Biomedical subjects

Horst Bischof

Publications and source records attributed to Horst Bischof.

10 recordsLinked to original sources

PubMed2006

Micro-colony array based high throughput platform for enzyme library screening.

Enzymes are becoming increasingly important tools for synthesizing and modifying fine and bulk chemicals. The availability of biocatalysts which fulfil the requirements of industrial processes is often limited. Recruiting suited enzymes from natural (e.g. metagenomes) and artificial (e.g. directed evolution) biodiversity is based on screening libraries of microbial clones expressing enzyme variants. However, exploring the complex diversity of such libraries needs efficient screening methods. Overcoming the "screening bottleneck" requires rapid high throughput technology allowing the analysis of a large diversity of different enzymes and applying different screening conditions. Facing these facts an efficient and cost effective method for high throughput screening of large enzyme libraries at the colony level was developed. Therefore, ordered high density micro-colony arrays were combined with optical sensor technology and automated image analysis. The system generally allows the simultaneous monitoring of enzyme activities reflected by up to 7000 micro-colonies spotted on a filter in the size of a micro-titer plate. A developed replica option also allows the analysis of clones under varying external conditions. The method was verified by a model screening using esterases and was proved to provide reliable enzyme activity measurements within single micro-colonies allowing the discrimination of activity differences in the range of 10-20%.

Calibration↗

PubMed2006

3D parallel coordinate systems--a new data visualization method in the context of microscopy-based multicolor tissue cytometry.

BACKGROUND: Presentation of multiple interactions is of vital importance in the new field of cytomics. Quantitative analysis of multi- and polychromatic stained cells in tissue will serve as a basis for medical diagnosis and prediction of disease in forthcoming years. A major problem associated with huge interdependent data sets is visualization. Therefore, alternative and easy-to-handle strategies for data visualization as well as data meta-evaluation (population analysis, cross-correlation, co-expression analysis) were developed. METHODS: To facilitate human comprehension of complex data, 3D parallel coordinate systems have been developed and used in automated microscopy-based multicolor tissue cytometry (MMTC). Frozen sections of human skin were stained using the combination anti-CD45-PE, anti-CD14-APC, and SytoxGreen as well as the appropriate single and double negative controls. Stained sections were analyzed using automated confocal laser microscopy and semiquantitative MMTC-analysis with TissueQuest 2.0. The 3D parallel coordinate plots are generated from semiquantitative immunofluorescent data of single cells. The 2D and 3D parallel coordinate plots were produced by further processing using the Matlab environment (Mathworks, USA). RESULTS: Current techniques in data visualization primarily utilize scattergrams, where two parameters are plotted against each other on linear or logarithmic scales. However, data evaluation on cartesian x/y-scattergrams is, in general, only of limited value in multiparameter analysis. Dot plots suffer from serious problems, and in particular, do not meet the requirements of polychromatic high-context tissue cytometry of millions of cells. The 3D parallel coordinate plot replaces the vast amount of scattergrams that are usually needed for the cross-correlation analysis. As a result, the scientist is able to perform the data meta-evaluation by using one single plot. On the basis of 2D parallel coordinate systems, a density isosurface is created for representing the event population in an intuitive way. CONCLUSIONS: The proposed method opens new possibilities to represent and explore multidimensional data in the perspective of cytomics and other life sciences, e.g., DNA chip array technology. Current protocols in immunofluorescence permit simultaneous staining of up to 17 markers. Showing the cross-correlation between these markers requires 136 scattergrams, which is a prohibitively high number. The improved data visualization method allows the observation of such complex patterns in only one 3D plot and could take advantage of the latest developments in 3D imaging.

Humans↗

PubMed2006

Fast active appearance model search using canonical correlation analysis.

A fast AAM search algorithm based on canonical correlation analysis (CCA-AAM) is introduced. It efficiently models the dependency between texture residuals and model parameters during search. Experiments show that CCA-AAMs, while requiring similar implementation effort, consistently outperform standard search with regard to convergence speed by a factor of four.

Algorithms↗

PubMed2005

Bio-computational model of object-recognition: quantum Hebbian processing with neurally shaped Gabor wavelets.

Theoretical and simulational evidence, as well as experimental indications, are accumulating that quantum associative memory and imaging are possible. We compare these data with biological evidence, since we find them to a significant extent compatible. This paper presents a computationally implementable integrative model of appearance-based viewpoint-invariant recognition of objects. The neuro-quantum hybrid model incorporates neural processing up to V1 and quantum associative processing in V1, achieving together an object-recognition result in V2 and ITC. Results of our simulation of the central quantum-like parts of the bio-model, receiving neurally pre-processed inputs, are presented. This part contains our original simulated storage by multiple quantum interference of image-encoding Gabor wavelets done in a Hebbian way, especially using the Griniasty et al. pose-sequence learning rule.

Humans↗

PubMed2005

Characterization of four-class motor imagery EEG data for the BCI-competition 2005.

