The Experts below are selected from a list of 29262 Experts worldwide ranked by ideXlab platform

Kai Wang - One of the best experts on this subject based on the ideXlab platform.

  • a survey for the applications of content based Microscopic Image analysis in microorganism classification domains
    Artificial Intelligence Review, 2019
    Co-Authors: Kai Wang
    Abstract:

    Microorganisms such as protozoa and bacteria play very important roles in many practical domains, like agriculture, industry and medicine. To explore functions of different categories of microorganisms is a fundamental work in biological studies, which can assist biologists and related scientists to get to know more properties, habits and characteristics of these tiny but obbligato living beings. However, taxonomy of microorganisms (microorganism classification) is traditionally investigated through morphological, chemical or physical analysis, which is time and money consuming. In order to overcome this, since the 1970s innovative content-based Microscopic Image analysis (CBMIA) approaches are introduced to microbiological fields. CBMIA methods classify microorganisms into different categories using multiple artificial intelligence approaches, such as machine vision, pattern recognition and machine learning algorithms. Furthermore, because CBMIA approaches are semi- or full-automatic computer-based methods, they are very efficient and labour cost saving, supporting a technical feasibility for microorganism classification in our current big data age. In this article, we review the development history of microorganism classification using CBMIA approaches with two crossed pipelines. In the first pipeline, all related works are grouped by their corresponding microorganism application domains. By this pipeline, it is easy for microbiologists to have an insight into each special application domain and find their interested applied CBMIA techniques. In the second pipeline, the related works in each application domain are reviewed by time periods. Using this pipeline, computer scientists can see the dynamic of technological development clearly and keep up with the future development trend in this interdisciplinary field. In addition, the frequently-used CBMIA methods are further analysed to find technological common points and potential reasons.

  • degradation monitoring of low voltage electromagnetic coil insulation based on Microscopic Image analysis
    Prognostics and System Health Management Conference, 2018
    Co-Authors: Kai Wang, Aidong Xu, Chen Li, Fanjie Kong, Shouliang Qi
    Abstract:

    Electromagnetic coils are widely used in a variety of industries, and their insulation damage is one of the main factors which results in failure of solenoid-operated valves and motors. This paper provides a novel method for degradation monitoring of low-voltage coil insulation based on Microscopic Image analysis. Degradation-sensitive color features from RGB, HSV and HSL color spaces are identified to quantify the appearance differences between healthy and degraded magnet wires, which provides a new way for coil health monitoring. Comparing to the existing high-frequency electrical signal based degradation monitoring methods, the proposed method is low-cost and easy to apply for coil insulation test.

Eric A Barnard - One of the best experts on this subject based on the ideXlab platform.

  • quaternary structure of the native gabaa receptor determined by electron Microscopic Image analysis
    Journal of Neurochemistry, 2002
    Co-Authors: N Nayeem, T P Green, I L Martin, Eric A Barnard
    Abstract:

    In the transmitter-gated ion channel class of receptors, the members of which are all believed to be heterooligomers, the number and arrangement of the subunits are only known with any certainty for the nicotinic acetylcholine receptor from Torpedo electric fish. That receptor has been shown to possess a pentameric rosette structure, with five homologous subunits (alpha 2, beta gamma delta) arranged to enclose the central ion channel. The data were obtained by electron Image analysis of two-dimensional receptor arrays, which form as a consequence of that receptor's exceptionally high abundance in the Torpedo membranes and are therefore not attainable for other receptors. We have applied another direct approach to determine the quaternary structure of native ionotropic GABA receptors. We have purified those receptors from porcine brain cortex and analysed the rotational symmetry of isolated receptors visualized by electron microscopy. The results show the receptor to have a pentameric structure with a central water-filled pore, which can now be said to be characteristic of the entire superfamily.

Krok Franciszek - One of the best experts on this subject based on the ideXlab platform.

  • Automatic Microscopic Image analysis by moving window local Fourier Transform and Machine Learning
    'Elsevier BV', 2020
    Co-Authors: Jany Benedykt, Janas Arkadiusz, Krok Franciszek
    Abstract:

    Analysis of microscope Images is a tedious work which requires patience and time, usually done manually by the microscopist after data collection. The results obtained in such a way might be biased by the human who performed the analysis. Here we introduce an approach of automatic Image analysis, which is based on locally applied Fourier Transform and Machine Learning methods. In this approach, a whole Image is scanned by a local moving window with defined size and the 2D Fourier Transform is calculated for each window. Then, all the Local Fourier Transforms are fed into Machine Learning processing. Firstly, a number of components in the data is estimated from Principal Component Analysis (PCA) Scree Plot performed on the data. Secondly, the data are decomposed blindly by Non-Negative Matrix Factorization (NMF) into interpretable spatial maps (loadings) and corresponding Fourier Transforms (factors). As a result, the Microscopic Image is analyzed and the features on the Image are automatically discovered, based on the local changes in Fourier Transform, without human bias. The user selects only a size and movement of the scanning local window which defines the final analysis resolution. This automatic approach was successfully applied to analysis of various Microscopic Images with and without local periodicity i.e. atomically resolved High Angle Annular Dark Field (HAADF) Scanning Transmission Electron Microscopy (STEM) Image of Au nanoisland of fcc and Au hcp phases, Scanning Tunneling Microscopy (STM) Image of Au-induced reconstruction on Ge(001) surface, Scanning Electron Microscopy (SEM) Image of metallic nanoclusters grown on GaSb surface, and Fluorescence microscopy Image of HeLa cell line of cervical cancer. The proposed approach could be used to automatically analyze the local structure of Microscopic Images within a time of about a minute for a single Image on a modern desktop/notebook computer and it is freely available as a Python analysis notebook and Python program for batch processing

