Thumbnail Available
    PublicationMetadata only
    Coupled Shape Priors for Dynamic Segmentation of Dendritic Spines
    (IEEE, 2017) Atabakilachini, Naeimeh; Erdil, Ertunc; Argunsah, A. Ozgur; Rada, Lavdie; Unay, Devrim; Cetin, Mujdat; Sabanci University; University of Zurich; Bahcesehir University; Izmir Ekonomi Universitesi
    Segmentation of biomedical images is a challenging task, especially when there is low quality or missing data. The use of prior information can provide significant assistance for obtaining more accurate results. In this paper we propose a new approach for dendritic spine segmentation from microscopic images over time, which is motivated by incorporating shape information from previous time points to segment a spine in the current time point. In particular, using a training set consisting of spines in two consecutive time points to construct coupled shape priors, and given the segmentation in the previous time point, we can improve the segmentation process of the spine in the current time point. Our approach has been evaluated on 2-photon microscopy images of dendritic spines and its effectiveness has been demonstrated by both visual and quantitative results.
  • No Thumbnail Available
    PublicationMetadata only
    Biomedical image time series registration with particle filtering
    (IEEE, 2013) Yagci, A. Murat; Erdil, Ertunc; Argunsah, A. Ozgur; Unay, Devrim; Cetin, Mujdat; Akarun, Lale; Gurgen, Fikret; Bogazici University; Sabanci University; Fundacao Champalimaud; Bahcesehir University
    We propose a family of methods for biomedical image time series registration based on Particle filtering. The first method applies an intensity-based information-theoretic approach to calculate importance weights. An effective second group of methods use landmark-based approaches for the same purpose by automatically detecting intensity maxima or SIFT interest points from image time series. A brute-force search for the best alignment usually produces good results with proper cost functions, but becomes computationally expensive if the whole search space is explored. Hill climbing optimizations seek local optima. Particle filtering avoids local solutions by introducing randomness and sequentially updating the posterior distribution representing probable solutions. Thus, it can be more robust for the registration of image time series. We show promising preliminary results on dendrite image time series.
  • No Thumbnail Available
    PublicationMetadata only
    Automated Dendritic Spine Tracking on 2-Photon Microscopic Images
    (IEEE, 2015) Kilic, Bike; Rada, Lavdie; Erdil, Ertunc; Argunsah, A. Ozgur; Cetin, Mujdat; Unay, Devrim; Bahcesehir University; Sabanci University; Fundacao Champalimaud
    The rapid and spontaneous morphological changes of dendritic spines have been an important observation to understand how information is stored in brain. Manual assessment of spine structure has been a useful tool to understand the differences between wild type (normal) and diseased cases. In order to perform a more through analysis, automatic tools need to be developed due to the immense amount of image data collected throughout the experiments. Additionally, dendritic spines are very dynamic structures and florescence microscopy contains high level of noise, blur and shift due to the optical properties. In this study, we track locations of dendritic spines in a full series of a time-lapse two photon microscopic images. To achieve this we propose a combined detection and tracking framework. For the detection we use a SIFT based algorithm, while the tracking requires a combination of registration and distance based spine matching. Experimental results show that this technique helps to track detected spines in time series even though the noise or blur deformed the image and complicated the detection.
  • No Thumbnail Available
    PublicationMetadata only
    A Watershed and Active Contours Based Method for Dendritic Spine Segmentation in 2-Photon Microscopy Images
    (IEEE, 2013) Erdil, Ertunc; Argunsah, A. Ozgur; Unay, Devrim; Cetin, Mujdat; Sabanci University; Fundacao Champalimaud; Bahcesehir University
    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.