To determine and compare the performance of different classifiers applied to four-class EEG data is the goal of this communication. The EEG data were recorded with 60 electrodes from five subjects performing four different motor-imagery tasks. The EEG signal was modeled by an adaptive autoregressive (AAR) process whose parameters were extracted by Kalman filtering. By these AAR parameters four classifiers were obtained, namely minimum distance analysis (MDA)--for single-channel analysis, and linear discriminant analysis (LDA), k-nearest-neighbor (kNN) classifiers as well as support vector machine (SVM) classifiers for multi-channel analysis. The performance of all four classifiers was quantified and evaluated by Cohen's kappa coefficient, an advantageous measure we introduced here to BCI research for the first time. The single-channel results gave rise to topographic maps that revealed the channels with the highest level of separability between classes for each subject. Our results of the multi-channel analysis indicate SVM as the most successful classifier, whereas kNN performed worst.

Algorithms↗

PubMed2005

Robust active appearance models and their application to medical image analysis.

Active appearance models (AAMs) have been successfully used for a variety of segmentation tasks in medical image analysis. However, gross disturbances of objects can occur in routine clinical setting caused by pathological changes or medical interventions. This poses a problem for AAM-based segmentation, since the method is inherently not robust. In this paper, a novel robust AAM (RAAM) matching algorithm is presented. Compared to previous approaches, no assumptions are made regarding the kind of gray-value disturbance and/or the expected magnitude of residuals during matching. The method consists of two main stages. First, initial residuals are analyzed by means of a mean-shift-based mode detection step. Second, an objective function is utilized for the selection of a mode combination not representing the gross outliers. We demonstrate the robustness of the method in a variety of examples with different noise conditions. The RAAM performance is quantitatively demonstrated in two substantially different applications, diaphragm segmentation and rheumatoid arthritis assessment. In all cases, the robust method shows an excellent behavior, with the new method tolerating up to 50% object area covered by gross gray-level disturbances.

Algorithms↗

PubMed2005

Computer vision analysis of collagen fiber bundles in the adventitia of human blood vessels.

Numerical simulations, which are based on reliable biomechanical models of blood vessels, can help to get a better understanding of cardiovascular diseases such as atherosclerosis, and can be used to develop optimal medical treatment strategies.The adventitia is the outer most layer of blood vessels and its mechanical properties are essentially determined by the three-dimensional, structural arrangement of collagen fibre bundles embedded in the tissue. Global information such as the orientation statistics of the fibre bundles as well as detailed information as the crimp of the single fibres within the bundles is of particular interest in biomechanical modeling.In order to obtain a sufficiently large amount of data for biomechanical modeling, a fully automatic method for the structural analysis of the soft tissue is required. In this contribution we present methods based on computer vision to fulfill this task. We start by discussing proper tissue preparation and imaging techniques that have to be used to obtain data, which reliably represents the real three-dimensional tissue structure. The next step is concerned with algorithms that robustly segment the collagen fibre bundles and cope with various kinds of artifacts. Novel segmentation techniques for robust segmentation of individual fibril bundles and methods for estimation of their parameters, such as location, shape, mean fibril orientation, crimp of fibrils, etc, is discussed. The proposed algorithms are based on novel perceptual grouping methods operating on the extracted orientation data of fibrils.Finally, we demonstrate the results obtained by our fully automatic method on real data. In addition, for a more quantitative assessment, we introduce a generative structural model that enables the synthesis of three-dimensional fibre bundles with well-defined characteristics.

Adventitia↗

PubMed2004

Quantum-implementable selective reconstruction of high-resolution images.

This paper, written for interdisciplinary audience, presents computational image reconstruction implementable by quantum optics. The input-triggered selection of a high-resolution image among many stored ones, and its reconstruction if the input is occluded or noisy, has been successfully simulated. The original algorithm, based on the Hopfield associative neural net, was transformed in order to enable its quantum-wave implementation based on holography. The main limitations of the classical Hopfield net are much reduced with the simulated new quantum-optical implementation.

Journal Article↗

PubMed2004

Differential diagnosis of rheumatic diseases using conventional radiography.

The approach to the differential diagnosis of rheumatic diseases using conventional radiography is systematic and module-oriented, which, with respect to future developments, forms the basis for computer-assisted diagnosis (CAD). The indications follow consensus-based referral criteria and attempts should be made to raise the evidence level of the recommendations. Investigation techniques have been improved in the last few years with the use of digital radiography. New imaging technologies may be available in the future that will achieve at least the same quality of images, while exposing patients to a significantly lower radiation dose. The interpretation of radiographical signs could be enhanced through a correlation with other imaging modalities. Computer-assisted techniques with image processing tools for automated measurements, lesion detection and in the form of expert systems are under development. With conventional radiography embedded in CAD systems, promising options will be available to enhance the differential diagnosis of rheumatic diseases.

Arthrography↗

PubMed1998

An efficient MDL-based construction of RBF networks.

We propose a method for optimizing the complexity of Radial basis function (RBF) networks. The method involves two procedures: adaptation (training) and selection. The first procedure adaptively changes the locations and the width of the basis functions and trains the linear weights. The selection procedure performs the elimination of the redundant basis functions using an objective function based on the Minimum Description Length (MDL) principle. By iteratively combining these two procedures we achieve a controlled way of training and modifying RBF networks, which balances accuracy, training time, and complexity of the resulting network. We test the proposed method on function approximation and classification tasks, and compare it with some other recently proposed methods.

Journal Article↗