Nonparametric joint shape learning for customized shape modeling

We present a shape optimization approach to compute patient-specific models in customized prototyping applications. We design a coupled shape prior to model the transformation between a related pair of surfaces, using a nonparametric joint probability density estimation. The coupled shape prior forces with the help of application-specific data forces and smoothness forces drive a surface deformation towards a desired output surface. We demonstrate the usefulness of the method for generating customized shape models in applications of hearing aid design and pre-operative to intra-operative anatomic surface estimation

Estimation of vector fields in unconstrained and inequality constrained variational problems for segmentation and registration

Vector fields arise in many problems of computer vision, particularly in non-rigid registration. In this paper, we develop coupled partial differential equations (PDEs) to estimate vector fields that define the deformation between objects, and the contour or surface that defines the segmentation of the objects as well.We also explore the utility of inequality constraints applied to variational problems in vision such as estimation of deformation fields in non-rigid registration and tracking. To solve inequality constrained vector field estimation problems, we apply tools from the Kuhn-Tucker theorem in optimization theory. Our technique differs from recently popular joint segmentation and registration algorithms, particularly in its coupled set of PDEs derived from the same set of energy terms for registration and segmentation. We present both the theory and results that demonstrate our approach

Cointegration and Extreme Value Analyses of Bovespa and the Istanbul Stock Exchange

This paper investigates the long-term financial integration and bivariate extreme dependence between Bovespa and the Istanbul Stock Exchange. While a static cointegration test presents no evidence of long-term cointegration, the introduction of a structural break into the model shows that Bovespa and the ISE were cointegrated following the local crisis in Turkey in 2000. Dynamic cointegration tests and DCC-GARCH analysis also reveal that Bovespa and the ISE reacted strongly not only to systemic crises as expected, but also unexpectedly to local crises in each other. This shows that equity prices in two emerging markets in distant regions of the world can co-move in the absence of significant trade and financial linkages. This suggests that there are underlying processes that affect equity prices other than trade, financial linkages, macroeconomic ties, and FDI as the prior literature suggests. While episodic cointegration is found for Bovespa and the ISE, the extremes of these markets still possess asymptotic independence, suggesting diversification opportunities.cointegration, structural break, dynamic conditional correlations, bivariate extreme value, emerging markets, Turkey, Brazil

Anatomical landmark based registration of contrast enhanced T1-weighted MR images

In many problems involving multiple image analysis, an im- age registration step is required. One such problem appears in brain tumor imaging, where baseline and follow-up image volumes from a tu- mor patient are often to-be compared. Nature of the registration for a change detection problem in brain tumor growth analysis is usually rigid or affine. Contrast enhanced T1-weighted MR images are widely used in clinical practice for monitoring brain tumors. Over this modality, con- tours of the active tumor cells and whole tumor borders and margins are visually enhanced. In this study, a new technique to register serial contrast enhanced T1 weighted MR images is presented. The proposed fully-automated method is based on five anatomical landmarks: eye balls, nose, confluence of sagittal sinus, and apex of superior sagittal sinus. Af- ter extraction of anatomical landmarks from fixed and moving volumes, an affine transformation is estimated by minimizing the sum of squared distances between the landmark coordinates. Final result is refined with a surface registration, which is based on head masks confined to the sur- face of the scalp, as well as to a plane constructed from three of the extracted features. The overall registration is not intensity based, and it depends only on the invariant structures. Validation studies using both synthetically transformed MRI data, and real MRI scans, which included several markers over the head of the patient were performed. In addition, comparison studies against manual landmarks marked by a radiologist, as well as against the results obtained from a typical mutual information based method were carried out to demonstrate the effectiveness of the proposed method

Registration of brain tumor images using hyper-elastic regularization

In this paper, we present a method to estimate a deformation field between two instances of a brain volume having tumor. The novelties include the assessment of the disease progress by observing the healthy tissue deformation and usage of the Neo-Hookean strain energy density model as a regularizer in deformable registration framework. Implementations on synthetic and patient data provide promising results, which might have relevant use in clinical problems

Endovaskuler müdahalelerde x-ray videodan kılavuz teli izleme = Guidewire tracking in x-ray videos of endovascular interventions

Bu bildiride kalp x-ray videolarında kılavuz telinin izlenmesi ic¸in yeni bir metot sunulmaktadır. Değgişimler hesabı kullanılarak bir kobra eğrisini içkin ve dıştan gelen kuvvetler ile kısıtlayarak deforme eden türevsel denklemler türetilmiştir. Bu denklemler kullanılarak eğrinin güncellenmesi ile imgedeki kılavuz teline uygunluğu, pürüzsüzlüğü, ve telin uzunluğunun korunması sağlanır. Analitik olarak türettiğimiz bu denklemler önceki metotlardan farklı olarak teğetsel terimler de içermektedir. X-ray videolarda tipik olarak karşılaşılan zayıf kontrasta karşı imgeye bağlı öznitelik olarak faz eşlenmesi haritası kullanılmıs¸tır. Geliştirilen metodun başarısı deneysel sonuçlar ile düşük kontrastlı x-ray videoları ¨uzerinde kılavuz teli izleme ile gösterilmis¸tir

Stent implant follow-up in intravascular optical coherence tomography images

The objectives of this article are (i) to utilize computer methods in detection of stent struts imaged in vivo by optical coherence tomography (OCT) during percutaneous coronary interventions (PCI); (ii) to provide measurements for the assessment and monitoring of in-stent restenosis by OCT post PCI. Thirty-nine OCT cross-sections from seven pullbacks from seven patients presenting varying degrees of neointimal hyperplasia (NIH) are selected, and stent struts are detected. Stent and lumen boundaries are reconstructed and one experienced observer analyzed the strut detection, the lumen and stent area measurements, as well as the NIH thickness in comparison to manual tracing using the reviewing software provided by the OCT manufacturer (LightLab Imaging, MA, USA). Very good agreements were found between the computer methods and the expert evaluations for lumen cross-section area (mean difference = 0.11 ± 0.70 mm2; r2 = 0.98, P\ 0.0001) and the stent cross-section area (mean difference = 0.10 ± 1.28 mm2; r2 = 0.85, P value\ 0.0001). The average number of detected struts was 10.4 ± 2.9 per crosssection when the expert identified 10.5 ± 2.8 (r2 = 0.78, P value\0.0001). For the given patient dataset: lumen cross-sectional area was on the average (6.05 ± 1.87 mm2), stent cross-sectional area was (6.26 ± 1.63 mm2), maximum angle between struts was on the average (85.96 ± 54.23), maximum, average, and minimum distance between the stent and the lumen were (0.18 ± 0.13 mm), (0.08 ± 0.06 mm), and (0.01 ± 0.02 mm), respectively, and stent eccentricity was (0.80 ± 0.08). Low variability between the expert and automatic method was observed in the computations of the most important parameters assessing the degree of neointimal tissue growth in stents imaged by OCT pullbacks. After further extensive validation, the presented methods might offer a robust automated tool that will improve the evaluation and follow-up monitoring of in-stent restenosis in patients