Texture image analysis: application to the classification of bovine muscles from meat slice images

Background: Medical image analysis application has complex resource requirement. Scheduling Medical image analysis application is the complex task to the grid resources. It is necessary to develop a new model to improve the breast cancer screening process. Proposed novel Meta scheduler algorithm allocate the image analyse applications to the local schedulers and local scheduler submit the job to the grid node which analyses the medical image and generates the result sent back to Meta scheduler. Meta schedulers are distinct from the local scheduler. Meta scheduler and local scheduler have the aim at resource allocation and management. Objective: The main objective of the CDAM meta-scheduler is to maximize the number of jobs accepted. Methods: In the beginning, the user sends jobs with the deadline to the global grid resource broker. Resource providers sent information about the available resources connected in the network at a fixed interval of time to the global grid resource broker, the information such as valuation of the resource and number of an available free resource. CDAM requests the global grid resource broker for available resources details and user jobs. After receiving the information from the global grid resource broker, it matches the job with the resources. CDAM sends jobs to the local scheduler and local scheduler schedule the job to the local grid site. Local grid site executes the jobs and sends the result back to the CDAM. Success full completion of the job status and resource status are updated into the auction history database. CDAM collect the result from all local grid site and return to the grid users. Results: The CDAM was simulated using grid simulator. Number of jobs increases then the percentage of the jobs accepted also decrease due to the scarcity of resources. CDAM is providing 2% to 5% better result than Fair share Meta scheduling algorithm. CDAM algorithm bid density value is generated based on the user requirement and user history and ask value is generated from the resource details. Users who, having the most significant deadline are generated the highest bid value, grid resource which is having the fastest processor are generated lowest ask value. The highest bid is assigned to the lowest Ask it means that the user who is having the most significant deadline is assigned to the grid resource which is having the fastest processor. The deadline represents a time by which the user requires the result. The user can define the deadline by which the results are needed, and the CDAM will try to find the fastest resource available in order to meet the user-defined deadline. If the scheduler detects that the tasks cannot be completed before the deadline, then the scheduler abandons the current resource, tries to select the next fastest resource and tries until the completion of application meets the deadline. CDAM is providing 25% better result than grid way Meta scheduler this is because grid way Meta scheduler allocate jobs to the resource based on the first come first served policy. Conclusion: The proposed CDAM model was validated through simulation and was evaluated based on jobs accepted. The experimental results clearly show that the CDAM model maximizes the number of jobs accepted than conventional Meta scheduler. We conclude that a CDAM is highly effective meta-scheduler systems and can be used for an extraordinary situation where jobs have a combinatorial requirement.

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Formation of an Export-Oriented Agricultural Economy and Regional Open Innovations

Journal of Open Innovation Technology Market and Complexity ◽

10.3390/joitmc7010032 ◽

2021 ◽

Vol 7 (1) ◽

pp. 32

Author(s):

Victor L. Shabanov ◽

Marianna Ya Vasilchenko ◽

Elena A. Derunova ◽

Andrey P. Potapov

Keyword(s):

Agricultural Economy ◽

Institutional Management ◽

Agricultural Exports ◽

Modeling Methodology ◽

Fixed Assets ◽

Analysis Application ◽

The Impact ◽

The Relationship

The aim of the work is to find relevant indicators for assessing the relationship between investments in fixed assets in agriculture, gross output of the industry, and agricultural exports using tools for modeling the impact of innovation and investment development on increasing production and export potential in the context of the formation of an export-oriented agricultural economy. The modeling methodology and the proposed estimating and forecasting tools for diagnosing and monitoring the state of sectoral and regional innovative agricultural systems are used to analyze the relationship between investments in fixed assets in agriculture, gross output of the industry, and agricultural exports based on the construction of the classification of Russian regions by factors that aggregate these features to diagnose incongruence problems and to improve institutional management in regional innovative export-oriented agrosystems. Based on the results of the factor analysis application, an underestimated role of indicators of investment in agriculture, the intensity and efficiency of agricultural production, were established. Based on the results of the cluster analysis, the established five groups of regions were identified, with significant differences in the level of investment in agriculture, the volume of production of the main types of agricultural products, and the export and exported food. The research results are of practical value for use in improving institutional management when planning reforms and transformations of regional innovative agrosystems.

