Segmentation of White Blood Cells through Nucleus Mark Watershed Operations and Mean Shift Clustering

This paper presents a novel method for segmentation of white blood cells (WBCs) in peripheral blood and bone marrow images under different lights through mean shift clustering, color space conversion and nucleus mark watershed operation (NMWO). The proposed method focuses on obtaining seed points. First, color space transformation and image enhancement techniques are used to obtain nucleus groups as inside seeds. Second, mean shift clustering, selection of the C channel component in the CMYK model, and illumination intensity adjustment are employed to acquire WBCs as outside seeds. Third, the seeds and NMWO are employed to precisely determine WBCs and solve the cell adhesion problem. Morphological operations are further used to improve segmentation accuracy. Experimental results demonstrate that the algorithm exhibits higher segmentation accuracy and robustness compared with traditional methods.

[1]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Bo Zhang,et al.  Tracking fluorescent cells with coupled geometric active contours , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[3]  Guanglei Xiong,et al.  Automated Segmentation of Drosophila RNAi Fluorescence Cellular Images Using Deformable Models , 2006, IEEE Transactions on Circuits and Systems I: Regular Papers.

[4]  S. Beucher,et al.  Watersheds of functions and picture segmentation , 1982, ICASSP.

[5]  Le Yu,et al.  A WBC segmentation methord based on HSI color space , 2011, 2011 4th IEEE International Conference on Broadband Network and Multimedia Technology.

[6]  Xiaomei Li,et al.  White Blood Cell Segmentation by Color-Space-Based K-Means Clustering , 2014, Sensors.

[7]  Rafael C. González,et al.  Digital image processing using MATLAB , 2006 .

[8]  Cecilia Di Ruberto,et al.  White Blood Cells Identification and Counting from Microscopic Blood Images , 2013 .

[9]  Brian V. Funt,et al.  Color Constant Color Indexing , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Larry D. Hostetler,et al.  The estimation of the gradient of a density function, with applications in pattern recognition , 1975, IEEE Trans. Inf. Theory.

[11]  J. Davies,et al.  Molecular Biology of the Cell , 1983, Bristol Medico-Chirurgical Journal.

[12]  Matthew P. Wand,et al.  Kernel Smoothing , 1995 .

[13]  Luc Vincent,et al.  Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  M. Holl,et al.  Differential blood cell counts obtained using a microchannel based flow cytometer , 1997, Proceedings of International Solid State Sensors and Actuators Conference (Transducers '97).

[15]  Jos B. T. M. Roerdink,et al.  The Watershed Transform: Definitions, Algorithms and Parallelization Strategies , 2000, Fundam. Informaticae.

[16]  Yizong Cheng,et al.  Mean Shift, Mode Seeking, and Clustering , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Behrouz Homayoun Far,et al.  An efficient technique for white blood cells nuclei automatic segmentation , 2012, 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[18]  Shitong Wang,et al.  A new detection algorithm (NDA) based on fuzzy cellular neural networks for white blood cell detection , 2006, IEEE Transactions on Information Technology in Biomedicine.

[19]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Vannary Meas-Yedid,et al.  Segmentation and tracking of migrating cells in videomicroscopy with parametric active contours: a tool for cell-based drug testing , 2002, IEEE Transactions on Medical Imaging.

[21]  Der-Chen Huang,et al.  Leukocyte nucleus segmentation and recognition in color blood-smear images , 2012, 2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings.

[22]  H. S. Bhadauria,et al.  White blood nucleus extraction using K-Mean clustering and mathematical morphing , 2014, 2014 5th International Conference - Confluence The Next Generation Information Technology Summit (Confluence).

[23]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[24]  M. Y. Mashor,et al.  White blood cell segmentation for acute leukemia bone marrow images , 2012, 2012 International Conference on Biomedical Engineering (ICoBE).

[25]  Nancy M. Salem,et al.  Segmentation of white blood cells from microscopic images using K-means clustering , 2014, 2014 31st National Radio Science Conference (NRSC).

[26]  Salim Arslan,et al.  A color and shape based algorithm for segmentation of white blood cells in peripheral blood and bone marrow images , 2014, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[27]  Jae-Yeal Nam,et al.  Automatic white blood cell segmentation using stepwise merging rules and gradient vector flow snake. , 2011, Micron.

[28]  Stanislaw Osowski,et al.  Feature generation for the cell image recognition of myelogenous leukemia , 2004, 2004 12th European Signal Processing Conference.

[29]  Hamid Soltanian-Zadeh,et al.  Automatic Recognition of Five Types of White Blood Cells in Peripheral Blood , 2010, ICIAR.

[30]  Kosin Chamnongthai,et al.  Acute leukemia classification by using SVM and K-Means clustering , 2014, 2014 International Electrical Engineering Congress (iEECON).

[31]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..