Browsing by Subject "Segmentation"

Now showing 1 - 20 of 20

Open Access
Adaptive reconstruction for vessel preservation in unenhanced MR angiography
(IEEE, 2016) Ilıcak, Efe; Çetin, S.; Sarıtaş, Emine Ülkü; Ünal, G.; Çukur, Tolga
The image quality of unenhanced magnetic resonance angiography, which images blood vessels without contrast agents, is limited by constraints related to scan time. To address this problem, techniques that undersample angiographic data and then apply regularized reconstructions are used. Conventional reconstructions employ regularization terms with uniform spatial weighting. Thus, they can yield improper suppression of aliasing artifacts and poor blood/background contrast. In this study, a reconstruction strategy is evaluated that applies spatially-adaptive regularization based on vessel maps obtained via a tractographic segmentation. This strategy is compared with conventional methods in terms of peak signal to noise ratio, structural similarity and contrast.
Open Access
Automatic detection of compound structures by joint selection of region groups from a hierarchical segmentation
(Institute of Electrical and Electronics Engineers, 2016) Akçay, H. G.; Aksoy, S.
A challenging problem in remote sensing image analysis is the detection of heterogeneous compound structures such as different types of residential, industrial, and agricultural areas that are composed of spatial arrangements of simple primitive objects such as buildings and trees. We describe a generic method for the modeling and detection of compound structures that involve arrangements of an unknown number of primitives in large scenes. The modeling process starts with a single example structure, considers the primitive objects as random variables, builds a contextual model of their arrangements using a Markov random field, and learns the parameters of this model via sampling from the corresponding maximum entropy distribution. The detection task is formulated as the selection of multiple subsets of candidate regions from a hierarchical segmentation where each set of selected regions constitutes an instance of the example compound structure. The combinatorial selection problem is solved by the joint sampling of groups of regions by maximizing the likelihood of their individual appearances and relative spatial arrangements. Experiments using very high spatial resolution images show that the proposed method can effectively localize an unknown number of instances of different compound structures that cannot be detected by using spectral and shape features alone.
Open Access
Building detection using directional spatial constraints
(IEEE, 2010) Akçay, H. Gökhan; Aksoy, Selim
We propose an algorithm for automatic detection of buildings with complex shapes and roof structures in very high spatial resolution remotely sensed images. First, an initial oversegmentation is obtained. Then, candidate building regions are found using shadow and sun azimuth angle information. Finally, the building regions are selected by clustering the candidate regions using minimum spanning trees. The experiments on Ikonos scenes show that the algorithm is able to detect buildings with complex appearances and shapes. © 2010 IEEE.
Open Access
Consumer behavior analysis and marketing communications strategy development: the case of Tuborg
(1995) Deniz, Mehmet Ali
Efes Pilsen and Tuborg have enjoyed an oligopolistic market stiiicture m the Turkish Beer Industry for decades. This has caused the companies to adopt a sales and product orientation and exert little effort on marketing. However, the market conditions and competition have begun to change recently, by the recent introduction of a new brand, feasibility studies of world giants to enter the Turkish market, and the Customs Union which will decrease the customs tax on import beer. On the other hand, Efes Pilsen has entered into market development efforts in foreign markets. The above competitive moves in the industry by various actors have changed the long-prevailing competitive structure in the industry, especially for the disadvantage of Tuborg. Thus, in this thesis, it is argued that the study of the consumer behavior in the Turkish Beer Market, that has long been underestimated by the agents in the industry, is crucial for Tuborg to compete the changes in the market and find differentiation points that are significant in the eyes of the consumers. It is also discussed that the adoption of a consumer orientation, which takes the current needs and perceptions of the consumers into consideration, is crucial for the success of the communications strategy, which is a sustainable differentiation factor. Therefore, a marketing research has been conducted for the beer market (taking Ankara as a pilot region for application) in order to better understand the needs of the consumers, to find out significant differences as well as similarities among the consumers. Depending on the marketing research conducted, a communications strategy has been suggested for Tuborg . This strategy has been designed to serve to differentiate the brand in the market through communications and also to constitute an entry barrier against the new entrants. On the other hand, the necessary adaptations in the organizational structure of Tuborg to the suggested consumer orientation have also been discussed within the thesis.