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Author Correction: Efficient microbial colony growth dynamics quantification with ColTapp, an automated image analysis application

Scientific Reports ◽

10.1038/s41598-021-85033-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Julian Bär ◽

Mathilde Boumasmoud ◽

Roger D. Kouyos ◽

Annelies S. Zinkernagel ◽

Clément Vulin

Keyword(s):

Image Analysis ◽

Growth Dynamics ◽

Colony Growth ◽

Automated Image Analysis ◽

Analysis Application

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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A rapid quantitation of cell attachment and spreading based on digital image analysis: application for cell affinity and compatibility assessment of synthetic polymers

Materials Science and Engineering C ◽

10.1016/j.msec.2021.112267 ◽

2021 ◽

pp. 112267

Author(s):

Zongliang Wang ◽

Yueming Guo ◽

Peibiao Zhang

Keyword(s):

Image Analysis ◽

Digital Image ◽

Digital Image Analysis ◽

Cell Attachment ◽

Synthetic Polymers ◽

Analysis Application ◽

Cell Affinity

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Classification of total carotene and quality of chili pepper (Capsicum frutescens) based on image analysis

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/733/1/012005 ◽

2021 ◽

Vol 733 (1) ◽

pp. 012005

Author(s):

Y Hendrawan ◽

R Utami ◽

D Y Nurseta ◽

Daisy ◽

S Nuryani ◽

...

Keyword(s):

Image Analysis ◽

Chili Pepper ◽

Capsicum Frutescens

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Time-frequency time-space long short-term memory networks for image classification of histopathological tissue

Scientific Reports ◽

10.1038/s41598-021-93160-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Tuan D. Pham

Keyword(s):

Image Analysis ◽

Short Term Memory ◽

Disease Diagnosis ◽

Short Term ◽

Term Memory ◽

Time Frequency ◽

Time Space ◽

Histopathological Images ◽

Long Short Term Memory

AbstractImage analysis in histopathology provides insights into the microscopic examination of tissue for disease diagnosis, prognosis, and biomarker discovery. Particularly for cancer research, precise classification of histopathological images is the ultimate objective of the image analysis. Here, the time-frequency time-space long short-term memory network (TF-TS LSTM) developed for classification of time series is applied for classifying histopathological images. The deep learning is empowered by the use of sequential time-frequency and time-space features extracted from the images. Furthermore, unlike conventional classification practice, a strategy for class modeling is designed to leverage the learning power of the TF-TS LSTM. Tests on several datasets of histopathological images of haematoxylin-and-eosin and immunohistochemistry stains demonstrate the strong capability of the artificial intelligence (AI)-based approach for producing very accurate classification results. The proposed approach has the potential to be an AI tool for robust classification of histopathological images.

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Validation of a new image analysis procedure for quantifying filamentous bacteria in activated sludge

Water Science & Technology ◽

10.2166/wst.2014.296 ◽

2014 ◽

Vol 70 (6) ◽

pp. 955-963 ◽

Cited By ~ 4

Author(s):

Ewa Liwarska-Bizukojc ◽

Marcin Bizukojc ◽

Olga Andrzejczak

Keyword(s):

Image Processing ◽

Image Analysis ◽

Activated Sludge ◽

Analysis Procedure ◽

Automated Image Analysis ◽

Filamentous Bacteria ◽

Image Processing And Analysis ◽

Manual Counting ◽

Analysis Application ◽

Activated Sludge Systems

Quantification of filamentous bacteria in activated sludge systems can be made by manual counting under a microscope or by the application of various automated image analysis procedures. The latter has been significantly developed in the last two decades. In this work a new method based upon automated image analysis techniques was elaborated and presented. It consisted of three stages: (a) Neisser staining, (b) grabbing of microscopic images, and (c) digital image processing and analysis. This automated image analysis procedure possessed the features of novelty. It simultaneously delivered data about aggregates and filaments in an individual calculation routine, which is seldom met in the procedures described in the literature so far. What is more important, the macroprogram performing image processing and calculation of morphological parameters was written in the same software which was used for grabbing of images. Previously published procedures required using two different types of software, one for image grabbing and another one for image processing and analysis. Application of this new procedure for the quantification of filamentous bacteria in the full-scale as well as laboratory activated sludge systems proved that it was simple, fast and delivered reliable results.

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