3D microscopy and image analysis provide reliable measurements of length, branching, density, tortuosity and orientation of tubular structures in biological samples. We present a survey of methods for analysis of large samples by measurement of local differences in geometrical characteristics. The methods are demonstrated on the structure of the capillary bed in a rat brain., J. Janáček ... [et al.]., and Obsahuje bibliografii a bibliografické odkazy
In this paper, we introduce a set of methods for processing and analyzing long time series of 3D images representing embryo evolution. The images are obtained by in vivo scanning using a confocal microscope where one of the channels represents the cell nuclei and the other one the cell membranes. Our image processing chain consists of three steps: image filtering, object counting (center detection) and segmentation. The corresponding methods are based on numerical solution of nonlinear PDEs, namely the geodesic mean curvature flow model, flux-based level set center detection and generalized subjective surface equation. All three models have a similar character and therefore can be solved using a common approach. We explain in details our semi-implicit time discretization and finite volume space discretization. This part is concluded by a short description of parallelization of the algorithms. In the part devoted to experiments, we provide the experimental order of convergence of the numerical scheme, the validation of the methods and numerous experiments with the data representing an early developmental stage of a zebrafish embryo.
The paper presents an overview of image analysis activities of the Brno DAR group in the medical application area of retinal imaging. Particularly, illumination correction and SNR enhancement by registered averaging as preprocessing steps are briefly described; further mono- and multimodal registration methods developed for specific types of ophthalmological images, and methods for segmentation of optical disc, retinal vessel tree and autofluorescence areas are presented. Finally, the designed methods for neural fibre layer detection and evaluation on retinal images, utilising different combined texture analysis approaches and several types of classifiers, are shown. The results in all the areas are shortly commented on at the respective sections. In order to emphasise methodological aspects, the methods and results are ordered according to consequential phases of processing rather then divided according to individual medical applications.
Deposition of fibers in human lungs is known as a health hazard. In-vitro measurements were performed with glass fibers in a realistic model of human lungs up to the seventh generation of branching to estimate the effect of fiber size and breathing pattern on fiber deposition. Deposited fibers were rinsed from the model segments and gathered on nitrocellulose filters. Phase-contrast microscopy with high resolution camera was used to capture images of filters with fibers. New software was developed for an automated image analysis and local deposition characteristics were calculated afterwards. The whole method proved to be a useful and valuable tool for the evaluation of fiber and particle deposition. and Obsahuje seznam literatury
In this article we use a combination of neural networks with other techniques for the analysis of orthophotos. Our goal is to obtain results that can serve as a useful groundwork for interactive exploration of the terrain in detail. In our approach we split an aerial photo into a regular grid of segments and for each segment we detect a set of features. These features depict the segment from the viewpoint of a general image analysis (color, tint, etc.) as well as from the viewpoint of the shapes in the segment. We perform clustering based on the Formal Concept Analysis (FCA) and Non-negative Matrix Factorization (NMF) methods and project the results using effective visualization techniques back to the aerial photo. The FCA as a tool allows users to be involved in the exploration of particular clusters by navigation in the space of clusters. In this article we also present two of our own computer systems that support the process of the validation of extracted features using a neural network and also the process of navigation in clusters. Despite the fact that in our approach we use only general properties of images, the results of our experiments demonstrate the usefulness of our approach and the potential for further development.
Colour pattern influences behaviour and affects survival of organisms through perception of light reflectance. Spectrophotometric methods used to study colour optimise precision and accuracy of reflectance across wavelengths, while multiband photographs are generally used to assess the complexity of colour patterns. Using standardised photographs of sand lizards (Lacerta agilis), we compare how colours characterised using point measurements (using the photographs, but simulating spectrophotometry) on the skin differ from colours estimated by clustering pixels in the photograph of the lizard's body. By taking photographs in the laboratory and in the field, the experimental design included two 2-way comparisons. We compare point vs. colour clustering characterisation and influence of illumination in the laboratory and in the field. We found that point measurements adequately represented the dominant colour of the lizard. Where colour patterning influenced measurement geometry, image analysis outperformed point measurement with respect to stability between technical replicates on the same animal. The greater colour variation derived from point measurements increased further under controlled laboratory illumination. Both methods revealed lateral colour asymmetry in sand lizards, i.e. that colours subtly differed between left and right flank. We conclude that studies assessing the impact of colour on animal ecology and behaviour should utilise hyperspectral imaging, followed by image analysis that encompasses the whole colour pattern.
Within the zoological disciplines the study of mammalian hair has mostly been limited to crossspecies comparisons, but there is also considerable intraspecifi c variation in hair characteristics that may be biologically meaningful and deserving of study, though it can be tedious to manually measure hundreds of hairs under a microscope. Here a method is presented for assessing a variety of morphological characteristics of mammalian hairs that is fast, nearly fully-automated, does not require a microscope, and that could easily be used by wildlife biologists or researchers studying museum skins. Using hair samples from 6 captive white-tailed deer (Odocoileus virginianus) hairs were placed in groups of ten on white 3 x 5 inch index cards and covered with clear packing tape. Cards were scanned with a standard fl atbed scanner at high resolution (1200dpi) and the images imported into a computer image analysis program. The program automatically selected and measured each hair, relayed the data to a text fi le, and cycled through all images so that the 120 deer hairs examined (20 per animal) were all measured within 5 minutes. The data returned included the length of each hair (even if it was curly), the width (the average width of the entire shaft), the 2-dimensional surface area, as well as the colour of the hair, measured with hue and brightness scores averaged over the entire shaft. These data are well-suited for examining questions regarding factors infl uencing the morphology or colour of mammalian pelage, or for using hair morphology to assess the nutritional status of individuals, as is done with humans. When measurements are completed, cards can be conveniently stored, either in an index card box or ringed binder, and they can even be re-scanned (at higher resolutions, for example) if needed. Alternatively, the index card step could be skipped and hairs could be scanned loosely in batches. Either way, this method should allow zoological researchers to pursue a wide variety of questions relating to mammalian hair morphology.