Detección, Clasificación y Conteo de Leucocitos en Frotis de Sangre Periférica. J . Martínez-Castro* S. Reyes-Cadena** E. Felipe-Riverón *. * Centro de. Scribd is the world’s largest social reading and publishing site. FROTIS DE SANFRE PERIFERICA Un frotis de sangre preparado en forma adecuada es esencial para asegurar la evaluación correcta de la morfología.

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In recent times there have been efforts to develop computer systems that perform the leukocitary differential count automatically, looking for shorten the waiting times and help to give ssangre more accurate diagnosis as soon as possible and independent of the human factor.

For our objective, it is interesting to know what constitutes the blood, the unique fluidic tissue of our body. Chiappe G, Crisp R. The counting and classification of leukocytes is a re that in most cases until today is carried out manually by a clinical laboratory technician or hematopathologist who requires years of study and experience and leriferica uses a perifefica counter of leukocytes to perform this task. Leukocytes segmentation After segmenting the nuclei to isolate them from the erythrocytes, we proceed to separate them from the nuclear membrane.

From our test we obtained results that could be considered very near the perfect classification. Leukocytes are cells that represent the main mechanism of the body’s defense against infectious processes and inflammatory and allergic reactions. These solutions are built, in its majority, based on the introduction of mathematical methods, algorithms and software tools.

Frotis de sangre

An approach that actively selects efficient samples by simulating visual attention [7], uses a two-stage method bottom-up and top-down via learning by on-line sampling to automatically segment complex leukocyte images. The erythrocyte cell drotis, the leukocyte cell line and finally the megakaryocytic cell line producing platelets.


The erythrocytes are characterized by a natural pigmentation in reddish tone related to the amount of iron present in the cells. Reporte de un caso. Objects detected in images during the segmentation process were those with purple color; that is the reason why the color planes related in measurements were only the red and blue planes. Farreras Valenti P, Rozman C.

In [13] image processing algorithms are proposed to recognize five types of white blood cells in peripheral blood automatically. Erythrocytes are a-nucleated cells peeiferica belong to the series of red blood cells.

Anemia en el anciano y su tratamiento [Internet]. Images are initially normalized in size. Automatic recognition of five types of white blood cells in peripheral blood.

The sample contained 21 neutrophils, 9 eosinophils, 2 basophils, 7 monocytes and 21 Lymphocytes previously classified by the hematologist. Periferoca l Imag e Processing. The main objective of the system developed is to identify, count and classify the type of leukocytes present in a digital image of a peripheral blood sample obtained through a microscope smear. Madoff L, Kasper DL. Revista de Inmunoalergia [revista en Internet].

Nuclei segmentation The first step was to separate the white blood cells from the red blood cells. Leukocyte segmentation in tissue images using differential evolution algorithm.

Rev Hosp Ital B Aires.

Frotis de sangre – Wikipedia, la enciclopedia libre

The result of this operation is shown in Fig. Points above the diagonal represent good classification and points below represent poor classification.


Sagnre use of a differential evolution algorithm has been used also to segment the leukocytes from the images of mice skin tissue images stained with hematoxylin and eosin [12]. Discriminatory analysis, nonparametric discrimination: Note the difference in color of leukocytes respect to the erythrocytes color and background.

To test the effectiveness of the proposed method, we compared the results obtained by our system against those obtained by a ds. Pueden observarse eritrocitos nucleados.

Manzoni D, Sujobert P. It is important to emphasize that this work does not attempt to diferentiate between smears sangrr leukocytes coming from healthy and sick people; this is because most diseases produce a change in the differential count of leukocytes rather than in theirs forms.

A method based on multispectral imaging technique perifericaa white blood cell segmentation. Reporting and grading of abnormal red blood cell morphology. ABSTRACT Using the k -NN classifier in combination with the first Minkowski metric, in addition to techniques of digital image processing, we developed a computational system platform-independent, which is able to identify, to classify and to count five normal types of leukocytes: Blood cell image segmentation: The adaptive thresholding [13] is a method that enables us to select the optimal threshold based on the probability distribution of aangre levels represented in the histogram of the image.

Departamento de Medicina; Goldman L, Schafer AI.