A procedure for the extraction of object features in microscope images

Water monitoring by means of digital microscopy identification and classification of microalgae

Marine and freshwater microalgae belong to taxonomically and morphologically diverse groups of organisms spanning many phyla with thousands of species. These organisms play an important role as indicators of water ecosystem conditions since they react quickly and predictably to a broad range of environmental stressors, thus providing early signals of dangerous changes. Traditionally, microscopic analysis has been used to identify and enumerate different types of organisms present within a given environment at a given point in time. However, this approach is both time-consuming and labor intensive, as it relies on manual processing and classification of planktonic organisms present within collected water samples. Furthermore, it requires highly skilled specialists trained to recognize and distinguish one taxa from another on the basis of often subtle morphological differences. Given these restrictions, a considerable amount of effort has been recently funneled into automating different steps of both the sampling and classification processes, making it possible to generate previously unprecedented volumes of plankton image data and obtain an essential database to analyze the composition of plankton assemblages. In this review we report state-of-the-art methods used for automated plankton classification by means of digital microscopy. The computer-microscope system hardware and the image processing techniques used for recognition and classification of planktonic organisms (segmentation, shape feature extraction, pigment signature determination and neural network grouping) will be described. An introduction and overview of the topic, its current state and indications of future directions the field is expected to take will be provided, organizing the review for both experts and researchers new to the field.

Water monitoring: automated and real time identification and classification of algae using digital microscopy

Microalgae are unicellular photoautotrophs that grow in any habitat from fresh and saline water bodies, to hot springs and ice. Microalgae can be used as indicators to monitor water ecosystem conditions. These organisms react quickly and predictably to a broad range of environmental stressors, thus providing early signals of a changing environment. When grown extensively, microalgae may produce harmful effects on marine or freshwater ecology and fishery resources. Rapid and accurate recognition and classification of microalgae is one of the most important issues in water resource management. In this paper, a methodology for automatic and real time identification and enumeration of microalgae by means of image analysis is presented. The methodology is based on segmentation, shape feature extraction, pigment signature determination and neural network grouping; it attained 98.6% accuracy from a set of 53,869 images of 23 different microalgae representing the major algal phyla. In our opinion this methodology partly overcomes the lack of automated identification systems and is on the forefront of developing a computer-based image processing technique to automatically detect, recognize, identify and enumerate microalgae genera and species from all the divisions. This methodology could be useful for an appropriate and effective water resource management.

Automatic and real time recognition of microalgae by means of pigment signature and shape

Microalgae are unicellular photoautotrophic organisms that grow in any habitat such as fresh and salt water bodies, hot springs, ice, air, and in or on other organisms and substrates. Massive growth of microalgae may produce harmful effects on the marine and freshwater ecological environment and fishery resources. Therefore, rapid and accurate recognition and classification of microalgae is one of the most important issues in water resource management. In this paper, a new methodology for automatic and real time identification of microalgae by means of microscopy image analysis is presented. This methodology is based on segmentation, shape features extraction, and characteristic colour (i.e. pigment signature) determination. A classifier algorithm based on the minimum distance criterion was used for microalgae grouping according to the measured features. 96.6% accuracy from a set of 3423 images of 24 different microalgae representing the major algal phyla was achieved by this methodology.

In vivo photocycle of the Euglena gracilis photoreceptor

We present the light-induced photocycle of the paraflagellar swelling of Euglena gracilis. The kinetics of this process was reconstructed by sampling its fluorescence emission and switching the excitation light from 365 nm to 436 nm. Stable intermediates in the photocycle were manifested. The measured millisecond resolution kinetics best fits a Michaelis-Menten equation. The data provide strong evidence that the paraflagellar swelling, a three-dimensional natural crystal of a light-detecting protein, is the true Euglena photoreceptor.