PCR amplification of 16S rDNA was found to be highly biased, so that the rDNA from one species out of four was preferentially amplified. We present evidence that the observed PCR bias most likely occurs because the genomic DNA of some species contains segments outside the amplified sequence that inhibit the initial PCR steps. Attempts to overcome this bias by use of a`touch down' PCR procedure or by performing PCR in the presence of denaturants or cosolvents such as acetamide, DMSO, or glycerol were unsuccessful. Since the PCR inhibiting interference from template flanking DNA segments evidently is dependent on the position of the primer sites, we suggest that community diversity analysis based on PCR amplification of 16S rDNA can be improved by extending the procedure from comparative analysis of 16S rDNA amplified by use of only one primer set to a procedure involving at least two different 16S rDNA PCR amplifications performed with different primer sets. z

We present an adaptive parametrization scheme for dynamic mesh refinement in the application of parametric image registration. The scheme is based on a refinement measure ensuring that the control points give an efficient representation of the warp fields, in terms of minimizing the registration cost function. In the current work we introduce multivariate B-splines as a novel alternative to the widely used tensor B-splines enabling us to make efficient use of the derived measure.The multivariate B-splines of order n are C n−1 smooth and are based on Delaunay configurations of arbitrary 2D or 3D control point sets. Efficient algorithms for finding the configurations are presented, and B-splines are through their flexibility shown to feature several advantages over the tensor B-splines. In spite of efforts to make the tensor product B-splines more flexible, the knots are still bound to reside on a regular grid. In contrast, by efficient nonconstrained placement of the knots, the multivariate Bsplines are shown to give a good representation of inhomogeneous objects in natural settings.The wide applicability of the method is illustrated through its application on medical data and for optical flow estimation.

Abstract. Whole-body magnetic resonance imaging is an emerging application gaining vast clinical interest during the last years. Although recent technological advances shortened the longish acquisition time, this is still the limiting factor avoiding its wide-spread clinical usage. The acquisition of images with large field-of-view helps to relieve this drawback, but leads to significantly distorted images. Therefore, we propose a deformable mosaicing approach, based on the simultaneous registration to linear weighted averages, to correct for distortions in the overlapping area. This method produces good results on in-vivo data and has the advantage that a seamless integration into the clinical workflow is possible.

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The current report concerns methods of early detection of connective tissue disorders leading to aortic aneurysms and dissections. Automated and accurate segmentation of the aorta in 4D (3D + time) MR image data is reviewed, and a computer-aided diagnosis (CAD) method using independent component analysis is reported. This admits the objective identification of subjects with connective tissue disorders from 4D aortic MR images.The majority of the presented work is concentrated on independent component analysis(ICA), estimating sources to be used for the diagnosis task. Prior knowledge of the source distribution is utilized using an ordering of the components. Two new ordering measures are introduced in current work. A novel approach to constrained dimensionality reduction in ICA is developed. A new idea of time-invariant independent components is introduced, and assists in the disease detection in the presence of sparse data.4D MR image data sets acquired from 21 normal and 10 diseased subjects are used to evaluate the efficiency of the method. The automated 4D segmentation result produces accurate aortic surfaces. The ICA results are validated by a leave-one-out classification test, and are further substantiated by visual inspection of the components. Using a single phase of the cardiac cycle, 8 out of 10 diseased subjects are identified and the specificity is 100 %, classifying all 21 healthy subjects correctly. These results are obtained using components showing correspondence to clinical observations. With 4D information included, the CAD method classifies 9 out of 10 diseased correctly, and still the specificity is 100 %. ii ResuméDen indevaerende rapport vedrører metoder til tidlig detektering af bindevaevssygdomme, som fører til aortic aneurysms og dissections. En automatisk og praecis metode til segmentering af aorta i 4D (3D + tid) MR data er refereret og en computerassisteret diagnose (CAD) metode, der involverer brugen af independent component analysis, er rapporteret. Dette muliggør en objektiv identificering af subjekter med bindevaevssygdomme, udfra 4D MR billeder af aorta.Hovedparten af det fremlagte arbejde er koncentreret omkring independent component analysis (ICA), som estimerer kilder, der bruges under diagnose opgaven. A priori viden om kildernes fordeling er udnyttet til udformningen af en sortering af de fundne komponenter. To nye sortereringsmål er fremført i det indevaerende arbejde. En ny tilgang til dimsionsreducering under bibetingelser i ICA er udviklet. Et nyt koncept om en tidsinvariant independent component er desuden introduceret, hvilket assisterer til sygdomsdetekteringen, når der kun er en staerkt begraenset maengde data til rådighed.4D MR billedsaet, optaget af 21 normale og 10 syge subjekter, er brugt til at evaluere effektiviteten af den udviklede metode. Den automatiserede 4D segmentering giver en nøjagtig aorta overflade. ICA resultaterne er valideret ved en leave-one-out klassificeringstest, og er yderligere underbygget ved visuel inspektion af de fundne komponent...

We describe a method for tracking people using a

Nonlinear registration is mostly performed after initialization by a global, linear transformation (in this work, we focus on similarity transformations), computed by a linear registration method. For the further processing of the results, it is mostly assumed that this preregistration step completely removes the respective linear transformation. However, we show that in deformable settings, this is not the case. As a consequence, a significant linear component is still existent in the deformation computed by the nonlinear registration algorithm. For construction of statistical shape models (SSM) from deformations, this is an unwanted property: SSMs should not contain similarity transformations, since these do not capture information about shape. We propose a method which performs an a posteriori extraction of a similarity transformation from a given nonlinear deformation field, and we use the processed fields as input for SSM construction. For computation of minimal displacements, a closed-form solution minimizing the squared Euclidean norm of the displacement field subject to similarity parameters is used. Experiments on real inter-subject data and on a synthetic example show that the theoretically justified removal of the similarity component by the proposed method has a large influence on the shape model and significantly improves the results.

Abstract. Crouzon syndrome is characterised by the premature fusion of cranial sutures. Recently the first genetic Crouzon mouse model was generated. In this study, Micro CT skull scannings of wild-type mice and Crouzon mice were investigated. Using nonrigid registration, a wild-type craniofacial mouse atlas was built. The atlas was registered to all mice providing parameters controlling the deformations for each subject. Our previous PCA-based statistical deformation model on these parameters revealed only one discriminating mode of variation. Aiming at distributing the discriminating variation over more modes we built a different model using Independent Component Analysis (ICA). Here, we focus on a third method, sparse PCA (SPCA), which aims at approximating the properties of a standard PCA while introducing sparse modes of variation. The results show that SPCA outperforms both ICA and PCA with respect to the Fisher discriminant, although many similarities are found with respect to ICA.