Red Blood Cell Cluster Separation From Digital Images for Use in Sickle Cell Disease

• Published in: IEEE journal of biomedical and health informatics Vol. 19; no. 4; pp. 1514 - 1525
• Main Authors: Gonzalez-Hidalgo, Manuel, Guerrero-Pena, F. A., Herold-Garcia, S., Jaume-i-Capo, Antoni, Marrero-Fernandez, P. D.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adjustment; Algorithms; Anemia, Sickle Cell - diagnosis; Anemia, Sickle Cell - pathology; Blood; Clustering algorithms; Clusters; Digital images; Diseases; Ellipses; Elongation; Erythrocytes; Erythrocytes - cytology; Erythrocytes - pathology; Humans; Image Interpretation, Computer-Assisted; Image Processing, Computer-Assisted - methods; Image segmentation; Microscopy; Morphology; Red blood cells; Reproducibility of Results; Shape; Sickle cell anemia; Sickle cell disease; cell cluster separation; overlapped cell splitting; ellipse fitting; concave point detection; Blood cell morphology counting
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2014.2356402

The study of cell morphology is an important aspect of the diagnosis of some diseases, such as sickle cell disease, because red blood cell deformation is caused by these diseases. Due to the elongated shape of the erythrocyte, ellipse adjustment and concave point detection are applied widely to images of peripheral blood samples, including during the detection of cells that are partially occluded in the clusters generated by the sample preparation process. In the present study, we propose a method for the analysis of the shape of erythrocytes in peripheral blood smear samples of sickle cell disease, which uses ellipse adjustments and a new algorithm for detecting notable points. Furthermore, we apply a set of constraints that allow the elimination of significant image preprocessing steps proposed in previous studies. We used three types of images to validate our method: artificial images, which were automatically generated in a random manner using a computer code; real images from peripheral blood smear sample images that contained normal and elongated erythrocytes; and synthetic images generated from real isolated cells. Using the proposed method, the efficiency of detecting the two types of objects in the three image types exceeded 99.00%, 98.00%, and 99.35%, respectively. These efficiency levels were superior to the results obtained with previously proposed methods using the same database, which is available at http://erythrocytesidb.uib.es/. This method can be extended to clusters of several cells and it requires no user inputs.