  • Automatic Microscopic Image analysis by moving window local Fourier Transform and Machine Learning
    'Elsevier BV', 2019
    Co-Authors: Jany, Benedykt R., Janas Arkadiusz, Krok Franciszek
    Abstract:

    Analysis of microscope Images is a tedious work which requires patience and time, usually done manually by the microscopist after data collection. Here we introduce an approach of automatic Image analysis, which is based on locally applied Fourier Transform and Machine Learning methods. In this approach, a whole Image is scanned by a local moving window with defined size and the 2D Fourier Transform is calculated for each window. Then, all the Local Fourier Transforms are fed into Machine Learning processing. Firstly, a number of components in the data is estimated from Principal Component Analysis (PCA) Scree Plot performed on the data. Secondly, the data are decomposed blindly by Non-Negative Matrix Factorization (NMF) into interpretable spatial maps (loadings) and corresponding Fourier Transforms (factors). The Microscopic Image is analyzed and the features on the Image are automatically discovered, based on the local changes in Fourier Transform. The user selects only a size and movement of the scanning local window which defines the final analysis resolution. This automatic approach was successfully applied to analysis of various Microscopic Images with and without local periodicity i.e. atomically resolved High Angle Annular Dark Field (HAADF) Scanning Transmission Electron Microscopy (STEM) Image of Au nanoisland of fcc and Au hcp phases, Scanning Tunneling Microscopy (STM) Image of Au-induced reconstruction on Ge(001) surface, Scanning Electron Microscopy (SEM) Image of metallic nanoclusters grown on GaSb surface, and Fluorescence microscopy Image of HeLa cell line of cervical cancer. The proposed approach could be used to automatically analyze the local structure of Microscopic Images within a time of about a minute for a single Image on a modern desktop/notebook computer and it is freely available as a Python analysis notebook and Python program for batch processing

Kosuke Morikawa - One of the best experts on this subject based on the ideXlab platform.

  • Open clamp structure in the clamp-loading complex visualized by electron Microscopic Image analysis.
    Proceedings of the National Academy of Sciences of the United States of America, 2005
    Co-Authors: Tomoko Miyata, Hirofumi Suzuki, Takuji Oyama, Kouta Mayanagi, Yoshizumi Ishino, Kosuke Morikawa
    Abstract:

    Ring-shaped sliding clamps and clamp loader ATPases are essential factors for rapid and accurate DNA replication. The clamp ring is opened and resealed at the primer–template junctions by the ATP-fueled clamp loader function. The processivity of the DNA polymerase is conferred by its attachment to the clamp loaded onto the DNA. In eukarya and archaea, the replication factor C (RFC) and the proliferating cell nuclear antigen (PCNA) play crucial roles as the clamp loader and the clamp, respectively. Here, we report the electron Microscopic structure of an archaeal RFC–PCNA–DNA complex at 12-A resolution. This complex exhibits excellent fitting of each atomic structure of RFC, PCNA, and the primed DNA. The PCNA ring retains an open conformation by extensive interactions with RFC, with a distorted spring washer-like conformation. The complex appears to represent the intermediate, where the PCNA ring is kept open before ATP hydrolysis by RFC.

Enmin Song - One of the best experts on this subject based on the ideXlab platform.

  • bone marrow cells detection a technique for the Microscopic Image analysis
    Journal of Medical Systems, 2019
    Co-Authors: Hong Liu, Haichao Cao, Enmin Song
    Abstract:

    In the detection of myeloproliferative, the number of cells in each type of bone marrow cells (BMC) is an important parameter for the evaluation. In this study, we propose a new counting method, which consists of three modules including localization, segmentation and classification. The localization of BMC is achieved from a color transformation enhanced BMC sample Image and stepwise averaging method. In the nucleus segmentation, both stepwise averaging method and Otsu’s method are applied to obtain a weighted threshold for segmenting the patch into nucleus and non-nucleus. In the cytoplasm segmentation, a color weakening transformation, an improved region growing method and the K-Means algorithm are employed. The connected cells with BMC will be separated by the marker-controlled watershed algorithm. The features will be extracted for the classification after the segmentation. In this study, the BMC are classified using the support vector machine into five classes; namely, neutrophilic split granulocyte, neutrophilic stab granulocyte, metarubricyte, mature lymphocytes and the outlier (all other cells not listed). Experimental results show that the proposed method achieves superior segmentation and classification performance with an average segmentation accuracy of 91.76% and an average recall rate of 87.49%. The comparison shows that the proposed segmentation and classification methods outperform the existing methods.