Open Access
Cross-document word matching for segmentation and retrieval of Ottoman divans
(Springer U K, 2016) Duygulu, P.; Arifoglu, D.; Kalpakli, M.
Motivated by the need for the automatic indexing and analysis of huge number of documents in Ottoman divan poetry, and for discovering new knowledge to preserve and make alive this heritage, in this study we propose a novel method for segmenting and retrieving words in Ottoman divans. Documents in Ottoman are difficult to segment into words without a prior knowledge of the word. In this study, using the idea that divans have multiple copies (versions) by different writers in different writing styles, and word segmentation in some of those versions may be relatively easier to achieve than in other versions, segmentation of the versions (which are difficult, if not impossible, with traditional techniques) is performed using information carried from the simpler version. One version of a document is used as the source dataset and the other version of the same document is used as the target dataset. Words in the source dataset are automatically extracted and used as queries to be spotted in the target dataset for detecting word boundaries. We present the idea of cross-document word matching for a novel task of segmenting historical documents into words. We propose a matching scheme based on possible combinations of sequence of sub-words. We improve the performance of simple features through considering the words in a context. The method is applied on two versions of Layla and Majnun divan by Fuzuli. The results show that, the proposed word-matching-based segmentation method is promising in finding the word boundaries and in retrieving the words across documents. © 2014, Springer-Verlag London.
Open Access
Do university students really need to be taught by the best instructors to learn?
(Cogent OA, 2017) Kalender, I.
The present study sought to contribute to the discussion on linearity relationship between teaching and learning at university level. Although the basic assumption that students who are taught by effective instructors learn better is acknowledged, defining the effective instructor seems not so simple. This study attempted to (i) cluster instructors with respect to instructional practices rated by students, and (ii) identify different instructional profiles that may be associated with high learning, rather than just focusing on relationship between instructional practices and learning. Using student ratings from 625 courses in a university setting, subgroups were defined in terms of instructional practices via a segmentation approach. Then, distinct profiles showing high instructional effectiveness were extracted by investigating learning level differences as measured by the end-of-semester grades and self-reported learning levels. Results indicated that the students need not to be taught by the best instructors to reach high learning levels. Effective learning can also take place under lack of some aspects of instructional practices if other aspects receive higher ratings to compensate for the missing aspects.
Open Access
The emergence and evolution of a politicized market : the production and circulation of Kurdish music Turkey
(2015) Kuruoğlu, Alev Pınar
This dissertation explicates the emergence and evolution of a market for Kurdish music in Turkey. Using ethnographic methods, I start by detailing the illegal circulation of cassettes during the restrictive and strife-laden period of the 1970s, 80s, and 90s. Through the resistive practices of circulation - recording, hiding, playing, and exchanging cassettes – cassettes became saturated with emotions, established shared emotional repertoires, and habituated individuals and collectives into common emotional dispositions. An emotional structure was generated, and accompanied the emergence of a sense of “us,” the delineation of the “other,” and the resistive relationship between the two. I thus demonstrate the entwinement of materiality with emotions, and the structuring potentiality that this entwinement generates. In the second part, I ethnographically explore the trajectory of the market after legalization in 1991. Situated within a context characterized by the sociopolitical dynamics of domination and stigmatization, I detail how market producers collectively construct an oppositional “market culture” by framing their marketrelated experiences, as well as by interacting with and borrowing ideological codes from the neighboring Kurdish political movement. These frames become entrenched as a political-normative logic, shaping artistic production and business decisions. This emergent logic negotiates societal-level conflict and stigma, and also resolves the market-level tension between artistic and commercial concerns. Finally, I explore the segmentation of the market in conjunction with changes in the socio-political atmosphere in the 2000s. I discuss how segmentation also corresponds to competing social imaginaries of a Kurdish public.
