472 research outputs found
LAN881655 Supplemental Material - Supplemental material for Improvement of the Mouse Grimace Scale set-up for implementing a semi-automated Mouse Grimace Scale scoring (Part 1)
No full textSupplemental material, LAN881655 Supplemental Material for Improvement of the Mouse Grimace Scale set-up for implementing a semi-automated Mouse Grimace Scale scoring (Part 1) by Lisa Ernst, Marcin Kopaczka, Mareike Schulz, Steven R Talbot, L Zieglowski, M Meyer, S Bruch, Dorit Merhof and Rene H Tolba in Laboratory Animals</p
LAN881664 Supplemetal Material - Supplemental material for Semi-automated generation of pictures for the Mouse Grimace Scale: A multi-laboratory analysis (Part 2)
No full textSupplemental material, LAN881664 Supplemetal Material for Semi-automated generation of pictures for the Mouse Grimace Scale: A multi-laboratory analysis (Part 2) by Lisa Ernst, Marcin Kopaczka, Mareike Schulz, Steven R Talbot, Birgitta Struve, Christine Häger, André Bleich, Mattea Durst, Paulin Jirkof, Margarete Arras, Roelof Maarten van Dijk, Nina Miljanovic, Heidrun Potschka, Dorit Merhof and Rene H Tolba in Laboratory Animals</p
A parameter-optimizing model-based approach to the analysis of low-SNR image sequences for biological virus detection
No full textThis thesis presents the multi-objective parameter optimization of a novel image analysis process. The focus of application is automatic detection of nano-objects, for example biological viruses, in real-time. Nano-objects are detected by analyzing time series of images recorded with the PAMONO biosensor, after parameters have been optimized on synthetic data created by a signal model for PAMONO. PAMONO, which is short for Plasmon-Assisted Microscopy of Nano-Sized Objects, is a biosensor yielding indirect proofs for objects on the nanometer-scale by measuring the Surface Plasmon Resonance (SPR) effects they cause on the micrometer scale. It is an optical microscopy technique enabling the detection of biological viruses and other nano-objects within a portable device. The PAMONO biosensor produces time series of 2-D images on the order of 4000 half-megapixel images per experiment. A particular challenge for automatic analysis of this data emerges from its low Signal-to-Noise Ratio (SNR). Manual analysis takes approximately two days per experiment and analyzing person. With the automatic analysis process developed in this thesis, occurrences of nano-objects in PAMONO data can be counted and displayed in real-time while measurements are being taken. Analysis is divided into a GPU-based detector aiming at high sensitivity, complemented with a machine learning-based classifier aiming at high precision. The analysis process is embedded into a multi-objective optimization approach that automatically adapts algorithm choice and parameters to changes in physical sensor parameters. Such changes occur, for example, during sensor prototype development. In order to automatically evaluate the objectives undergoing optimization, a signal model for the PAMONO sensor is proposed, which serves to synthesize ground truth-annotated data. The parameters of the analysis process are optimized on this synthetic data, and the classifier is learned from it. Hence, the signal model must accurately mimic the data recorded by the sensor, which is achieved by incorporating real sensor data into synthesis. Both, optimized parameters and the learned classifier, achieve high quality results on the real sensor data to be analyzed: Nano-objects with diameters down to 100nm are detected reliably in PAMONO data. Note that the median SNR over all nano-objects to be detected was below two in the examined experiments with 100nm objects. While the presented analysis process can be used for real-time virus detection in PAMONO data, the optimization approach can serve in accelerating the advancement of the sensor prototype towards a final setup of its physical parameters: In this scenario, frequent changes in physical sensor parameters make the automatic adaptation of algorithmic process parameters a desirable goal. No expertise concerning the underlying algorithms is required in these use cases, enabling ready applicability in a lab scenario
Perceptual aspects of sound scattering in concert halls
No full textThis work aims to expand the understanding of sound scattering in architectural spaces and the comprehension of its influence on the auditory perception in concert halls. The notion of scattering coefficient, which numerically represents the physical phenomenon of sound scattering, constitutes the main paradigm for the entire work. In a first part, the scattering coefficient is presented in its meaning and implications, providing both the mathematical formulation and the empirical evaluation. Scattering coefficients of new objects, such as pieces of furniture, have been for the first time determined, hence the foundations for a new scattering coefficient open database is laid. A new solution for avoiding recurrent measurement inaccuracies is presented by means of an improved measurement setup, which consists of a revised scale model reverberation chamber. The benefit of having more accurate acoustic computer simulations by using a wider set of experimental data for scattering coefficient is proved by a case study of classroom acoustics. The implementation of scattering coefficient in different room acoustic computer software is shown and discussed by using a concert hall as a case study. In a second part, the relationship between scattering coefficient and auditory perception is explored. Binaural impulse responses have been determined for three different scenarios, such as two virtual enclosed spaces and one real concert hall, and convolved with music samples to be used in listening tests. Results from listening tests show how changes in scattering coefficient of diffusing surfaces affect the perception of music among the audience in concert halls. A difference limen for scattering coefficient is determined by means of auralized binaural room impulse responses, which have been obtained under different scattering conditions. Results from listening tests are shown and discussed
