Automatic Recognition of Light Microscope Pollen Images

This paper is a progress report on a project aimed at the realization of a low-cost, automatic, trainable system "AutoStage" for recognition and counting of pollen. Previous work on image feature selection and classification has been extended by design and integration of an XY stage to allow slides to be scanned, an auto focus system, and segmentation software. The results of a series of classification tests are reported, and verified by comparison with classification performance by expert palynologists. A number of technical issues are addressed, including pollen slide preparation and slide sampling protocols

Two Supervised Neural Networks for Classification ofSedimentary Organic Matter Images fromPalynological Preparations

An improvement in the supervised artificial neural network classification of sedimentary organic matter images from palynological preparations is presented. Sedimentary organic matter encompasses the entire acid-resistant organic micro-particles (typically with a diameter of 5-500μm) recovered from a sediment or sedimentary rock. Supervised neural networks are trained to recognize patterns within databases for which the correct classifications are already known. Once trained, they are verified on pre-classified samples not seen by the network, and then used for classification of samples whose class is not known. Such networks have an input, hidden and output layer. Typically, these networks determine what the output class is by adjusting weights associated with the layer interconnects, and by modifying the signals that propagate through the hidden layer by a non-linear transfer function. In this example, the inputs in each network are the salient features selected from an available set of 194, while the outputs are the sedimentary organic matter classifications which were formerly developed with the rationalization of descriptive terms from previous classification schemes. The author's past work tested the supervised back propagation neural network for the classification of sedimentary organic matter images. This gave an overall correct classification rate of 87%. However, because the back propagation network underperformed on two of the four classes, the radial basis function neural network was tested on the same databases initially used in an attempt to improve the recognition rate of these two classes. The difference between the back propagation and radial basis function networks lies in the non-linear transfer function applied in the hidden layer, which was modified by a Gaussian function in the latter. In the best-case scenario, this improved the recognition rate by 4% to just over 91%. This has also determined that a series of different supervised neural networks may be better for classification of sedimentary organic matter images. These results are encouraging enough to prompt further research that may result in a commercially viable syste

Characterization of Microparticles through Digital Holography

In this work, digital holography (DH) is extensively utilized to characterize microparticles. Here, “characterization” refers to the determination of a particle’s shape, size, and, in some cases, its surface structure. A variety of microparticles, such as environmental dust, pollen, volcanic ash, clay, and biological samples, are thoroughly analyzed. In this technique, the microscopically fine interference pattern generated by the coherent superposition of an object and a reference wave fields is digitally recorded using an optoelectronic sensor, in the form of a hologram, and the desired particle property is then computationally extracted by performing a numerical reconstruction to form an image of the particle. The objective of this work is to explore, develop, and demonstrate the feasibility of different experimental arrangements to reconstruct the image of various arbitrary-shaped particles. Both forward- and backward-scattering experimental arrangements are constructed and calibrated to quantify the size of several micron-sized particles. The performance and implications of the technique are validated using the National Institute of Standards and Technology (NIST)-traceable borosilicate glass microspheres of various diameters and a Thorlabs resolution plate. After successful validation and calibration of the system, the resolution limit of the experimental setup is estimated, which is ~10 microns. Particles smaller than 10 microns in size could not be imaged well enough to ensure that what appeared like a single particle was not in fact a cluster. The forward- and backward-scattering holograms of different samples are recorded simultaneously and images of the particles are then computationally reconstructed from these recorded holograms. Our results show that the forward- and backward-scattering images yield different information on the particle surface structure and edge roughness, and thus, reveal more information about a particle profile. This suggests that the two image perspectives reveal aspects of the particle structure not available from a more commonly used forward-scattering based image alone. The results of this work could be supportive to insight more on the particles’ morphology and subsequently important for the advancement of contactree particle characterization technique

Texture Segregation By Visual Cortex: Perceptual Grouping, Attention, and Learning

