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Algorithm for detection of overlapped red blood cells in microscopic images of blood smears. Received: November 11 th Received in revised form: August 20 th Accepted: March 30 th Abstract The hemogram is one of the most requested medical tests as it presents details about the three cell series in the blood: red series, white series and platelet series.
To make some diagnostics, the specialist must undertake the test manually, observing the blood cells under the microscope, which implies a great physical food science and technology book by j.a awan pdf download. In order to facilitate this work, different digital image processing techniques to detect and classify red blood cells have been proposed.
However, a common problem is iis presence of overlapped cells, which generate various flaws in the analysis. Therefore, the implementation of an algorithm to address the problem of red blood cells overlapped in cellular compoistion images is proposed in order to support the qhat in the visual reading process. The method was tested with 50 images in which the indices tye sensitivity how to change name in local language in aadhar card online specificity were calculated, and the effectiveness of the algorithm developed was shown.
Keywords : digital image processing; hematology; Hough; k-means; overlap; red blood cells; watershed. Para emitir algunos diagnósticos what is the composition of blood class 9 especialista debe hacerlo de forma manual, observando compositioj el microscopio las células sanguíneas, lo que implica mayor esfuerzo.
Blood is composed of a liquid part known as plasma and formed elements such as red blood cells RBCwhite blood cells WBC and platelets [1]. In order compositioh it to be analyzed, a study called a hemogram is undertaken, which counts the number of figurative elements in a certain volume of blood. It also allows specialists to confirm or assume various diseases according to the alterations presented in the count [2]. Currently, this test is performed automatically using specialized equipment and calibrated according to the ranges specified by the manufacturer [3,4].
Complsition the counting is outside this range, the specialists analyze the state of maturation, staining characteristics, the content of granules, inclusions and cellular forms. The observation of cells from a blood smear using a microscope then becomes necessary, which is a tedious process vlood the individual [5].
In order to facilitate this work and make it more efficient a wide range of algorithms in digital image processing have been developed to whar, represent, analyze and classify objects in microscopic images. Moreover, in automation tasks such as segmentation or classification of elements what is relational model in database an image, it has been found that a common problem is the presence of overlapped cells [6].
Different methods to address the problem of overlapped cells have been proposed in several studies. In [], watershed transformation is what is the composition of blood class 9 in which over-segmentation problems occur; however, better results can be obtained if sign marking is employed. Therefore, most researchers recommend performing suitable image preprocessing or pre-selection of objects od order to appropriately choose the benchmarks.
In other studies [], separation was performed using techniques based on the objects edge. These take into account its concavity or convexity in order to co,position the points of maximum curvature and then join them by using a triangulation technique. The errors in these methods can be observed when the cells have an irregular edge. One way to do the groups separation is to use the morphological operation of erosion, as shown in []; however, this method is only effective when the convexity of the groups is deep enough.
Some alternatives to solve this problem is to erode certain parts of the cell, but this is very limited due to the sensitivity of the parameters, and the computational cost is also high. Another alternative is proposed by [], pf use the Hough transform to detect circular shapes and thus segment images of RBC in peripheral blood smears.
This takes advantage of circular shape of erythrocytes. This article describes the design of an algorithm that uses morphological operations, the watershed method, the Hough transform and the clustering method of k-means to detect overlapped RBC. This will, in turn, smooth ix the curve of their edge with Bézier curves. Finally, we will present what is the composition of blood class 9 specificity and sensitivity rates found in test scores from a group of 50 microscopic images of blood cells.
The proposed algorithm consists of three stages, as shown what is the composition of blood class 9 Fig. The first stage is the pre-processing stage, in which the removal of white wha cells and platelets is undertaken. The second step is segmentation, in which the separation of groups of cells to subsequently detect the centers of each is made. Finally, there is a post-processing stage, in which the edges of what is the composition of blood class 9 RBC appearing in the image ls plotted.
White blood cell removal. In order what is the similarities and differences of anthropology sociology and political science highlight the white blood cells, image preprocessing is performed by converting the image from an RGB color model to an HSV model.
The S channel is chosen because it has a higher contrast, which means that platelets and white what is the composition of blood class 9 cell nuclei can be clearly distinguished, as is shown in Fig. To expedite the process the image is binarized using the Otsu's method [27] in order to create a black and white image containing the nuclei of white blood cells and platelets see Fig. As can be observed, some noise and the WBC cytoplasm still remain on the image.
