Delgado-Font Wilkie, Escobedo-Nicot Miriela, González-Hidalgo Manuel, Herold-Garcia Silena, Jaume-I-Capó Antoni, Mir Arnau
Departamento de Computación, Facultad de Ciencias Naturales y Exactas, Universidad de Oriente, Santiago de Cuba, Cuba.
Balearic Islands Health Research Institute (IdISBa), Soft Computing, Image Processing and Aggregation (SCOPIA) Research Group, Department of Mathematics and Computer Science, Universitat de les Illes Balears, Palma, Spain.
Med Biol Eng Comput. 2020 Jun;58(6):1265-1284. doi: 10.1007/s11517-019-02085-9. Epub 2020 Mar 28.
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.
红细胞（RBC）变形是多种疾病的结果，包括镰状细胞贫血，该病会导致反复的疼痛发作和严重的明显贫血。对患有这些疾病的患者进行监测需要在显微镜下观察外周血样本，这是一个耗时的过程。此外，执行这项技术需要一名专家，并且由于对单个红细胞观察的主观性，错误率很高。在本文中，我们提出了一种用于差异枚举红细胞的自动化方法，该方法使用外周血涂片图像分析。在这种方法中，使用Chan-Vese活动轮廓模型对图像中的感兴趣对象进行分割。然后进行分析，使用基本形状分析描述符：圆形形状因子（CSF）和椭圆形形状因子（ESF），将红细胞（也称为红血球）分类为正常、拉长或有其他变形。为了分析在样本制备过程中在簇中部分被遮挡的细胞，进行椭圆调整以允许分析具有盘状和拉长形状的红细胞。本研究中使用的患者血液样本图像由古巴圣地亚哥“胡安·布鲁诺·萨亚斯博士”综合医院特殊血液学部门的临床实验室专家采集。将我们实验中所提出方法获得的结果与一些最先进方法获得的结果进行比较表明，所提出的方法在镰状细胞贫血的诊断方面更具优势。通过获得的F值测量值（正常细胞为0.97，拉长细胞为0.95）和几个整体多类性能指标证明了这种优势。所提出方法取得的结果适用于镰状细胞贫血的临床治疗和诊断支持目的。我们提出了一种使用外周血涂片样本图像进行红细胞形状分类的新方法。该方法的目的是分割细胞、分离簇并对细胞（圆形、拉长形和其他形状）进行分类。我们将我们的方法与最先进的方法进行了比较。结果表明，我们的方法在镰状细胞贫血的诊断支持方面更具优势。