A neural model is proposed of how laminar interactions in the visual cortex may learn and recognize object texture and form boundaries. The model brings together five interacting processes: region-based texture classification, contour-based boundary grouping, surface filling-in, spatial attention, and object attention. The model shows how form boundaries can determine regions in which surface filling-in occurs; how surface filling-in interacts with spatial attention to generate a form-fitting distribution of spatial attention, or attentional shroud; how the strongest shroud can inhibit weaker shrouds; and how the winning shroud regulates learning of texture categories, and thus the allocation of object attention. The model can discriminate abutted textures with blurred boundaries and is sensitive to texture boundary attributes like discontinuities in orientation and texture flow curvature as well as to relative orientations of texture elements. The model quantitatively fits a large set of human psychophysical data on orientation-based textures. Object boundar output of the model is compared to computer vision algorithms using a set of human segmented photographic images. The model classifies textures and suppresses noise using a multiple scale oriented filterbank and a distributed Adaptive Resonance Theory (dART) classifier. The matched signal between the bottom-up texture inputs and top-down learned texture categories is utilized by oriented competitive and cooperative grouping processes to generate texture boundaries that control surface filling-in and spatial attention. Topdown modulatory attentional feedback from boundary and surface representations to early filtering stages results in enhanced texture boundaries and more efficient learning of texture within attended surface regions. Surface-based attention also provides a self-supervising training signal for learning new textures. Importance of the surface-based attentional feedback in texture learning and classification is tested using a set of textured images from the Brodatz micro-texture album. Benchmark studies vary from 95.1% to 98.6% with attention, and from 90.6% to 93.2% without attention.Air Force Office of Scientific Research (F49620-01-1-0397, F49620-01-1-0423); National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

Hardware and software integration and testing for the automation of bright-field microscopy for tuberculosis detection

Automated microscopy for the detection of tuberculosis (TB) in sputum smears would reduce the load on technicians, especially in countries with a high TB burden. This dissertation reports on the development and testing of an automated system built around a conventional microscope for the detection of TB in Ziehl-Neelsen (ZN) stained sputum smears. Microscope auto-focusing, image analysis and stage movement were integrated. Images were captured at 40x magnification

Assessment of multi - date sentinel - 1 polarizations and GLCM texture features capacity for onion and sunflower classification in an irrigated valley: An object level approach

The objective of this work is to evaluate the capacity of the C-band Synthetic Aperture Radar (SAR) time series imagery, acquired by the European satellite Sentinel-1 (S1), for the agriculture crop classification and its reliability to differentiate onion from sunflower, among others. The work then focused on classifying land cover in intensively cultivated agricultural regions. The study was developed in the Bonaerense Valley of the Colorado River (BVCR), Buenos Aires Province in Argentina, backed up by the field truth of 1634 field samples. In addition to the onion and sunflower crops, there are other crops present in the study area such as cereals, alfalfa, potatoes and maize, which are considered as the image background in the classification process. The field samples database was used for training and supporting a supervised classification with two machine learning algorithms—Random Forest (RF) and Support Vector Machine (SVM)—obtaining high levels of accuracy in each case. Different S1 SAR time-series features were used to assess the performance of S1 crop classification in terms of polarization VH+VV, Grey Level Co-occurrence Matrix (GLCM) image texture and a combination of both. The analysis of SAR data and their features was carried out at OBIA lot level (Object Based Image Analysis) showing an optimal strategy to counteract the effect of the residual and inherent speckle noise of the radar signal. In the process of differentiating onion and sunflower crops, the analysis of the VH+VV stack with the SVM algorithm delivered the best statistical classification results in terms of Overall Accuracy (OA) and Kappa Index, (Kp) when other crops (image background) were not considered (OA = 95.35%, Kp = 0.89). Certainly, the GLCM texture analysis derived from the S1 SAR images is a valuable source of information for obtaining very good classification results. When differentiating sunflower from onion considering also other crops present in the BVCR, the GLCM stack proved to be the most suitable dataset analyzed in this work (OA = 89.98%, Kp = 0.66 for SVM algorithm). This working methodology is applicable to other irrigated valleys in Argentina dedicated to intensive crops. There are also variables inherent to each lot, soil, crop and agricultural producer that differ according to the study area and that should be considered for each case in the future.Fil: Caballero, Gabriel. Universidad Blas Pascal. Centro de Investigación y Desarrollo Aplicado en Informática y Telecomunicaciones (CIADE-IT); ArgentinaFil: Platzech, Gabriel. INVAP. Government & Security Division; ArgentinaFil: Pezzola, Alejandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Casella, Alejandra. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Winschel, Cristina Ines. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Silva, Samanta. Ministerio de Desarrollo Agrario (Buenos Aires, provincia). Colorado River Development Corporation (CORFO); ArgentinaFil: Ludueña, Emilia. INGTRADUCCIONES; ArgentinaFil: Pasqualotto, Nieves. Universidad de Valencia. Image Processing Laboratory (IPL); EspañaFil: Delegido, Jesús. Universidad de Valencia. Image Processing Laboratory (IPL); Españ

