Erythrocyte shape classification using integral-geometry-based methods

• Published in: Medical & biological engineering & computing 2015-07, Vol.53 (7), p.623-633
• Main Authors: Gual-Arnau, X., Herold-García, S., Simó, A.
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Anemia, Sickle Cell - pathology; Bioengineering; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Blood; Blood vessels; Cell Shape; Classification; Computer Applications; Erythrocytes; Erythrocytes - classification; Erythrocytes - cytology; Erythrocytes - pathology; Fourier transforms; Geometry; Hemoglobin; Human Physiology; Humans; Illnesses; Image Processing, Computer-Assisted - methods; Imaging; Malaria; Medical diagnosis; Methods; Microscopy; Morphology; Neural networks; Original Article; Parasites; Polymerization; Radiology; Sickle cell disease; Studies
• ISSN: 0140-0118; 1741-0444
• DOI: 10.1007/s11517-015-1267-x

Erythrocyte shape deformations are related to different important illnesses. In this paper, we focus on one of the most important: the Sickle cell disease. This disease causes the hardening or polymerization of the hemoglobin that contains the erythrocytes. The study of this process using digital images of peripheral blood smears can offer useful results in the clinical diagnosis of these illnesses. In particular, it would be very valuable to find a rapid and reproducible automatic classification method to quantify the number of deformed cells and so gauge the severity of the illness. In this paper, we show the good results obtained in the automatic classification of erythrocytes in normal cells, sickle cells, and cells with other deformations, when we use a set of functions based on integral-geometry methods, an active contour-based segmentation method, and a k-NN classification algorithm. Blood specimens were obtained from patients with Sickle cell disease. Seventeen peripheral blood smears were obtained for the study, and 45 images of different fields were obtained. A specialist selected the cells to use, determining those cells which were normal, elongated, and with other deformations present in the images. A process of automatic classification, with cross-validation of errors with the proposed descriptors and with other two functions used in previous studies, was realized.