Abstract:In cell biology and other fields the automatic accurate localization of sub-resolution objects in images is an important tool. The signal is often corrupted by multiple forms of noise, including excess noise resulting from the amplification by an electron multiplying charge-coupled device (EMCCD). Here we present our novel Nested Maximum Likelihood Algorithm (NMLA), which solves the problem of localizing multiple overlapping emitters in a setting affected by excess noise, by repeatedly solving the task of independent localization for single emitters in an excess noise-free system. NMLA dramatically improves scalability and robustness, when compared to a general purpose optimization technique. Our method was successfully applied for in vivo localization of fluorescent proteins.

The determination of water application parameters for creating an optimal soil moisture profile represents a complex nonlinear optimization problem which renders traditional optimization into a cumbersome procedure. For this reason, an alternative methodology is proposed which combines a numerical subsurface flow model and artificial neural networks ͑ANN͒ for solving the problem in two, fully separate steps. The first step employs the flow model for calculating a large number of wetting profiles ͑output͒, obtained from a systematic variation of both water application and initial soil moisture ͑input͒. The resulting matrix of corresponding input/output values is used for training the ANN. The second step, the application of the fully trained ANN, then provides the irrigation parameters which range from a specified initial soil moisture to a desired crop-specific soil moisture profile. In order to avoid substantial disadvantages associated with the common feedforward backpropagation approach, a self-organizing topological feature map is implemented to perform this task. After a comprehensive sensitivity analysis, the new methodology is applied to the outcome of an irrigation experiment. The convincing results recommend the new methodology as a positive contribution towards an improved irrigation efficiency.

[1] Inverse solutions of the Richards equation, either for evaluating soil hydraulic parameters from experimental data or for optimizing irrigation parameters, require considerable numerical effort. We present an alternative methodology based on self-organizing maps (SOM) which was further developed in order to include multiple input-output (MIO) relationships. The resulting SOM-MIO network approximates the Richards equation and its inverse solution with an outstanding accuracy, and both tasks can be performed by the same network. No additional training is required for solving the different tasks, which represents a significant advantage over conventional networks. An application of the SOM-MIO simulating a laboratory irrigation experiment in a Monte Carlo-based framework shows a much improved computational efficiency compared to the used numerical simulation model. The high consistency of the results predicted by the artificial neural network and by the numerical model demonstrates the excellent suitability of the SOM-MIO for dealing with such kinds of stochastic simulation or for solving inverse problems.

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Multimodal medical image fusion combines information of one or more images in order to improve the diagnostic value. While previous applications mainly focus on merging images from computed tomography, magnetic resonance imaging (MRI), ultrasonic and singlephoton emission computed tomography, we propose a novel approach for the registration and fusion of preoperative 3D MRI with intraoperative 2D infrared thermography. Image-guided neurosurgeries are based on neuronavigation systems, which further allow us track the position and orientation of arbitrary cameras. Hereby, we are able to relate the 2D coordinate system of the infrared camera with the 3D MRI coordinate system. The registered image data are now combined by calibration-based image fusion in order to map our intraoperative 2D thermographic images onto the respective brain surface recovered from preoperative MRI. In extensive accuracy measurements, we found that the proposed framework achieves a mean accuracy of 2.46 mm.