Signal enhancement and signal reconstruction for diffusion imaging using deep learning
No full textDiffusion imaging, which is based on magnetic resonance imaging (MRI), allows a reconstruction of neural pathways inside the human brain and can, therefore, be employed to investigate neuronal diseases. However, a major disadvantage of this method is that long acquisition times are required to achieve high accuracy. Another disadvantage is that MRI systems from different manufacturers differ considerably from each other, which is why the resulting signals and the resulting characteristics often cannot be compared across devices. This is particularly important for research of neural diseases conducted based on large group studies. This is why this dissertation presents and evaluates novel deep learning based approaches for solving the obstacles mentioned above. The first main part of this thesis presents a new deep learning method, which was developed especially for spherical signals (as they occur in diffusion imaging). This involves the inclusion of previously unused information through a convolution on the signal's sphere. Furthermore, various activation functions are evaluated concerning their applicability on a measured diffusion signal. The second part focuses on signal harmonization, i.e., the homogenization of different MRT systems. The method developed here uses a training database of traveling subjects who have been scanned on both scanners to learn a mapping of signals from the first to the second scanner. It is evaluated using various essential metrics that are often used in clinical practice. This allows a minimizing of differences between two MRI systems. The third part of this dissertation presents a new method which interpolates the measured signal based on a high-resolution training database. Since this interpolation is learned on a signal basis, no assumptions about the underlying microstructure or physical properties need to be made. Subsequently, this approach can be integrated into state-of-the-art reconstruction pipelines. As a result, the measurement time can be considerably reduced and the signal quality substantially improved. The last part of this thesis addresses the reconstruction of the diffusion orientations. Since different diffusion orientations might occur within a voxel, two methods were developed and evaluated that predict the fiber orientation distribution function based on training data. Both methods achieve a higher accuracy in comparison to the evaluated state-of-the-art method, especially if very complex signals occur or if only a few gradient directions have been acquired. In this thesis, new methods for signal reconstruction, signal interpolation and signal harmonization using deep learning are presented in the context of diffusion imaging. It is shown that an inclusion of additional information is of great importance and leads to a distinctly improved result, especially for signal interpolation but also in signal harmonization
Computer-aided texture analysis combined with experts' knowledge: Improving endoscopic celiac disease diagnosis
No full textAIM: To further improve the endoscopic detection of intestinal mucosa alterations due to celiac disease (CD). METHODS: We assessed a hybrid approach based on the integration of expert knowledge into the computer-based classification pipeline. A total of 2835 endoscopic images from the duodenum were recorded in 290 children using the modified immersion technique (MIT). These children underwent routine upper endoscopy for suspected CD or non-celiac upper abdominal symptoms between August 2008 and December 2014. Blinded to the clinical data and biopsy results, three medical experts> visually classified each image as normal mucosa (Marsh-0) or villous atrophy (Marsh-3). The experts’ decisions were further integrated into state-of-the-art texture recognition systems. Using the biopsy results as the reference standard, the classification accuracies of this hybrid approach were compared to the experts’ diagnoses in 27 different settings. RESULTS: Compared to the experts’ diagnoses, in 24 of 27 classification settings (consisting of three imaging modalities, three endoscopists and three classification approaches), the best overall classification accuracies were obtained with the new hybrid approach. In 17 of 24 classification settings, the improvements achieved with the hybrid approach were statistically significant (P < 0.05). Using the hybrid approach classification accuracies between 94% and 100% were obtained. Whereas the improvements are only moderate in the case of the most experienced expert, the results of the less experienced expert could be improved significantly in 17 out of 18 classification settings. Furthermore, the lowest classification accuracy, based on the combination of one database and one specific expert, could be improved from 80% to 95% (P < 0.001). CONCLUSION: The overall classification performance of medical experts, especially less experienced experts, can be boosted significantly by integrating expert knowledge into computer-aided diagnosis systems.Michael Gadermayr, Hubert Kogler, Maximilian Karla, Dorit Merhof, Andreas Uhl, Andreas Vécse
Local feature description with invariance against affine projection
