RESUMO

Red blood cell (RBC) deformation is the consequence of several diseases, including sickle cell anemia, which causes recurring episodes of pain and severe pronounced anemia. Monitoring patients with these diseases involves the observation of peripheral blood samples under a microscope, a time-consuming procedure. Moreover, a specialist is required to perform this technique, and owing to the subjective nature of the observation of isolated RBCs, the error rate is high. In this paper, we propose an automated method for differentially enumerating RBCs that uses peripheral blood smear image analysis. In this method, the objects of interest in the image are segmented using a Chan-Vese active contour model. An analysis is then performed to classify the RBCs, also called erythrocytes, as normal or elongated or having other deformations, using the basic shape analysis descriptors: circular shape factor (CSF) and elliptical shape factor (ESF). To analyze cells that become partially occluded in a cluster during sample preparation, an elliptical adjustment is performed to allow the analysis of erythrocytes with discoidal and elongated shapes. The images of patient blood samples used in the study were acquired by a clinical laboratory specialist in the Special Hematology Department of the "Dr. Juan Bruno Zayas" General Hospital in Santiago de Cuba. A comparison of the results obtained by the proposed method in our experiments with those obtained by some state-of-the-art methods showed that the proposed method is superior for the diagnosis of sickle cell anemia. This superiority is achieved for evidenced by the obtained F-measure value (0.97 for normal cells and 0.95 for elongated ones) and several overall multiclass performance measures. The results achieved by the proposed method are suitable for the purpose of clinical treatment and diagnostic support of sickle cell anemia. We present a new method to obtain erythrocyte shape classification using peripheral blood smear sample images. The aim of the method is to segment the cells, to separate clusters and classify cells (circulars, elongated and others). We compared our method with state-of the-art. Results showed that our method with is superior for the diagnosis support of sickle cell anemia.

Assuntos

RESUMO

El procesamiento de imágenes digitales y la visión por computador son ampliamente utilizados en medicina actualmente y son de gran interés las propuestas de nuevos métodos de análisis automatizado de imágenes digitales o mejorar la eficiencia de los existentes. En este trabajo se desarrollaron métodos nuevos para estudiar computacionalmente a través de imágenes de muestras de sangre la drepanocitosis, dolencia con alta incidencia mundial y en Cuba, sobre todo en la región oriental. Se propusieron nuevos métodos de análisis de formas, obtenidos a partir de resultados clásicos de geometría integral y nuevas propuestas de visión por computador para evaluar trastornos neurofisiológicos asociados a través del estudio de las expresiones faciales del paciente. La validación estadística realizada comprobó la superioridad de estos métodos sobre otros, se determinó que son válidos para ser introducidos en software de apoyo para mejorar la calidad de la atención médica(AU)

Digital image processing and computer vision are frequently used in medicine at present and the proposals of new methods of automatic analysis of digital images or the efficiency improvement of the existing are of great interest. In this work new methods to computationally study sickle cell disease through blood samples images are developed, an illness with high incidence in the world and in Cuba, mainly in the eastern region. New shape analysis methods obtained from classical results of integral geometry and new computer vision proposals for evaluate neuro physiological disorders associated with this illness through the study of the facial expressions of the patient were proposed. The statistical validation realized confirmed the superiority of these methods on previous proposals, which is why they are valid to be introduced in support software to improve the quality of the medical attention(AU)

Assuntos

RESUMO

Se realiza un estudio del desempeño de los modelos ocultos de Márkov (HMM) en la clasificación morfológica supervisada de eritrocitos en muestras de sangre periférica de pacientes con anemia drepanocítica. Los contornos se representan de forma novedosa considerando las diferencias angulares en la curvatura de los puntos del mismo. El entrenamiento de cada modelo se realiza tanto con la descripción normal de los contornos como con la representación de la rotación de los mismos, para garantizar una mayor estabilidad en los parámetros estimados. Se desarrolla un proceso de validación cruzada de 5x1 para estimación del error. Se obtienen las medidas de sensibilidad, precisión y especificidad de la clasificación. Los mejores resultados en cuanto a sensibilidad se obtienen al clasificar eritrocitos pertenecientes a dos clases: normales (96 por ciento) y elongados (99 por ciento). Al considerar además una clase de eritrocitos con otras deformaciones los mejores resultados se obtienen realizando el entrenamiento de los modelos con la rotación de todos los contornos, que alcanzó sensibilidades de normales (94 por ciento), elongados (82 por ciento) y con otras deformaciones (76 por ciento)(AU)

A study of the performance of Hidden Markov Models (HMM) in morphologic supervised classification of erythrocytes in peripheral blood smears of patients with sickle cell disease is realized. Contours are represented in original way considering the angular differences in the curvature of the points of the same. The training of every model comes true with the normal description of the contours and with the representation of the rotation of the same, in order to guarantee a bigger stability in the esteemed parameters. A process of validation crossed of 5x1 for estimate of the error is developed. The measures of sensibility, precision and specificity of classification are obtained. The best results obtain when classifying erythrocytes in two classes, with sensibility values in normal of 96 percent and elongated 99 percent. In the classification of erythrocytes considering the class of other deformations better results obtain accomplishing the training of the models with the rotation of all the contours, that it attained sensibilities of normal (94 percent), elongated (82 percent) and with other deformations (76 percent)(AU)

Assuntos