A low-cost automated digital microscopy platform for automatic identification of diatoms

This article belongs to the Special Issue Advanced Intelligent Imaging Technology Ⅱ[EN] Currently, microalgae (i.e., diatoms) constitute a generally accepted bioindicator of water quality and therefore provide an index of the status of biological ecosystems. Diatom detection for specimen counting and sample classification are two difficult time-consuming tasks for the few existing expert diatomists. To mitigate this challenge, in this work, we propose a fully operative low-cost automated microscope, integrating algorithms for: (1) stage and focus control, (2) image acquisition (slide scanning, stitching, contrast enhancement), and (3) diatom detection and a prospective specimen classification (among 80 taxa). Deep learning algorithms have been applied to overcome the difficult selection of image descriptors imposed by classical machine learning strategies. With respect to the mentioned strategies, the best results were obtained by deep neural networks with a maximum precision of 86% (with the YOLO network) for detection and 99.51% for classification, among 80 different species (with the AlexNet network). All the developed operational modules are integrated and controlled by the user from the developed graphical user interface running in the main controller. With the developed operative platform, it is noteworthy that this work provides a quite useful toolbox for phycologists in their daily challenging tasks to identify and classify diatomsSIThis research was funded by the Spanish Government under the AQUALITAS-RETOS project with Ref. CTM2014-51907-C2-2-R-MINEC

Automated Diatom Classification (Part A): Handcrafted Feature Approaches

This article belongs to the Special Issue Automated Analysis and Identification of Phytoplankton Images[EN] This paper deals with automatic taxa identification based on machine learning methods. The aim is therefore to automatically classify diatoms, in terms of pattern recognition terminology. Diatoms are a kind of algae microorganism with high biodiversity at the species level, which are useful for water quality assessment. The most relevant features for diatom description and classification have been selected using an extensive dataset of 80 taxa with a minimum of 100 samples/taxon augmented to 300 samples/taxon. In addition to published morphological, statistical and textural descriptors, a new textural descriptor, Local Binary Patterns (LBP), to characterize the diatom’s valves, and a log Gabor implementation not tested before for this purpose are introduced in this paper. Results show an overall accuracy of 98.11% using bagging decision trees and combinations of descriptors. Finally, some phycological features of diatoms that are still difficult to integrate in computer systems are discussed for future workSIThe authors acknowledge financial support of the Spanish Government under the Aqualitas-retos project (Ref. CTM2014-51907-C2-2-R-MINECO), http://aqualitas-retos.es/en

Lights and pitfalls of convolutional neural networks for diatom identification

P. 1-10Diatom detection has been a challenging task for computer scientist and biologist during past years. In this work, the new state of art techniques based on the deep learning framework have been tested, in order to check whether they are suitable for this purpose. On the one hand, RCNNs (Region based Convolutional Neural Networks), which select candidate regions and applies a convolutional neural network and, on the other hand, YOLO (You Only Look Once), which applies a single neural network over the whole image, have been tested. The first one is able to reach poor results in out experimentation, with an average of 0.68 recall and some tricky aspects, as for example it is needed to apply a bounding box merging algorithm to get stable detections; but the second one gets remarkable results, with an average of 0.84 recall in the evaluation that have been carried out, and less aspects to take into account after the detection has been performed. Future work related to parameter tuning and processing are needed to increase the performance of deep learning in the detection task. However, as for classification it has been probed to provide succesfully performance.S

Low-cost oblique illumination: an image quality assessment

P. 1-14We study the effectiveness of several low-cost oblique illumination filters to improve overall image quality, in comparison with standard bright field imaging. For this purpose, a dataset composed of 3360 diatom images belonging to 21 taxa was acquired. Subjective and objective image quality assessments were done. The subjective evaluation was performed by a group of diatom experts by psychophysical test where resolution, focus, and contrast were assessed. Moreover, some objective nonreference image quality metrics were applied to the same image dataset to complete the study, together with the calculation of several texture features to analyze the effect of these filters in terms of textural properties. Both image quality evaluation methods, subjective and objective, showed better results for images acquired using these illumination filters in compari-son with the no filtered image. These promising results confirm that this kind of illumination filters can be a practical way to improve the image quality, thanks to the simple and low cost of the design and manufacturing process.S

Sistema de bajo coste para la digitalización automática de muestras microscópicas

EN este Trabajo de Fin de Grado (TFG) se desarrolla un sistema de microscopía de bajo coste y portátil para la adquisición automática de muestras biológicas y médicas. Este sistema realiza el escaneado y almacenamiento automático y completo de una muestra para su posterior análisis o tratamiento. Además cuenta con funcionalidades adicionales como el enfoque automático de las muestras o la posibilidad de guardar imágenes en cualquier momento. Tanto los sistemas de microscopía como el análisis de la imagen microscópica en su totalidad, conocido como whole slide imaging (WSI), son de gran utilidad en el mundo de la medicina, la biología o la investigación en general pero tienen un coste muy elevado que en algunas situaciones puede ser difícil de afrontar. Por ello, otro de los factores importantes del proyecto es que el sistema desarrollado sea de bajo coste y portátil. Para ello se ha seleccionado un microscopio de bajo coste y se ha desarrollado un prototipo con los elementos necesarios (cámara, motores, hardware de control) para dotarle de las funcionalidades anteriormente mencionadas y permitir su control desde un ordenador. Para el manejo de estas funcionalidades se ha desarrollado una aplicación gráfica para que el usuario pueda gestionar y manejar el sistema completo. Además se ha incluido la opción de controlar el microscopio utilizando un mando infrarrojo. Por último, se han analizado e implementado varias técnicas para obtener imágenes digitales de una calidad óptima para hacer evaluaciones y diagnósticos. Se busca, por tanto, que la calidad sea igual que la de los sistemas de microscopía profesionales. Se han implementado filtros de iluminación y procesados de imagen para la corrección de ciertos defectos de iluminación y eliminar ruido sistemático o artefactos añadidos por la óptica del microscopio o la cámara

Vision and Crowdsensing Technology for an Optimal Response in Security: Project results

This paper describes the progress and work carried out during the execution of the VICTORY project (Vision and Crowdsensing Technology for an Optimal Response in Security). It describes both its motivation and the progress made in the field of managing violent attacks in buildings. It is assumed that these buildings have a WiFi infrastructure that allows indoor localisation through mobile applications and a closed-circuit television system that allows real-time images to be captured. The advances of this project have allowed 1) the design of a crowdsensing application that allows indoor localisation in a reliable way and with a low battery consumption as well as the detection of stampedes and falls; 2) the development of a multi-agent model that simulates human behaviour in the face of a violent attack and 3) new approaches to improve the state of the art in computer vision for the detection of weaponsMinisterio de Economía y Competitividad TIN2017-8211