No full textWhile the understanding of the image content is relatively easy for most humans, an automatic analysis is challenging for a computer vision system. To describe the content of an image, local features, which can be understood as mathematical representations of image regions, are used frequently. The image description should be invariant against photo- and geometric image distortions which typically occur when the illumination or the viewing angle during image acquisition changes.This thesis focuses on increasing the robustness of local features under geometric, or more specific, affine image projection, in the context of object detection.To increase robustness of local features against geometric image distortions, the affine invariant coordinate transformation is developed. The affine invariant coordinate transformation is an iterative normalization algorithm which exploits local properties of an image region to normalize it, such that two image regions captured from different viewpoints are identical, up to a rotational transformation after normalization. It can be combined with various feature detection and feature extraction algorithms and used for both globally and locally distorted images.For the detection of objects with various affine projections in different image recordings, the correspondence consensus merging is developed. The correspondence consensus merging uses the normalization matrices of two corresponding features to estimate the projection between the objects. Based on the assumption that features belonging to one semantic object are projected similarly across the images, the correspondence consensus merging groups correspondences whose projection estimates are similar. The algorithm provides reliable object detection results even when the established correspondences between the two images contain plenty false correspondences and can also be used to distinguish between correct and false feature correspondences.The developed algorithms are evaluated on synthetically warped as well as on camera captured image pairs with global and local geometric projection and compared against state of the art methods for affine invariant feature extraction, respectively for feature grouping. It is shown, that especially for images with local geometric projections, the presented algorithm is superior to the state of the art. Furthermore, it is shown that the presented grouping of feature correspondences allows for reliable object detection
Perceptual aspects of sound scattering in concert halls
No full textThis work aims to expand the understanding of sound scattering in architectural spaces and the comprehension of its influence on the auditory perception in concert halls. The notion of scattering coefficient, which numerically represents the physical phenomenon of sound scattering, constitutes the main paradigm for the entire work. In a first part, the scattering coefficient is presented in its meaning and implications, providing both the mathematical formulation and the empirical evaluation. Scattering coefficients of new objects, such as pieces of furniture, have been for the first time determined, hence the foundations for a new scattering coefficient open database is laid. A new solution for avoiding recurrent measurement inaccuracies is presented by means of an improved measurement setup, which consists of a revised scale model reverberation chamber. The benefit of having more accurate acoustic computer simulations by using a wider set of experimental data for scattering coefficient is proved by a case study of classroom acoustics. The implementation of scattering coefficient in different room acoustic computer software is shown and discussed by using a concert hall as a case study. In a second part, the relationship between scattering coefficient and auditory perception is explored. Binaural impulse responses have been determined for three different scenarios, such as two virtual enclosed spaces and one real concert hall, and convolved with music samples to be used in listening tests. Results from listening tests show how changes in scattering coefficient of diffusing surfaces affect the perception of music among the audience in concert halls. A difference limen for scattering coefficient is determined by means of auralized binaural room impulse responses, which have been obtained under different scattering conditions. Results from listening tests are shown and discussed
Steigerung der Energieeffizienz des pneumatischen Fasertransportes
No full textThe textile industry is one of the most energy-intensive sectors of the manufacturing industry. In light of rising energy prices, energy consumption is an important investment criterion. About 45 – 50 % of the energy costs in fibre preparation are accounted for by pneumatic fibre transport. In order to avoid clogging of the conveying systems as well as damage and compaction of the fibres, the conveying fans are usually overdimensioned by about 30 % and are operated at maximum speed. The possibilities for geometric optimisation of the conveying systems are limited due to constructional boundaries. The greatest potential is to be achieved by reducing the air velocity and thus the speed of the conveying fan. The aim of the development is to reduce the air speed by at least 30 %. In the dissertation, a monitoring and control system is developed to control the speed of the conveying fan in an energy-optimised manner depending on the transport state of fibres and flakes. The selection of the sensors and the monitoring strategy is carried out according to the Nine-Step-Tool method, which is updated and extended in the thesis. The most significant characteristic variables for the transport condition are the fibre distribution factor and the temporal fluctuation of the fibre flow. A system model is derived from the tests on an industrial fibre conveying system. For the detection of the transport state, a Sugeno network is developed and trained with model data from the system model. The validation of the state monitoring is carried out using both the model data and the raw data from the industrial test. Furthermore, a two-degree-of-freedom control is developed and validated on the model. The reduction of the air velocity by 30 % leads to a reduction of the energy consumption by about 65 %. For a medium-sized conveying fan with a nominal energy input of 12.5 kW, this results in an annual savings potential of around 9,000 €. The payback period is 1 year. In industrial trials, energy savings of up to 74 % are possible, depending on the desired state of transport
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