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The ability to image the choroid by OCT using enhanced depth imaging or long-wavelength light sources has triggered substantial scientific interest in the role of the choroidal vasculature (CV) in ocular pathology, especially in retinal vascular diseases such as diabetic macular edema (DME). However,[for full text, please go to the a.m. URL], 26. Jahrestagung der Retinologischen Gesellschaft

We present a method for the incorporation of regional image information in a 3-D graph-theoretic approach for optimal multiple surface segmentation. By transforming the multiple surface segmentation task into finding a minimum-cost closed set in a vertex-weighted graph, the optimal set of feasible surfaces with respect to an objective function can be found. In the past, this family of graph search applications only used objective functions which incorporated “on-surface” costs. Here, novel “in-region” costs are incorporated. Our new approach is applied to the segmentation of seven intraretinal layer surfaces of 24 3-D macular optical coherence tomography images from 12 subjects. Compared to an expert-defined independent standard, unsigned border positioning errors are comparable to the inter-observer variability (7.8 ± 5.0 μm and 8.1 ± 3.6 μm, respectively).

We have developed a method for the automated segmentation of the internal limiting membrane and the pigment epithelium in 3-D OCT retinal images. Each surface was found as a minimum s-t cut from a geometric graph constructed from edge/regional information and a priori-determined surface constraints. Our approach was tested on 18 3-D data sets (9 from patients with normal optic discs and 9 from patients with papilledema) obtained using a Stratus OCT-3 scanner. Qualitative analysis of surface detection correctness indicates that our method consistently found the correct surfaces and outperformed the proprietary algorithm used in the Stratus OCT-3 scanner. For example, for the internal limiting membrane, 4% of the 2-D scans had minor failures with no major failures using our approach, but 19% of the 2-D scans using the Stratus OCT-3 scanner had minor or complete failures.

This chapter introduces statistical shape- and appearance models and their biomedical applications. Three- and four-dimensional extension of these models are the main focus. Approaches leading to automated landmark definition are introduced and discussed. The applicability is underlined by presenting practical examples of 3D medical image segmentation.

This paper describes a Multi-View Active Appearance Model (AAM) for coherent segmentation of multiple cardiac views. Cootes' AAM framework was adapted by considering shapes and intensities from multiple views, while eliminating trivial difference in object pose in different views. This way, the coherence in organ shape and intensities between different views is modeled, and utilized during image search. The method is validated in two substantially different and novel applications: segmentation of combined end-diastolic and end-systolic left ventricular X-ray angiograms, and simultaneous segmentation of a combination of four chamber, two chamber and short-axis cardiac MR views

The quality of statistical analyses of functional neuroimages is studied after applying various preprocessing methods. We present wavelet-based denoising as an alternative to Gaussian smoothing, the standard denoising method in statistical parametric mapping (SPM). The wavelet-based denoising schemes are extensions of WaveLab routines, using the symmetric orthogonal cubic spline wavelet basis. In a first study, activity in a time series is simulated by superimposing a time-dependent signal on a selected region. We add noise with a known signal-to-noise ratio (SNR) and spatial correlation. After denoising, the statistical analysis, performed with SPM, is evaluated. We compare the shapes of activations detected after applying the wavelet-based methods with the shapes of activations detected after Gaussian smoothing. In a second study, the denoising schemes are applied to a real functional MRI time series, where signal and noise cannot be separated. The denoised time series are analysed with SPM, while false discovery rate (FDR) control is used to correct for multiple testing. Wavelet-based-denoising, combined with FDR control, yields reliable activation maps. While Gaussian smoothing and wavelet-based methods producing smooth images work well with very low SNRs, less smoothing wavelet-based methods produce better results for time series of moderate quality.

A much-used measure for registration of three-dimensional medical images is mutual information, which originates from information theory. However, information theory offers many more measures that may be suitable for image registration. Such measures denote the divergence of the joint grey value distribution of two images from the joint distribution for complete independence of the images. This paper compares the performance of mutual information as a registration measure with that of other information measures. The measures are applied to rigid registration of clinical PET/MR and MR/CT images, for 35 and 41 image pairs respectively. An accurate gold standard transformation is available for the images, based on implanted markers. Both registration performance and accuracy of the measures are studied. The results indicate that some information measures perform very poorly for the chosen registration problems, yielding many misregistrations, even when using a good starting estimate. Other measures, however , were shown to produce significantly more accurate results than mutual information.