Open Access
GRJointNET: 3B eksik nokta bulutları için sinerjistik tamamlama ve parça bölütleme
(IEEE, 2021-07-19) Gürses, Yiğit; Taşpınar, Melisa; Yurt, Mahmut; Özer, Sedat
Üç boyutlu (3B) nokta bulutları üzerinde bölütleme yapmak, otonom sistemler için önemli ve gerekli bir işlemdir. Bölütleme algoritmalarının başarısı, üzerinde işlem yapılan nokta bulutlarının niteliğine (çözünürlük, tamlık vb.) bağlıdır. Dolayısıyla, nokta bulutundaki mevcut eksiklikler, nokta bulutu tabanlı uygulamaların başarısını düşürmektedir. Bu konuda, güncel bir çalısma olan GRNet, eksik nokta bulutlarını tamamlamaya odaklanan başarılı bir algoritmadır, ancak bölütleme yeteneği yoktur. Biz bu çalışmada, GRNet üzerine geliştirdigimiz derin öğrenme tabanlı GRJointNet algoritmasını sunmaktayız. GRJointNet hem bir nokta bulutundaki eksik noktaları tamamlamakta, hem de onun yapamadığı parça bölütlemesini de yapmaktadır. Bu işlemler elde ettikleri verileri birbirlerini desteklemek için kullanmaktadır. ShapeNet-Part veri kümesinde yapılmış deneylerimiz, GRJointNet algoritmasının nokta bulutu tamamlamada GRNet’den daha başarılı olduğunu göstermektedir. Aynı zamanda, GRNet bölütleme yapamazken, GRJointNet bu özelliği de kazanmıştır. Dolayısıyla bu çalışma nokta bulutlarının 3B bilgisayarlı görüde kullanışlılığını arttırmak adına umut vadetmektedir.
Open Access
Heart sound segmentation using signal processing methods
(2015) Şahin, Devrim
Heart murmurs are pathological heart sounds that originate from blood flowing with abnormal turbulence due to physiological defects of the heart, and are the prime indicator of many heart-related diseases. Murmurs can be diagnosed via auscultation; that is, by listening with a stethoscope. However, manual detection and classification of murmur requires clinical expertise and is highly prone to misclassification. Although automated classification algorithms exist for this purpose; they heavily depend on feature extraction from ‘segmented’ heart sound waveforms. Segmentation in this context refers to detecting and splitting cardiac cycles. However, heart sound signal is not a stationary signal; and typically has a low signal-to-noise ratio, which makes it very difficult to segment using no external information but the signal itself. Most of the commercial systems require an external electrocardiography (ECG) signal to determine S1 and S2 peaks, but ECG is not as widely available as stethoscopes. Although algorithms that provide segmentation using sound alone exist, a proper comparison between these algorithms on a common dataset is missing. We propose several modifications to many of these algorithms, as well as an evaluation method that allows a unified comparison of all these approaches. We have tested each combination of algorithms on a real data set [1], which also provides manual annotations as ground truth. We also propose an ensemble of several methods, and a heuristic for which algorithm’s output to use. Whereas tested algorithms report up to 62% accuracy, our ensemble method reports a 75% success rate. Finally, we created a tool named UpBeat to enable manual segmentation of heart sounds, and construction of a ground truth dataset. UpBeat is a starting medium for auscultation segmentation, time-domain based feature extraction and evaluation; which has automatic segmentation capabilities, as well as a minimalistic drag-and-drop interface which allows manual annotation of S1 and S2 peaks.
Open Access
Image classification of human carcinoma cells using complex wavelet-based covariance descriptors
(Public Library of Science, 2013-01-16) Keskin, F.; Suhre, A.; Kose, K.; Ersahin, T.; Çetin, A. Enis; Cetin Atalay, R.