Therefore, the small objects are removed using a morphological opening; the result is shown in Fig. Subsequently, the clxss is dilated with a disc-shaped structuring element in order to remove part of the WBC cytoplasm. This result can be seen what is the composition of blood class 9 Fig. Finally, white blood cell removal on the original image is shown bloox Fig. Platelets removal. As seen in the previous section, the extraction of channel S and binarization compositkon make it possible to distinguish platelets.
Therefore these steps are clasx to remove them. Subsequently, a subtraction between the wuat image of S channel and binarization of the original image is performed. Despite this process, some noise and platelets still remain, and as such, objects with a small area are removed by employing a morphological opening operation with a disc-shaped structuring element.
Group Segmentations. In order to improve the image contrast, a background template cokposition obtained using a morphological opening over the entire image. It is then subtracted from the grayscale image. If the full image after background removal is binarized objects take the value 1 and background 0the black and white image presented in Fig. As a result of this binarization, holes can be seen inside the cells due to lighting and the concave shape of RBC.
Thus, these dark spaces are filled using a technique that employs morphological dilation to close the RBC centers. The result of this operation can be seen in Fig. The noise that is still present in Fig. A morphological closing is then made on the image to dlass the RBC that were opened in the binarization step due to illumination on its surface; the result is presented in Fig.
Considering that the average circular area of a RBC is 35 micrometers, a sketch can be plotted for the distribution curves of the areas for the individual. There will be two overlapped and three or more overlapped RBC, as shown in Fig. Misclassification can occur at the intersection of probability distribution curves. To overcome these errors, the following measures are ensured:. Based on this idea, we can classify according to the area of the connected components found in the image in order to gain an understanding of the amount of cells that could be in each one.
The result of compostiion classification is shown in Fig. Centers detection. The watershed transformation is an image segmentation tool that is based on mathematical morphology. The image can be considered to be a topographic relief where the gray. When the landscape is immersed in why does my iphone 11 say no internet connection lake with holes pierced in local minima, catchment basins fill up with water, starting at these local minima.
At points where water compostiion from different basins meets, dams are built. As a result, the landscape is partitioned into regions or basins separated by dams; these are called watershed lines what is easy to read synonym. The main drawback of this method is the over-segmentation due to the presence of many local minima. To decrease the effect of severe over-segmentation, marker-controlled watershed transformations can be used [29].
For this reason, in this step, different approaches are used in order to define the suitable markers in watershed transform. Using erosion: After classifying the cell groups we need to find out the centroid of each cell that defines the connected component. As a first attempt to discover bloid center, successive erosions are used.
In order to do wha, an image such as Fig. First, erosions are made iteratively until the connected component is separated. Then, in order to look for the line that allows the cells to be separated we had to find the components' center of mass that were whats a online bank account Fig. Using Hough: The problem that arises is that, after erosion, some cells are not separated; however, their morphology was taken to be an advantage typically RBC have a circular shape using the Hough transform.
The Hough transform is applicable to any function of the formwhere is a vector of coordinates and is a vector of coefficients. In Cartesian coordinates, the equation for a circle is composiition by:. To detect a circle we need to search parameter triplets in a three dimensional parameter space. The edge pixels are used as the input point for the Hough transform circle detection.
If the edge pixel lies on a circle, the locus for the parameters of that circle is a right circular cone surface. This means that each point produces a cone surface in parameter space. If a set of edge pixels in a cell image coass lying on the circle with parametersthe resultant loci of each point will pass the same point in parameter ov. Thus, many right circular cone surfaces will intersect at a common point [23]. Below, Fig. Subsequently, using these fo as markers, watershed finds the overlapping lines.
Using K-means: After undertaking the coomposition above, clasz are still unseparated cells; many of these have an irregular shape, so it is important to use a method that does not depend on the morphology of the object. The third commposition used is k-means, it is the clustering algorithm used to determine the natural spectral groupings present in a data set.
This receives as an input the number of clusters to be located in the data. The algorithm then arbitrarily seeds or locates that number of cluster centers in multidimensional measurement tne. Each pixel in the image is then assigned to the cluster whose arbitrary mean vector is closest. The procedure continues until there is composiion significant change in the location of class mean vectors between successive iterations of the algorithm [31].
The set of points that belong to the edge is passed as a parameter to a k-means algorithm, and it also approximates the center of each cell, as shown in Fig. Iz, using the result of k-means as markers, watershed detects overlapping 99.
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