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Publication Metadata only 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; Sabanci University; Bahcesehir University; Fundacao Champalimaud; Izmir Ekonomi Universitesi; Utah System of Higher Education; University of UtahMultimodal 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.Publication Metadata 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 UniversityFunctional 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.Publication Metadata only A JOINT CLASSIFICATION AND SEGMENTATION APPROACH FOR DENDRITIC SPINE SEGMENTATION IN 2-PHOTON MICROSCOPY IMAGES(IEEE, 2015) Erdil, Ertunc; Argunsah, A. Ozgur; Tasdizen, Tolga; Unay, Devrim; Cetin, Mujdat; Sabanci University; Fundacao Champalimaud; Utah System of Higher Education; University of Utah; Bahcesehir UniversityShape priors have been successfully used in challenging biomedical imaging problems. However when the shape distribution involves multiple shape classes, leading to a multimodal shape density, effective use of shape priors in segmentation becomes more challenging. In such scenarios, knowing the class of the shape can aid the segmentation process, which is of course unknown a priori. In this paper, we propose a joint classification and segmentation approach for dendritic spine segmentation which infers the class of the spine during segmentation and adapts the remaining segmentation process accordingly. We evaluate our proposed approach on 2-photon microscopy images containing dendritic spines and compare its performance quantitatively to an existing approach based on nonparametric shape priors. Both visual and quantitative results demonstrate the effectiveness of our approach in dendritic spine segmentation.Publication Metadata 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 UniversitesiSegmentation 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.Publication Metadata 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 UniversityWe 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.Publication Metadata only Concordance Between Computer-based Neuroimaging Findings and Expert Assessments in Dementia Grading(IEEE, 2013) Iheme, Leonardo O.; Unay, Devrim; Baskaya, Osman; Sennaz, Ahmet; Kandemir, Melek; Yalciner, Z. Betul; Tepe, M. Savas; Kahraman, Turgay; Unal, Gozde; Bahcesehir UniversityWe present computer-based techniques for the measurement, detection and quantification of dementia related neuroimaging findings. The three focal points of these are: brain atrophy, lateral ventricle enlargement, and white matter hyperintensity (WMH) lesion detection and quantification. Research has shown that pathological changes in these three areas have strong correlations with dementia. Our proposed methods seek to provide consistent and accurate measures of these pathologies by applying advanced image processing techniques to brain MRIs. The results are evaluated by investigating correlations with experts' grades. We report correlations of up to 0.79, 0.70 and -0.24 for hemispheric cortical atrophy, central sulcus width, and central sulcus depth, respectively. For lateral ventricle (LV) enlargement, we obtain an LV-to-brain ratio correlation of up to 0.81 among other measurements. We automatically segment and quantify periventricular WMH lesions by adaptive thresholding and 3D connectivity analysis on fluid attenuation inversion recovery MRIs. A mean Dice score of 0.83 (standard deviation is 0.06), and a correlation score of up to 0.84 is obtained.Publication Metadata 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 ChampalimaudThe 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.Publication Metadata 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 UniversityAnalysing 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.Publication Metadata only AUTOMATIC DENDRITIC SPINE DETECTION USING MULTISCALE DOT ENHANCEMENT FILTERS AND SIFT FEATURES(IEEE, 2014) Rada, Lavdie; Erdil, Ertunc; Argunsah, A. Ozgur; Unay, Devrim; Cetin, Mujdat; Sabanci University; Fundacao Champalimaud; Bahcesehir UniversityStatistical characterization of morphological changes of dendritic spines is becoming of crucial interest in the field of neurobiology. Automatic detection and segmentation of dendritic spines promises significant reductions on the time spent by the scientists and reduces the subjectivity concerns. In this paper, we present two approaches for automated detection of dendritic spines in 2-photon laser scanning microscopy (2pLSM) images. The first method combines the idea of dot enhancement filters with information from the dendritic skeleton. The second method learns an SVM classifier by utilizing some pre-labeled SIFT feature descriptors and uses the classifier to detect dendritic spines in new images. For the segmentation of detected spines, we employ a watershed-variational segmentation algorithm. We evaluate the proposed approaches by comparing with manual segmentations of domain experts and the results of a non-commercial software, NeuronIQ. Our methods produce promising detection rate with high segmentation accuracy thus can serve as a useful tool for spine analysis.Publication Metadata only DEMENTIA DIAGNOSIS USING SIMILAR AND DISSIMILAR RETRIEVAL ITEMS(IEEE, 2011) Unay, Devrim; Ekin, Ahmet; Bahcesehir University; Philips; Philips ResearchImage-based disease diagnosis often requires radiologists' qualitative interpretations that are highly dependent on their levels of expertise, and physical and mental status. Automated image analysis tools can help radiologists increase the diagnosis accuracy by providing quantitative measures. Accordingly, this paper presents an automated method for dementia diagnosis using search and retrieval of brain MR (magnetic resonance) images. The main contributions of the method are 1) its generic decision-making based on similar and dissimilar cases retrieved from a database that can be used to diagnose various brain disorders, 2) it realizes dementia diagnosis utilizing a tailored version of histogram of oriented gradients (HOGs) as features, and 3) it achieves high performance in dementia diagnosis that is independent of the database size. Comprehensive experiments with real data showed that combining information from similar and dissimilar cases leads to improved diagnostic accuracy than using similar or dissimilar cases alone, accuracy diminishes with extreme quantization of the HOGs and with small databases, and our method achieves high performance with comparable sensitivity score to the most skilled experts at better specificity figures.