Cancer cell lines are widely used for research purposes in laboratories all over the world. Computer-assisted classification of cancer cells can alleviate the burden of manual labeling and help cancer research. In this paper, we present a novel computerized method for cancer cell line image classification. The aim is to automatically classify 14 different classes of cell lines including 7 classes of breast and 7 classes of liver cancer cells. Microscopic images containing irregular carcinoma cell patterns are represented by subwindows which correspond to foreground pixels. For each subwindow, a covariance descriptor utilizing the dual-tree complex wavelet transform (DT-CWT) coefficients and several morphological attributes are computed. Directionally selective DT-CWT feature parameters are preferred primarily because of their ability to characterize edges at multiple orientations which is the characteristic feature of carcinoma cell line images. A Support Vector Machine (SVM) classifier with radial basis function (RBF) kernel is employed for final classification. Over a dataset of 840 images, we achieve an accuracy above 98%, which outperforms the classical covariance-based methods. The proposed system can be used as a reliable decision maker for laboratory studies. Our tool provides an automated, time-and cost-efficient analysis of cancer cell morphology to classify different cancer cell lines using image-processing techniques, which can be used as an alternative to the costly short tandem repeat (STR) analysis. The data set used in this manuscript is available as supplementary material through http://signal.ee.bilkent.edu.tr/cancerCellLineClassificationSampleImages.html.
Open Access
MTFD-Net: left atrium segmentation in CT images through fractal dimension estimation
(Elsevier BV * North-Holland, 2023-08-18) Saber Jabdaragh, Aziza; Firouznia, M.; Faez, K.; Alikhani, F.; Alikhani Koupaei, J.; Gündüz-Demir, Ç.
Multi-task learning proved to be an effective strategy to increase the performance of a dense prediction network on a segmentation task, by defining auxiliary tasks to reflect different aspects of the problem and concurrently learning them with the main task of segmentation. Up to now, previous studies defined their auxiliary tasks in the Euclidean space. However, for some segmentation tasks, the complexity and high variation in the texture of a region of interest may not follow the smoothness constraint in the Euclidean geometry. This paper addresses this issue by introducing a new multi-task network, MTFD-Net, which utilizes the fractal geometry to quantify texture complexity through self-similar patterns in an image. To this end, we propose to transform an image into a map of fractal dimensions and define its learning as an auxiliary task, which will provide auxiliary supervision to the main segmentation task, towards betterment of left atrium (LA) segmentation in computed tomography (CT) images. To the best of our knowledge, this is the first proposal of a dense prediction network that employs the fractal geometry to define an auxiliary task and learns it in parallel to the segmentation task in a multi-task learning framework. Our experiments revealed that the proposed MTFD-Net model led to more accurate LA segmentations compared to its counterparts.
Open Access
Multi-task network for computed tomography segmentation through fractal dimension estimation
(2023-01) Jabdaragh, Aziza Saber
Multi-task learning proved to be an effective strategy to increase the performance of a dense prediction network on a segmentation task, by defining auxiliary tasks to reflect different aspects of the problem and concurrently learning them with the main task of segmentation. Up to now, previous studies defined their auxiliary tasks in the Euclidean space. However, for some segmentation tasks, the complexity and high variation in the texture of a region of interest may not follow the smoothness constraint in the Euclidean geometry. This thesis addresses this issue by introducing a new multi-task network, MTFD-Net, which utilizes the fractal geometry to quantify texture complexity through self-similar patterns in an image. To this end, we propose to transform an image into a map of fractal dimensions and define its learning as an auxiliary task, which will provide auxiliary supervision to the main segmentation task, towards betterment of left atrium segmentation in computed tomography images. To the best of our knowledge, this is the first proposal of a dense prediction network that employs the fractal geometry to define an auxiliary task and learns it in parallel to the segmentation task in a multi-task learning framework. Our experiments revealed that the proposed MTFD-Net model led to more accurate left atrium segmentation, compared to its counterparts.
