Vera Yashina

Automating extraction and analysis of dopaminergic axon terminals in images of frontal slices of the striatum

We develop a method to automate the obtaining of experimental data necessary to supplement the model of the preclinical stage of Parkinson’s disease. As a source of experimental data for constructing the model, images of stained slices of the brains of experimental animals are used. The suggested method is based on mathematical morphology operations and intended for automatic extraction of dopaminergic neuron terminals in images of frontal slices of the striatum. Experimental studies have confirmed the possibility and expediency of automating the processing and analysis of images of slices using this method.

Descriptive approach to image analysis: Image formalization space

The paper presents and discusses the main results obtained using the descriptive approach to analyzing and understanding images when solving fundamental problems of the formalization and systematization of the methods and forms of representing information in the problems of the analysis, recognition, and understanding of images, in particular that arise in connection with the automation of information extraction from images in order to make intelligent decisions (diagnosis, prediction, detection, evaluation, and identification of patterns). In this direction, so far, the following results have been obtained: (1) the conceptualization of a system of concepts that describe the initial information (images) in recognition problems has been carried out; (2) descriptive models of images focused on the recognition problem have been defined; (3) the image-formalization space has been introduced, the elements of which include different forms (states, phases) of representing the image transformed from the original form into the recognizable one, i.e., into the image model; (4) the basic axioms of the descriptive approach were introduced. Axiomatics and its formal structures provide the methods and tools of representation and the description of images for their subsequent analysis and evaluation.

Descriptive approach to medical image mining. An algorithmic scheme for analysis of cytological specimens

The present paper is devoted the development and formal representation of a descriptive model for an information technology to automate the morphological analysis of cytologic preparations (a tumor of the lymphatic system). The theoretical basis of the model is a descriptive approach to image analysis and understanding and its main mathematical tools. Practical application of the algebraic tools of the descriptive approach is demonstrated, and the algorithmic scheme of the technology is described in the language of descriptive image algebras.

A new method for automated detection and identification of neurons in microscopic images of brain slices

A new combined mathematical method is proposed, implemented, and experimentally tested for extracting information necessary for modeling and, in future, predicting Parkinson’s disease (PD) from microscopic images of brain slices of experimental animals. The method allows one to detect and identify as neurons a set of small informative extended objects with well distinguished (by brightness) oval inclusions. The result is a binary image of the contours of detected objects and their inclusions and a list of characteristics calculated for each detected object. The method is based on the joint application of image processing methods, methods of mathematical morphology, methods of segmentation, and the methods of classification of microscopic images.

Image formalization space: Formulation of tasks, structural properties, and elements

The work is devoted to developing the main results in solving the fundamental problem of formalization and systematization of methods and forms of information representation in image analysis, recognition, and understanding tasks. This problem arises in connection with development of the descriptive approach to image analysis and understanding. The main result is the concept of image spatial formalization, including a set of image states and a set of states of image transformation schemes. We consider the construction of algorithmic schemes generating phase trajectories for solving problems of image analysis and recognition. We present axioms defining the properties and structure of the image formalization space. The introduced system of concepts constitute the basis for standardizing methods for synthesizing image models oriented to image analysis and understanding tasks.

Systematization and feature selection for formalization of descriptions of the methodological structure of cytological and histological preparations and analytical review

The main aim of this work is development of a method of automated selection of informative features to solve the tasks of medical diagnostics by cytological and histological images. The main result is the structure of the proposed method. Analytical review of features applied for description of cytological and histological images is carried out in this work, brief characteristics of methods applied for synthesis of feature descriptions is provided, and the possibility to use data of the method for automation of diagnostic feature selection is discussed.

Conditions of Generating Descriptive Image Algebras by a Set of Image Processing Operations

It is outlined new results of investigations into development of mathematical tools for analysis and estimation of information represented by images. It continues research of a new class of image algebras (IA) – the Descriptive Image Algebras (DIA). Practical implementation of DIA in image analysis applications requires a study of a set of operations, leading or not leading to DIA construction, having or not having physical interpretation. Operations of the ring in these algebras are both standard algebraic operations and special operations of image processing and transformation. The problem of operations that can be used for construction of DIA and of how this possibility is connected with physical interpretation of corresponding algebra operations is still open. This problem is reduced to formulation of the conditions that should be satisfied by a set of operations for construction of the DIA. The first stage of its solution is the construction of the examples of the sets of operations (having physical meaning), leading or not leading to DIA construction. The basic results of the report are both the method of testing the specified conditions and the examples of sets with various elements and the operations introduced on them (both generating algebras and not).

A New Image-Mining Technique for Automation of Parkinson’s Disease Research

This work aimes at the development of mathematical tools and information technology elements for automated extraction and characterization of objects in striatum section images. The latter are used to construct a Parkinson’s disease model at a preclinical stage. Experimental applications of the developed technique have confirmed its high efficiency and suitability for automated processing and analysis of brain section images (a 200 times increase in productivity and a 10 times decrease in the amount of animals and expendables).

Extraction of neurons from images of mouse brain slices based on automated selection of connected morphological filters

We describe the results of a study on creating an algorithm for automated selection of connected morphological filters to solve image segmentation problems. We propose a similarity measure for image partition. It is used to select the best filters from given families of connected morphological filters in such a way that partition obtained by applying the watershed-segmentation algorithm to a filtered image has the maximum similarity value with the given partition. This method is used to extract neurons from images of mouse brain slices. Experimental research has confirmed that this method is applicable for automated processing and analysis of slice images.

Descriptive image analysis: Genesis and current trends

This paper is devolved to descriptive image analysis, an important, if not a leading, direction in the modern mathematical theory of image analysis. Descriptive image analysis is a logically organized set of descriptive methods and models meant for analyzing and estimating the information represented in the form of images, as well as for automating the extraction (from images) of knowledge and data needed for intelligent decision making about the real-world scenes reflected and represented by images under analysis. The basic idea of descriptive image analysis consists in reducing all processes of analysis (processing, recognition, and understanding) of images to (1) construction of models (representations and formalized descriptions) of images; (2) definition of transformations over image models; (3) construction of models (representations and formalized descriptions) of transformations over models and representations of images; and (4) construction of models (representations and formalized descriptions) of schemes of transformations over models and representations of images that provide the solution to image analysis problems. The main fundamental sources that predetermined the origination and development of descriptive image analysis, or had a significant influence thereon, are considered. In addition, a brief description of the current state of descriptive image analysis that reflects the main results of the descriptive approach to analysis and understanding of images is presented. The opportunities and limitations of algebraic approaches to image analysis are discussed. During recent years, it was accepted that algebraic techniques, particularly, different kinds of image algebras, are the most promising direction of construction of the mathematical theory of image analysis and of the development of a universal algebraic language for representing image analysis transforms, as well as image representations and models. The main goal of the algebraic approaches is designing a unified scheme for representation of objects under recognition and its transforms in the form of certain algebraic structures. This makes it possible to develop the corresponding regular structures ready for analysis by algebraic, geometrical, and topological techniques. The development of this line of image analysis and pattern recognition is of crucial importance for automatic image mining and application problems solving, in particular, for diversification of the classes and types of solvable problems, as well as for significant improvement of the efficiency and quality of solutions. The main subgoals of the paper are (1) to set forth the-state-of-the-art of the mathematical theory of image analysis; (2) to consider the algebraic approaches and techniques suitable for image analysis; and (3) to present a methodology, as well as mathematical and computational techniques, for automation of image mining on the basis of the descriptive approach to image analysis (DAIA). The main trends and problems in the promising basic researches focused on the development of a descriptive theory of image analysis are described.