Method for erythrocyte count on blood smear photos (versions)

FIELD: medicine.

SUBSTANCE: for the purpose of erythrocyte count on blood smear photos, black-and-white photos of blood cells are formed. A Canny edge detector is used to contour the cells. Circles of pre-set diameters are found in the contours by Hough method. Their centres are localised. When forming the photos, the centres of the found circles are distributed. The formed photos are morphologically treated with a close operator, and compact cell count respective to a target erythrocyte count is found.

EFFECT: precision in erythrocyte count on the blood smear photos.

2 cl, 4 dwg

 

The invention relates to the field of pattern recognition and can be used to calculate the round objects in the image, given the average radius and can be used to count red blood cells in images of blood smears.

The current level of technology there is a method of counting cells, including threshold image segmentation, the selection of objects segmented black-and-white image, removing objects that do not meet the required parameters, the detector boundaries Canny to segmented image, finding the boundaries of cells and their segmentation, calculation of segmented cells. Patent GB No. 2478593 And IPC G06K 9/00, G06T 7/00, publ. 14.09.2011.

Also there is a method of counting cells, including binarization components "and" image in the color space CIE Lab, the edge detection of cell nuclei using the model of active contour segmentation of cell nuclei counting cells. D. Murashov. A method of automated segmentation of cytological preparations of model-based active contour. // Proceedings of the 50th scientific conference of MIPT "Modern problems of fundamental and applied Sciences". Part VII. Management and applied mathematics. Volume 1. M: MIPT. 2007. P.88-93.

Closest to the claimed technical solution and selected as the prototype is with the persons counting of red blood cells in images of blood smears, including the formation of black and white blood cells, selection using a detector boundaries Canny contours of cells being in the selected paths method have circles of a given radius and the count of the selected circles, whose number is the number of erythrocytes in the image of a blood smear. A.R.J. Katz Image Analysis and Supervised Learning in the Automated Differentiations of White Blood Cells from Microscopic Images // Royal Melbourne Institute of Technology. 2000.

The task, which directed the claimed invention, is to improve the accuracy of counting of red blood cells in images of blood smears due to the fact that not counted incorrectly found the circumference and separated red blood cells, superimposed on each other.

This task is solved by forming a black and white blood cells, isolated using the detector boundaries Canny contours cells, are selected paths method hafa circle of a given diameter, determine the coordinates of their centers. Then form the image distribution centers found circles, morphologically processed image by the operator closing and count the number of cohesive elements, which corresponds to the desired number of red blood cells.

Also this problem is solved by forming a black and white blood cells, isolated with the aid of the completion detector boundaries Canny contours of the cells, found in selected paths method hafa circle of a given diameter, determine the coordinates of their centers. Before outlining cells implement threshold segmentation obtained black-and-white image of blood cells in brightness on the "background" and "background"to create an image of the distribution of the mentioned centers of the detected circles, morphologically processed image by the operator closing, remove the connected elements, the coordinates of which correspond to the background, and count the number of remaining communication elements, which corresponds to the desired number of red blood cells.

The inventive combination of the above proposed action improves the accuracy of the counting of red blood cells.

The essence of the proposed invention is illustrated in the following.

After the formation of black-and-white image of blood cells by shooting blood smear digital microscope, you Canny algorithm to select paths in the resulting image. In this algorithm, first the image smoothing filter to remove noise. The next step Canny method is the computation of the gradient of each point of the image. Kernel filter for calculating a gradient image using the Sobel operator are arrays of size 3 3:

MGy=( 10-120-210-1)MGx=(121000-1-2-1).

Boundaries are highlighted where the gradient of the image becomes the maximum value, with only local maxima are marked as boundaries. The next stage of the algorithm edge-Canny is to define the potential boundaries of the dual-threshold filtering. The final boundaries are defined by suppressing all edges that are not associated with defined boundaries.

As erythrocytes, most are rounded, their calculation can be carried out using the methods of allocation of circles in the image. One such method is the Hough transform, which allows you to browse for the image objects belonging to a certain class of shapes. A circle can be defined by parametric equations:

x=a+Rcos(θ);

y=b+Rsin(θ),

where (x, y) - coordinates of a point is to a circle, (a, b) - coordinates of the center of the circle, R is the radius of the circle.

As is known, the coordinates of the center of the circle (a, b) and radius R are parameters, with which you can describe any circle in the plane. The Hough transform uses a special array called battery, the dimension of which is equal to the number of unknown parameters of the object. The battery is calculated in such a way that if a sufficient number of contour points of the image satisfied parametric equations, then the contents of the corresponding cell of the battery is increased by one. Thus, the maximum battery will fit the parameters of the desired circle at the given point.

Large dimensionality of the battery increases the complexity of the algorithm, and therefore the radius of the search circle is determined in advance equal to the average size of erythrocytes.

The radius of the cells may vary within certain values. To calculate these values on the image contours are selected most successful spin-off circuit, representing an object, a closed surface which has the smallest eccentricity and a fairly large radius.

The range of values of the radii of the selected paths is used as the parameter R in the Hough transform.

The coordinates of the assumed goals the centers of the circles form a cloud of points in the image, with the greatest density in the district centers of the desired cells, it is possible to accurately determine the location of erythrocytes in the most dense and large gatherings alleged centers.

Some tight formation points are not located in the district centers of the cells and the background. This is due to imperfect selection of borders and the formation of false contours, having the shape of a circle. To address the points belonging to the background, use the following method. Given the fact that the background images of the blood smear on its intensity is always higher intensity of red blood cells, form the mask of the background, which is the image of the threshold segmentation of the original halftone image brightness. After building the masks are removed the points from the cloud centers of the arcs that belong to the background. To combine dense formations of points used morphological close operation, resulting in the centers of cells in the image distribution centers of the circles are formed connected elements. Too small area connected elements, which represent the centers of falsely selected circles are removed and the remaining number of cohesive elements is the number of cells in the image.

The essence of the proposed methods is illustrated by figures 1-4, where:

On Figo is e 1 - the image of a blood smear;

Figure 2 - image paths and image distribution centers found circles superimposed on the image of a blood smear;

On the figure 3 - the mask image "background" and "background";

Figure 4 - image of the morphological image processing distribution centers of the circles superimposed on the image of a blood smear.

The method is as follows.

Using a digital microscope MSP-2 is the formation of black and white blood cells. The resulting image is transmitted to the processing in the personal computer. As a result of processing count the number of cohesive elements, which corresponds to the desired number of cells in the image.

Thus, the inventive methods allow precise counting of red blood cells in images of blood smears.

1. The way of counting red blood cells in images of blood smears, including the formation of black and white blood cells, selection using a detector boundaries Canny contours of cells being in the selected paths method have circles of a given diameter, the coordinates of their centers, characterized in that form the image distribution centers found circles, morphologically processed image operas the torus is closed and count the number of cohesive elements, which corresponds to the desired number of red blood cells.

2. The way of counting red blood cells in images of blood smears, including the formation of black and white blood cells, selection using a detector boundaries Canny contours of cells being in the selected paths method have circles of a given diameter, the coordinates of their centers, wherein before allocating the cell contours implement threshold segmentation obtained black-and-white image of blood cells in brightness on the "background" and "background"to create an image of the distribution of the mentioned centers of the detected circles, morphologically processed image by the operator closing, remove the connected elements, the coordinates of which correspond to the background, and count the number of remaining communication elements that corresponds to the desired number of red blood cells.



 

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