Open Access
Object-based 3-d motion and structure analysis for video coding applications
(1997) Alatan, A. Aydin
Novel 3-D motion analysis tools, which can be used in object-based video codecs, are proposed. In these tools, the movements of the objects, which are observed through 2-D video frames, are modeled in 3-D space. Segmentation of 2-D frames into objects and 2-D dense motion vectors for each object are necessary as inputs for the proposed 3-D analysis. 2-D motion-based object segmentation is obtained by Gibbs formulation; the initialization is achieved by using a fast graph-theory based region segmentation algorithm which is further improved to utilize the motion information. Moreover, the same Gibbs formulation gives the needed dense 2-D motion vector field. The formulations for the 3-D motion models are given for both rigid and non- rigid moving objects. Deformable motion is modeled by a Markov random field which permits elastic relations between neighbors, whereas, rigid 3-D motion parameters are estimated using the E-matrix method. Some improvements on the E-matrix method are proposed to make this algorithm more robust to gross errors like the consequence of incorrect segmentation of 2-D correspondences between frames. Two algorithms are proposed to obtain dense depth estimates, which are robust to input errors and suitable for encoding, respectively. While the former of these two algorithms gives simply a MAP estimate, the latter uses rate-distortion theory. Finally, 3-D motion models are further utilized for occlusion detection and motion compensated temporal interpolation, and it is observed that for both applications 3-D motion models have superiority over their 2-D counterparts. Simulation results on artificial and real data show the advantages of the 3-D motion models in object-based video coding algorithms.
Open Access
RadGT: graph and transformer-based automotive radar point cloud segmentation
(Institute of Electrical and Electronics Engineers, 2023-10-25) Sevimli, R. A.; Ucuncu, M.; Koç, Aykut
The need for visual perception systems providing situational awareness to autonomous vehicles has grown significantly. While traditional deep neural networks are effective for solving 2-D Euclidean problems, point cloud analysis, particularly for radar data, contains unique challenges because of the irregular geometry of point clouds. This letter proposes a novel transformer-based architecture for radar point clouds adapted to the graph signal processing (GSP) framework, designed to handle non-Euclidean and irregular signal structures. We provide experimental results by using well-established benchmarks on the nuScenes and RadarScenes datasets to validate our proposed method.
Open Access
The relationship between destination performance, overall satisfaction, and behavioral intention for distinct segments
(Routledge, 2004) Baloglu, S.; Pekcan, A.; Chen, S.; Santos, J.
Destination performance, visitor satisfaction, and favorable future behavior of visitors are key determinants of destination competitiveness. Most empirical work, assuming that overall tourist population is homogenous, investigates the relationships among product performance, satisfaction, and/or behavioral intentions in an aggregated manner. This study investigates these linkages for different segments of Canadian visitors of Las Vegas. The findings confirmed the mediating role of overall satisfaction for both segments and aggregated sample, and revealed variations in linkages and explanatory power of the models. The study concludes that the segment-based approach is more pragmatic because it provides segment-specific implications for destination management and marketing.
Open Access
Segmentation-aware MRI reconstruction
(Springer Cham, 2022-09-22) Acar, Mert; Çukur, Tolga; Öksüz, İ.
Deep learning models have been broadly adopted for accelerating MRI acquisitions in recent years. A common approach is to train deep models based on loss functions that place equal emphasis on reconstruction errors across the field-of-view. This homogeneous weighting of loss contributions might be undesirable in cases where the diagnostic focus is on tissues in a specific subregion of the image. In this paper, we propose a framework for segmentation-aware reconstruction based on segmentation as a proxy task. We leverage an end-to-end model comprising reconstruction and segmentation networks; and leverage backpropagation of segmentation error to devise a pseudo-attention effect to focus the reconstruction network. We introduce a novel stabilization method to prevent convergence onto a local minima with unacceptably poor reconstruction or segmentation performance. Our stabilization approach initiates learning on fully-sampled acquisitions, and gradually increases the undersampling rate assumed in the training set to its desired value. We validate our approach for cardiac MR reconstruction on the publicly available OCMR dataset. Segmentation-aware reconstruction significantly outperforms vanilla reconstruction for cardiac imaging.
Open Access
Segmentation-based extraction of important objects from video for object-based indexing
(IEEE, 2008-06) Baştan, Muhammet; Güdükbay, Uğur; Ulusoy, Özgür
We describe a method to automatically extract important video objects for object-based indexing. Most of the existing salient object detection approaches detect visually conspicuous structures in images, while our method aims to find regions that may be important for indexing in a video database system. Our method works on a shot basis. We first segment each frame to obtain homogeneous regions in terms of color and texture. Then, we extract a set of regional and inter-regional color, shape, texture and motion features for all regions, which are classified as being important or not using SVMs trained on a few hundreds of example regions. Finally, each important region is tracked within each shot for trajectory generation and consistency check. Experimental results from news video sequences show that the proposed approach is effective. © 2008 IEEE.
