One of the effective methods of early medical diagnosis is the method of wedge dehydration. It is based on the analysis of facies images. Facia is a thin film of dried human biological fluids. The presence of special structures (markers) indicates various pathologies of the organism at their earliest stages. In this article, the algorithm for detecting spherulite marker on microscopic images of human serum facies is presented. The presence of spherulites on facies is the norm. However, the atypical form of spherulite is a marker of precancerous diseases: uterine fibroids, endometrial hyperplastic processes and the mammary gland. Due to the visual analysis of the marker, its characteristic features were identified. Then algorithmic detection methods for these features were developed. The decision on the probable presence of a marker was made if there was a combination of features of this marker. As a result of the application of the developed algorithm, most images of atypical spherulites were identified.
Currently, early medical diagnostic methods are actively developing. Of particular interest are methods based on the analysis of human biological fluids (BF). One such method is the wedge dehydration method. It consists of applying a drop of BF on a specially prepared and degreased slide. This drop is dried at room temperature in a closed cabinet and the film is formed and called the facies (from the Lat. Faciens - appearance, shape). The scheme of interaction of the elements of BF is recorded on the facies. After dehydration, information from the liquid phase is represented as various structures on the film. These structures are viewable under a microscope.
D.K. Maxwell compiled the first theoretical description of the process of evaporation of a fixed
large droplet flowing in the diffusion mode. In 1877 he considered the case of evaporation of a
spherical drop in the article "Diffusion" [
In the present work, the algorithm for the detection of spherulite markers is presented. The presence of spherulites on facies is considered normal. However, the atypical form of spherulite is a marker of serious diseases. Among them are such as uterine fibroids, hyperplastic endometrial processes and the mammary gland, which are among the precancerous.
For the development of a detection algorithm the main features of the spherulite marker were highlighted. These features distinguish it from the basic structure of facies and other markers.
Spherulite is a complex rounded mineral aggregate. It consists of thin needle-like crystals and found in
nature in the form of balls of radial-radiant structure [
The algorithm was developed based on the features described above. It consists in the sequential application of the following operations to the original image.
• Image segmentation by brightness. Spherulites are darker than the main color of the facies.
The average brightness of the image is calculated and all areas are darker than they stand out. • Search the contours of areas with low brightness. • Search for closed contours. • Approximation by ellipse of the found closed contours.
• Decision-making.
This paragraph displays the results of the analysis using this algorithm using the example of the image of the facies shown in figure 2 on the left.
The color image is loaded into memory when you start the program. The entire image is converted to
grayscale because the marker colors do not carry important information (figure 3). A median filter is
carried out to get rid of random noise [
Sections of the image with brightnesses above the average are removed. The result of the threshold section is shown in figure 3a.
Outlining is implemented by the Canny algorithm [
The set of closed contours Cz is selected among the set C of the remaining contours. The definition of closed loop is implemented using pixel bypass. The criterion of closure is the return of the bypass to the starting point.
Approximating ellipse E is found for each contour from Cz. It approximates the set of its boundary points O = {P(i) = (x(i), y(i)), i = 1,..., N} . Consider a second-order curve equation
The atypical spherulite marker has large deviations of the boundaries from the approximating ellipse. In addition, the major and minor axes of the ellipse should be almost equal to the norm. Therefore, the evaluation form takes place in two stages.
• The axes of the approximating ellipse are compared. The value r=a/b is considered, where a and b are the major and minor semiaxes of the ellipse. When r ∈ [1; 1,3) spherulite is considered normal, with r ∈ [1,3; 3) - with pathology, and for r ∈ [3; ∞) - the contour is removed from consideration. • The maximum and minimum distance of the boundary points from the center of the ellipse is estimated. Spherulite is considered pathological. If one of the thresholds is exceeded, a message on the presence of a marker is displayed. The threshold is calculated adaptively based on the size of the approximating ellipse. The minimum distance is defined as 0.8 from the minor axis and the maximum as 1.2 from the main axis.
The algorithm was tested on 200 images of facies of blood serum with x400 optical magnification. As a result of the test, 86% of the atypical spherulites images were correctly identified. The algorithm made false detections and chose several structures similar to the desired marker in 11% of the images. This suggests that in the future it is advisable to expand the set of features of this marker for more accurate identification. It should be noted that there were other markers among the images from the list of false alarms. Search for other markers is also included in the tasks of the developed marker detection software complex.
In the course of this work, an algorithm and software for the automated detection of facies with suspicion of finding an atypical spherulite marker was created. Algorithm tests on real facies images showed acceptable results.