Abstract
Erythrocytes (RBC) are the most common type of blood cell. These cells are responsible for the delivery of oxygen to body tissues. The abnormality in erythrocyte cell affects the physical properties of red cell. It may also decrease the life span of red blood cells which may lead to stroke, anemia and other fatal diseases. Until now, Manual techniques are in practiced for diagnosis of blood cell’s diseases. However, this traditional method is tedious, time consuming and subject to sampling error. The accuracy of manual method depends on the expertise of the expert, while the accuracy of automated analyzer depends on the segmentation of objects in microscopic image of blood cell. Despite numerous efforts made for accurate blood cells image segmentation and cell counting in the literature. Still accurate segmentation is difficult due to the complexity of overlapping objects and shapes in microscopic images of blood cells. In this paper we have proposed a novel method for the segmentation of blood cells. We have used wiener filter along with Curvelet transform for image enhancement and noise removal. The snake algorithm and Gram-Schmidt orthogonalization have applied for boundary detection and image segmentation, respectively.
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Shirazi, S.H., Umar, A.I., Haq, N.U., Naz, S., Razzak, M.I. (2015). Accurate Microscopic Red Blood Cell Image Enhancement and Segmentation. In: Ortuño, F., Rojas, I. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2015. Lecture Notes in Computer Science(), vol 9043. Springer, Cham. https://doi.org/10.1007/978-3-319-16483-0_18
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DOI: https://doi.org/10.1007/978-3-319-16483-0_18
Publisher Name: Springer, Cham
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