Open Access
Semantic scene classification for content-based image retrieval
(2008) Çavuş, Özge
Content-based image indexing and retrieval have become important research problems with the use of large databases in a wide range of areas. Because of the constantly increasing complexity of the image content, low-level features are no longer sufficient for image content representation. In this study, a content-based image retrieval framework that is based on scene classification for image indexing is proposed. First, the images are segmented into regions by using their color and line structure information. By using the line structures of the images the regions that do not consist of uniform colors such as man made structures are captured. After all regions are clustered, each image is represented with the histogram of the region types it contains. Both multi-class and one-class classification models are used with these histograms to obtain the probability of observing different semantic classes in each image. Since a single class with the highest probability is not sufficient to model image content in an unconstrained data set with a large number of semantically overlapping classes, the obtained probability values are used as a new representation of the images and retrieval is performed on these new representations. In order to minimize the semantic gap, a relevance feedback approach that is based on the support vector data description is also incorporated. Experiments are performed on both Corel and TRECVID datasets and successful results are obtained.
Open Access
Signal and image processing algorithms for agricultural applications
(2006) Dülek, Berkan
Medical studies indicate that acrylamide causes cancer in animals and certain doses of acrylamide are toxic to the nervous system of both animals and humans. Acrylamide is produced in carbohydrate foods prepared at high temperatures such as fried potatoes. For this reason, it is crucial for human health to quantitatively measure the amount of acrylamide formed as a result of prolonged cooking at high temperatures. In this thesis, a correlation is demonstrated between measured acrylamide concentrations and NABY (Normalized Area of Brownish Yellow regions) values estimated from surface color properties of fried potato images using a modified form of the k-means algorithm. Same method is used to estimate acrylamide levels of roasted coffee beans. The proposed method seems to be a promising approach for the estimation of acrylamide levels and can find applications in industrial systems. The quality and price of hazelnuts are mainly determined by the ratio of shell weight to kernel weight. Due to a number of physiological and physical disorders, hazelnuts may grow without fully developed kernels. We previously proposed a prototype system which detects empty hazelnuts by dropping them onto a steel plate and processing the acoustic signal generated when kernels hit the plate. In that study, feature vectors describing time and frequency nature of the impact sound were extracted from the acoustic signal and classified using Support Vector Machines. In the second part of this thesis, a feature domain post-processing method based on vector median/mean filtering is shown to further increase these classification results.
Open Access
Targeted vessel reconstruction in non-contrast-enhanced steady-state free precession angiography
(John Wiley and Sons Ltd, 2016) Ilicak, E.; Cetin S.; Bulut E.; Oguz, K. K.; Saritas, E. U.; Unal, G.; Çukur, T.
Image quality in non-contrast-enhanced (NCE) angiograms is often limited by scan time constraints. An effective solution is to undersample angiographic acquisitions and to recover vessel images with penalized reconstructions. However, conventional methods leverage penalty terms with uniform spatial weighting, which typically yield insufficient suppression of aliasing interference and suboptimal blood/background contrast. Here we propose a two-stage strategy where a tractographic segmentation is employed to auto-extract vasculature maps from undersampled data. These maps are then used to incur spatially adaptive sparsity penalties on vascular and background regions. In vivo steady-state free precession angiograms were acquired in the hand, lower leg and foot. Compared with regular non-adaptive compressed sensing (CS) reconstructions (CSlow), the proposed strategy improves blood/background contrast by 71.3±28.9% in the hand (mean±s.d. across acceleration factors 1-8), 30.6±11.3% in the lower leg and 28.1±7.0% in the foot (signed-rank test, P< 0.05 at each acceleration). The proposed targeted reconstruction can relax trade-offs between image contrast, resolution and scan efficiency without compromising vessel depiction.