Recognition of micro-objects with adaptive models of image processing in a parallel computing environment

Jumanov, Isroil I.; Djumanov, Olimjon I.; Safarov, Rustam A.

doi:10.1088/1742-6596/1791/1/012099

Cited by 18 publications

(8 citation statements)

References 3 publications

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“…Recognition and classification of micro-objects, in turn, are associated with the optimization of image identification based on the use of mechanisms that eliminate the noise component -interference, "debris" in the composition of pollen, contrast defects, brightness, which are implemented using numerical modeling, geometric, morphological, texture analysis, as well as using mechanisms for extracting statistical, dynamic and specific characteristics of images [7,8,9,10,11].…”

Section: Intoductionmentioning

confidence: 99%

Error Control of Identification and Filtering of Micro-Object Images

Jumanov¹,

Safarov²,

Djumanov³

2022

Proceedings of III All-Russian Scientific Conference With International Participation "Science, Technology, Society: Environmen

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Researched and developed scientifically and methodologically foundations for optimal identification of micro-objects using traditional and Gaussian filtering, median filter, filters based on fast Fourier transform, wavelet transforms, shift transforms, mechanisms using geometric, specific features, statistical, dynamic properties of image information. Mechanisms for optimizing the identification of micro-objects are proposed that have advantages in reducing the complexity and laboriousness of analyzing the structure and processing information, identifying and segmentation of the image contour, using the dynamics of growth, visual differentiation, extracting internal features and properties, approximation, smoothing, and interpretation of objects. A mechanism has been investigated and implemented that performs the following functions: aligns histology slices; finds contours of objects, a set of levels, thresholds, combines segmentation, conducts registrations, forms a search graph, performs approximations based on a wavelet, shear, and other transformations, determines parameters, performs color coding and color visualization of micro-objects. The implementations of algorithms and software modules of the software complex for identification, recognition and classification of micro-objects, in particular, cellular elements of the inflammatory series (fibroblasts, fibrocytes) of lung disease, have been tested. The signs of chronic inflammation were assessed - the presence of giant cells. A software package for visualization, recognition, classification of images of pollen grains has been developed, the implementations of which have been tested taking into account the conditions of a priori insufficiency, parametric uncertainty and nonstationarity of processes.

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Section: Intoductionmentioning

confidence: 99%

Error Control of Identification and Filtering of Micro-Object Images

Jumanov¹,

Safarov²,

Djumanov³

2022

Proceedings of III All-Russian Scientific Conference With International Participation "Science, Technology, Society: Environmen

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“…The study is related to the accounting, recognition, classification and systematization of various microobjects, for example, pollen grains, unicellular organisms, etc [3,4]. Methods, models, algorithms for the identification of micro-objects are created, which are implemented in the form of software and hardware, information processing complexes that differ from analogues in their functionality, specialization, and level of automation [5,6].…”

Section: Intoductionmentioning

confidence: 99%

Optimization of recognition of micro-objects based on the use of morphometric characteristics of images

Jumanov¹,

Safarov²

2022

Proceedings of III All-Russian Scientific Conference With International Participation "Science, Technology, Society: Environmen

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Constructive approaches, principles and methods of identification, recognition and classification of micro-objects based on the use of neural networks and mechanisms for extracting morphometric characteristics of images have been developed. Information processing technology based on obtaining images of micro-objects from a photo, video camera, digital microscope is proposed. A technique has been developed for interactive measurement of the size of micro-objects, counting, determining the structure, conducting statistical analysis, isolating and segmenting fragments, selecting informative points, recognizing and classifying images. A computational scheme for preliminary processing of images is built, including mechanisms for texture, contour segmentation, detection, and regulation of variables. Algorithms for learning neural networks with setting variables within the limits of permissible values, taking into account the properties of nonstationarity of image points, have been built. The effectiveness of learning algorithms combined with neural network dynamic models with mechanisms for regulating linear, nonlinear, compositional connections of neurons between network layers has been investigated. The study was carried out according to the criterion of the percentage of correct recognition. A software package for visualization, recognition, classification of images of pollen grains has been developed and implemented, which has been tested under conditions of a priori insufficiency, uncertainty and nonstationarity.

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“…A computational scheme for the identification, recognition, and classification of microobjects, in particular, pollen grains, medical objects, is implemented, which includes the following blocks: input image input, preprocessing, identification of objects of interest, highlighting features and image features, recognition, and classification of a micro-object, presentation of results. The effectiveness of the proposed computational scheme is based on real data from the chest X-ray processing (CXR) system [4,5,[13][14][15].…”

Section: Main Part 21 Optimization Of the Identification Of Micro-objects Using The Morphological Features Of Imagesmentioning

confidence: 99%

“…A modified mechanism of vector median filtering (VMF) is proposed, which allows suppressing the noise part, interference in images. The mechanism ensures the preservation of the most important features, properties of objects that are used in detection, selection, segmentation[10,11,12,15]. When filtering images, the mechanism receives new values of the points of the boundaries of the contour of each component, selects windows for points by values of the median in the form…”

mentioning

confidence: 99%

Improving the quality of identification and filtering of micro-object images based on neural networks

2021

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Constructive approaches, principles, and models for optimizing the identification of micro-objects have been developed based on the use of combined statistical, dynamic models and neural networks with mechanisms for filtering noise and foreign particles of images of medical objects and pollen grains. Algorithms for learning neural networks under conditions of a priori insufficiency, uncertainty of parameters, and low accuracy of data processing are investigated. The mechanisms of contour selection, segmentation, obtaining the boundaries of segments with hard and soft thresholds, filtering using the morphological features of the image have been developed [1]. Mechanisms for recognition and classification of images, adaptation of parameter values, tuning of the network structure, approximation and smoothing of random emissions, bursts in the image contour are proposed. A mechanism for suppressing impulse noise and noise is implemented based on various filtering methods, preserving the boundaries of objects and small-sized parts. Mathematical expressions are obtained for estimating the identification errors caused by nonstationarity, inadequacy of approximation, interpolation, and extrapolation of the image contour. A software package for the recognition and classification of micro-objects has been developed. The results were obtained for correct, incorrect recognition, as well as rejected pollen samples, which were synthesized with cubic, biquadratic, interpolation spline-functions and wavelet transforms.

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Recognition of micro-objects with adaptive models of image processing in a parallel computing environment

Cited by 18 publications

References 3 publications

Error Control of Identification and Filtering of Micro-Object Images

Error Control of Identification and Filtering of Micro-Object Images

Optimization of recognition of micro-objects based on the use of morphometric characteristics of images

Improving the quality of identification and filtering of micro-object images based on neural networks

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