Abstract. We present a dynamic laser speckle method to easily discriminate filamentous fungi from motile bacteria in soft surfaces, such as agar plate. The method allows the detection and discrimination between fungi and bacteria faster than with conventional techniques. The new procedure could be straightforwardly extended to different micro-organisms, as well as applied to biological and biomedical research, infected tissues analysis, and hospital water and wastewaters studies.
In this work we present a method to evaluate activity in low dynamic speckle patterns. It consists of binarizing the speckle image and analyzing the displacements and deformations of the resulting speckle grain regions, here called islands. Numerical simulations and controlled experiments were used to study the variations of the island features with the aim of finding a correlation with the activity of the speckle pattern. From the obtained results it was possible to conclude that the developed method can be useful for the analysis of low activity speckle patterns with the advantage of requiring only pairs of frames, thus permitting the assessment of nonstationary processes. In the case of stationary phenomena, so that stacks of frames registers are representative of them, dilute activity images can also be constructed.
Abstract:The movement of the microorganisms towards a higher concentration of the chemical attractant is called positive chemotaxis and is involved in the efficiency of chemical degradation. Several studies are focused in this field related to genomics, and towards demonstrating chemotactic responses by bacteria, but there is little information related to the activity and morphology of their response. In this work, we use a recently reported dynamic speckle laser method, to process images and to distinguish motile surface patterns per area of colonisation by applying image processing techniques called fuzzy mathematical morphology (FMM). The images of bacterial colonies are usually surfaced, with vague edges and non-homogeneous grey levels. Hence, conventional image processing methods for shape analysis cannot be applied in these cases. In this paper, we propose the application FMM to solve this problem. The approach given was effective to segment, detect and also to describe colonisation patterns.
60M. Nisenbaum et al.
The aim of this work is to build a computational model able to automatically identify, after training, dynamic speckle pattern regions with similar properties. The process is carried out using a set of descriptors applied to the intensity variations with time in every pixel of a speckle image sequence. An image obtained by projecting a self-organized map is converted into regions of similar activity that can be easily distinguished. We propose a general procedure that could be applied to numerous situations. As examples we show different situations: (a) an activity test in a simplified situation; (b) a non-biological example and (c) biological active specimens. The results obtained are encouraging; they significantly improve upon those obtained using a single descriptor and will eventually permit automatic quantitative assessment.
In this work, the design of a multi-descriptor model is proposed to improve the identification of activity levels in dynamic laser speckle image sequences. Fuzzy predicates evaluated with operators of different logic systems were used. The model considers both temporal and spatial effects of the dynamic laser speckle phenomenon. Experiments were carried out to characterize the drying times of a sample painted with water-based paint, illuminated with a coherent light, whose image sequences were captured with a ccd camera. To assess the improvement of the proposed model with respect to the single descriptors, the Kruskal-Wallis test and the silhouette index were used. The compensatory logic operators achieved the best results to improve the discrimination of the activity levels, according to the different drying times in the dynamic laser speckle sequences.
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