Segmentation of White Blood Cells from Microscopic Images Using a Novel Combination of k-means Clustering and Modified Watershed Algorithm

Narjes Ghane, Alireza Vard, Ardeshir Talebi, Pardis Nematollahy

DOI:

Abstract


Recognition of white blood cells (WBCs) is the first step to diagnose some particular diseases such as acquired immune deficiency syndrome, leukemia, and other blood-related diseases that are usually done by pathologists using an optical microscope. This process is time-consuming, extremely tedious, and expensive and needs experienced experts in this field. Thus, a computer-aided diagnosis system that assists pathologistsin the diagnostic process can be so effective. Segmentation of WBCsis usually a first step in developing a computer-aided diagnosis system. The main purpose of this paper is to segment WBCs from microscopic images. For this purpose, we present a novel combination of thresholding, kmeans clustering, and modified watershed algorithms in three stages including (1) segmentation of WBCs from a microscopic image, (2) extraction of nuclei from cell’s image, and (3) separation of overlapping cells and nuclei. The evaluation results of the proposed method show that similarity measures, precision, and sensitivity respectively were 92.07, 96.07, and 94.30% for nucleus segmentation and 92.93, 97.41, and 93.78% for cell segmentation. In addition, statistical analysis presents high similarity between manual segmentation and the results obtained by the proposed method.

Keywords


K-means clustering; segmentation; thresholding; watershed algorithm; white blood cells

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References


Mohan H. Textbook of Pathology. New Delhi: Jaypee Brothers; 2005.

Chastine F, Tangel ML, Widyanto MR, Hirota K. Parameter optimization of local fuzzy patterns based on fuzzy contrast measure for white blood cell texture feature extraction. J Adv Comput Intell Intell Inform 2012;16:412-9.

Mohamed M, Far B, Guaily A. An efficient technique for white blood cells nuclei automatic segmentation. 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2012. p. 220-5.

Wu J, Zeng P, Zhou Y, Olivier C. A novel color image segmentation method and its application to white blood cell image analysis. 2006 8th International Conference on Signal Processing. IEEE, 2006.

Otsu N. A threshold selection method from gray-level histograms. Automatica 1975;11:23-7.

Tosta TA, de Abreu AF, Travençolo BA, do Nascimento MZ, Neves LA. Unsupervised segmentation of leukocytes images using thresholding neighborhood valley-emphasis. 2015 IEEE 28th International Symposium on Computer-Based Medical Systems. IEEE, 2015. p. 93-4.

Fan J-L, Lei B. A modified valley-emphasis method for automatic thresholding. Pattern Recognit Lett 2012;33:703-8.

Soltanzadeh R,RabbaniH, TalebiA. Extraction of nucleolus candidate zone in white blood cells of peripheral blood smear images using curvelet transform.ComputMathMethodsMed2012;2012: Article ID 574184, 12 pages.

Ma J, Plonka G. The curvelet transform. IEEE Signal Process Mag 2010;27:118-33.

Sarrafzadeh O, Rabbani H, Talebi A, Banaem HU. Selection of the best features for leukocytes classification in blood smear microscopic images. SPIE Medical Imaging. International Society for Optics and Photonics, 2014. p. 90410P.

Ghosh M, Das D, Chakraborty C, Ray AK. Automated leukocyte recognition using fuzzy divergence. Micron 2010;41:840-6.

Chaira T, Ray AK. Segmentation using fuzzy divergence. Pattern Recognit Lett 2003;24:1837-44.

Liao Q, Deng Y. An accurate segmentation method for white blood cell images. Biomedical Imaging, 2002. Proceedings. 2002 IEEE International Symposium on IEEE, 2002. p. 245-8.

Ongun G, Halici U, Leblebicioglu K, Atalay V, Erkmen A, Beksaç S. An automated differential blood count system. 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2001;3: p. 2583-6.

Sinha N, Ramakrishnan AG. Automation of differential blood count. IEEE Reg 10 Tech Conf Converg Technol Asia-Pacific Reg (TENCON 2003), 2003;2: p. 547-51.

Kass M, Witkin A, Terzopoulos D. Snakes: Active contour models. Int J Comput Vis 1988;1:321-31.

Sadeghian F, Seman Z, Ramli AR, Abdul Kahar BH, Saripan M-I. A framework for white blood cell segmentation in microscopic blood images using digital image processing. Biol Proced Online 2009;11:196-206.

Yang L, Meer P, Foran DJ. Unsupervised segmentation based on robust estimation and color active contour models. IEEE Trans Inf Technol Biomed 2005;9:475-86.

Zack GW, Rogers WE, Latt SA. Automatic measurement of sister chromatid exchange frequency. J Histochem Cytochem 1977;25: 741-53.

Rezatofighi SH, Soltanian-Zadeh H. Automatic recognition of five types of white blood cells in peripheral blood. Comput Med Imaging Graph 2011;35:333-43.

Sarrafzadeh O, Dehnavi AM. Nucleus and cytoplasm segmentation in microscopic images using K-means clustering and region growing. Adv Biomed Res 2015;4:174-84.

Xing F, Yang L. Robust nucleus/cell detection and segmentation in digital pathology and microscopy images: A comprehensive review. IEEE Rev Biomed Eng 2016;9:234-63.

Gonzalez RC, Woods RE, Masters BR. Digital image processing, third edition. J Biomed Opt 2009;14:029901.

Saeedizadeh Z, Mehri Dehnavi A, Talebi A, Rabbani H, Sarrafzadeh O, Vard A. Automatic recognition of myeloma cells in microscopic images using bottleneck algorithm, modified watershed and SVM classifier. J Microsc 2015;261: 46-56.

Putzu L, Caocci G, Di Ruberto C. Leucocyte classification for leukaemia detection using image processing techniques. Artif Intell Med 2014;62:179-91.

Verma NK, Roy A, Vasikarla S. Medical image segmentation using improved mountain clustering technique version-2. Information Technology: New Generations (ITNG), 2010 Seventh International Conference on IEEE, 2010. p. 156-61.

Olson E. Particle shape factors and their use in image analysis -Part 1: Theory. J GXP Compliance 2011;15:85.

Bala A. An improved watershed image segmentation technique using MATLAB. Int J Sci Eng Res 2012;3:1-4.

Gonzalez RC, Woods RE. Digital Image Processing. 3rd ed., NJ: Prentice Hall; 2008.

Seber GA, Lee AJ. Linear Regression Analysis. John Wiley & Sons; 2012.

Vard A, Jamshidi K, Movahhedinia N. An automated approach for segmentation of intravascular ultrasound images based on parametric active contour models. Australas Phys Eng Sci Med 2012;35:135-50.


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