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Modern Applied Statistics With S

Many cameras implement auto-focus functionality. However, they typically require the user to manually identify the location to be focused on. While such an approach works for temporally-sparse autofocusing functionality (e.g., photo shooting), it presents extreme usability problems when the focus must be quickly switched between multiple areas (and depths) of interest - e.g., in a gaze-based autofocus approach. This work introduces a novel, real-time auto-focus approach based on eye-tracking, which enables the user to shift the camera focus plane swiftly based solely on the gaze information. Moreover, the proposed approach builds a graph representation of the image to estimate depth plane surfaces and runs in real time (requiring ~20ms on a single i5 core), thus allowing for the depth map estimation to be performed dynamically. We evaluated our algorithm for gaze-based depth estimation against state-of-the-art approaches based on eight new data sets with flat, skewed, and round surfaces, as well as publicly available datasets.

Fast camera focus estimation for gaze-based focus control

Light widefield microscopes and digital imaging are the basis for most of the analyses performed in every biological laboratory. In particular, the microscope's user is typically interested in acquiring high-detailed images for analysing observed cells and tissues, meanwhile being representative of a wide area to have reliable statistics. The microscopist has to choose between higher magnification factor and extension of the observed area, due to the finite size of the camera's field of view. To overcome the need of arrangement, mosaicing techniques have been developed in the past decades for increasing the camera's field of view by stitching together more images. Nevertheless, these approaches typically work in batch mode and rely on motorized microscopes. Or alternatively, the methods are conceived just to provide visually pleasant mosaics not suitable for quantitative analyses. This work presents a tool for building mosaics of images acquired with nonautomated light microscopes. The method proposed is based on visual information only and the mosaics are built by incrementally stitching couples of images, making the approach available also for online applications. Seams in the stitching regions as well as tonal inhomogeneities are corrected by compensating the vignetting effect. In the experiments performed, we tested different registration approaches, confirming that the translation model is not always the best, despite the fact that the motion of the sample holder of the microscope is apparently translational and typically considered as such. The method's implementation is freely distributed as an open source tool called MicroMos. Its usability makes building mosaics of microscope images at subpixel accuracy easier. Furthermore, optional parameters for building mosaics according to different strategies make MicroMos an easy and reliable tool to compare different registration approaches, warping models and tonal corrections.

Automated image mosaics by non-automated light microscopes: The MicroMos software tool

We describe a weighted least squares based global registration method for creating virtual slides. In microscopy a large number of fields of view is required to capture a complete slide. These fields of view are aligned in such a way that a globally consistent virtual slide is formed. The positioning accuracy of the presented algorithm is evaluated using a new method based on synthetic virtual slides. Using these synthetic virtual slides it is now possible to give a ground truth about the positioning accuracy of stitching algorithm for virtual microscopy. Our algorithm has been evaluated on these synthetic slides and it can be shown that on average, each image exhibits a mean deviation of 0.8 pixel with respect to the correct position. Additionally the presented algorithm has been evaluated on several real world examples.

Efficient large scale image stitching for virtual microscopy

Summary


We present a new algorithm to analyse information content in images acquired using automated fluorescence microscopy. The algorithm belongs to the group of autofocusing methods, but differs from its predecessors in that it can handle thick specimens and operate also in confocal mode. It measures the information content in images using a ‘content function’, which is essentially the same concept as a focus function. Unlike previously presented algorithms, this algorithm tries to find all significant axial positions in cases where the content function applied to real data is not unimodal, which is often the case. This requirement precludes using algorithms that rely on unimodality. Moreover, choosing a content function requires careful consideration, because some functions suppress local maxima. First, we test 19 content functions and evaluate their ability to show local maxima clearly. The results show that only six content functions succeed. To save time, the acquisition procedure needs to vary the step size adaptively, because a wide range of possible axial positions has to be passed so as not to miss a local maximum. The algorithm therefore has to assess the steepness of the content function online so that it can decide to use a bigger or smaller step size to acquire the next image. Therefore, the algorithm needs to know about typical behaviour of content functions. We show that for normalized variance, one of the most promising content functions, this knowledge can be obtained after normalizing with respect to the theoretical maximum of this function, and using hierarchical clustering. The resulting algorithm is more reliable and efficient than a simple procedure with constant steps.

Information content analysis in automated microscopy imaging using an adaptive autofocus algorithm for multimodal functions

In this paper, we present a robust focus algorithm tailored to the requirements of automated microscopy of fluorescently labelled bacteria. We combine two strategies to make the algorithm robust against focus errors due to low bacteria densities and the presence of dirt. First, we acquire a number of focus estimates for different fields on each slide well using a correlation-based focus measure. A planar model is fitted to these initial estimates using robust regression. The fit parameters provide information about slide tilt and the variability of the focus positions. We use this to constrain the search range to the locally optimal range for each field. Second, we introduce a heuristic pre-filtering step, based on a morphological area opening, into the computation of the focus measure to suppress false maxima arising from the presence of dirt particles. The effectiveness of both steps is demonstrated by focus results obtained for a sample slide.

Robust Autofocusing for Automated Microscopy Imaging of Fluorescently Labelled Bacteria

Image mosaicing has found a number of applications such as panoramic imaging, digital terrain mapping, ophthalmology, and virtual microscopy. In this paper, we present an automated mosaicing technique for generating mosaics from microscope images. We carry out robust image feature matching and global geometric and radiometric parameter estimation. The images are transformed using the estimated parameters and mosaiced together, producing accurate registration of overlapping regions without visible seams.

Mosaicing of Microscope Images

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Robust Autofocusing for Automated Microscopy Imaging of Fluorescently Labelled Bacteria

Mosaicing of Microscope Images