zbMATH — the first resource for mathematics

Simultaneous segmentation of leukocyte and erythrocyte in microscopic images using a marker-controlled watershed algorithm. (English) Zbl 1411.92165
Summary: The density or quantity of leukocytes and erythrocytes in a unit volume of blood, which can be automatically measured through a computer-based microscopic image analysis system, is frequently considered an indicator of diseases. The segmentation of blood cells, as a basis of quantitative statistics, plays an important role in the system. However, many conventional methods must firstly distinguish blood cells into two types (i.e., leukocyte and erythrocyte) and segment them in independent procedures. In this paper, we present a marker-controlled watershed algorithm for simultaneously extracting the two types of blood cells to simplify operations and reduce computing time. The method consists of two steps, that is, cell nucleus segmentation and blood cell segmentation. An image enhancement technique is used to obtain the leukocyte marker. Two marker-controlled watershed algorithms are based on distance transformation and edge gradient information to acquire blood cell contour. The segmented leukocytes and erythrocytes are obtained simultaneously by classification. Experimental results demonstrate that the proposed method is fast, robust, and efficient.

92C55 Biomedical imaging and signal processing
Full Text: DOI
[1] Fenderson, B. A., Molecular biology of the cell, 5th edition, Shock, 30, 1, 100, (2008)
[2] Piuri, V.; Scotti, F., Morphological classification of blood leucocytes by microscope images, Proceedings of the IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA ’04)
[3] Zhang, C.; Xiao, X.; Li, X.; Chen, Y.-J.; Zhen, W.; Chang, J.; Zheng, C.; Liu, Z., White blood cell segmentation by color-space-based k-means clustering, Sensors, 14, 9, 16128-16147, (2014)
[4] Huang, D. C.; Hung, K. D.; Chan, Y. K., A computer assisted method for leukocyte nucleus segmentation and recognition in blood smear images, Journal of Systems & Software, 85, 9, 2104-2118, (2012)
[5] Cuevas, E.; Oliva, D.; Díaz, M.; Zaldivar, D.; Pérez-Cisneros, M.; Pajares, G., White blood cell segmentation by circle detection using electromagnetism-like optimization, Computational and Mathematical Methods in Medicine, 2013, (2013)
[6] Sadeghian, F.; Seman, Z.; Ramli, A. R.; Abdul Kahar, B. H.; Saripan, M.-I., A framework for white blood cell segmentation in microscopic blood images using digital image processing, Biological Procedures Online, 11, 1, 196-206, (2009)
[7] Ma, R.; Liang, Y.; Ma, Y., A self-adapting method for RBC count from different blood smears based on PCNN and image quality, Proceedings of the 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
[8] Rashid, N. Z. N.; Mashor, M. Y.; Hassan, R., Unsupervised color image segmentation of red blood cell for thalassemia disease, Proceedings of the 2nd International Conference on Biomedical Engineering, ICoBE 2015
[9] Wei, X.; Cao, Y., Automatic counting method for complex overlapping erythrocytes based on seed prediction in microscopic imaging, Journal of Innovative Optical Health Sciences, 9, 5, (2016)
[10] Gonzalez, R. C.; Wintz, P., Digital Image Processing, (2010), Publishing House of Electronics Industry
[11] Haris, K.; Efstratiadis, S. N.; Maglaveras, N.; Katsaggelos, A. K., Hybrid image segmentation using watersheds and fast region merging, IEEE Transactions on Image Processing, 7, 12, 1684-1699, (1998)
[12] Borgefors, G., Distance transformations in digital images, Computer Vision Graphics and Image Processing, 34, 3, 344-371, (1986)
[13] Soille, P., Morphological Image Analysis: Principles and Applications, (2003), Berlin, Germany: Springer, Berlin, Germany · Zbl 1012.68212
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.