Imaging nanoscale pollen morphology with Superresolution Structured Illumination Microscopy

Applications in the plant sciences that stem from disciplines as diverse as phylogenetics, physiology, and paleoecology all require ever-increasing imaging resolutions for accurate investigations of morphological hypotheses. Of these applications, the visualization of nanoscale plant morphology, such as the taxonomically diagnostic surface texture of individual pollen grains, is a special challenge for researchers. However, a combination of high-resolution imaging and computational analyses promises to unveil such nanoscale plant morphology for a whole spectrum of hypotheses, including those that address the taxonomic resolution of fossil pollen records. Therefore, the choice of imaging method for fossil pollen and hypothesized modern plant affinities is critical to research concerning the ecology and evolution of Earth’s biomes. However, the options for visualizing such nanoscale plant morphologies that are smaller than the diffraction limit of light are often limited to electron microscopy, which presents significant disadvantages in routine palynological work compared to optical microscopy. Superresolution Structured Illumination Microscopy (SR-SIM) is an emerging method that presents a powerful, non-destructive, and optically-sectioned way of imaging pollen that avoids certain disadvantages of electron microscopy. We examined and optimized the performance of SR-SIM in recovering the nanoscale surface morphology of the pollen of nine Poaceae species and compare our results to images obtained using Scanning Electron Microscopy (SEM) and an advanced transmitted light method: Laser-Scanning High-Resolution Differential Interference Contrast (LS-HR-DIC). Through our comparisons of resulting images, we appreciated that SR-SIM, LS-HR-DIC, and SEM represent three very different imaging methods. SR-SIM uses fluorescence, LS-HR- DIC uses transmitted light, and SEM uses reflected electrons. Therefore, the results of our study are expected in that the morphological information gathered by SR-SIM, LS-HR-DIC, and SEM is complementary, not identical: SR-SIM recovers three-dimensional features smaller than the diffraction limit, LS-HR-DIC produces diffraction-limited high contrast 3D representations, and SEM provides two-dimensional high-resolution images of an object’s surface. The morphological detail recovered from the SR-SIM is qualitatively comparable to the SEM. SR-SIM also represents an entirely new source of information on nanoscale plant morphologies, such as fine-scale pollen ornamentation and the interior structure of the pollen exine that could be used in conjunction with other standard approaches in optical and electron microscopy. SR-SIM is not a replacement for existing microscopic approaches, but is a viable alternative for material that is, by necessity or choice, mounted on microscopic slides

A survey of visual preprocessing and shape representation techniques

Many recent theories and methods proposed for visual preprocessing and shape representation are summarized. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. It was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections: an overview of biological visual processing; methods of preprocessing (extracting parts of shape, texture, motion, and depth); and shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention)