Patch-Based White Blood Cell Nucleus Segmentation Using Fuzzy Clustering

Cell segmentation is one of important steps in the automatic white blood cell dierential counting. In this paper, we propose a technique to segment singlecell images of white blood cells in bone marrow into two regions, i.e., nucleus and non-nucleus. The segmentation is based on the fuzzy C-means clustering and mathematical morphology. The segmentation results are compared to an expert’s manually segmented images. The initial investigation of the use of the derived segmented images in the cell classification is also performed by using the Bayes classifier.

[1]  D. Sturm,et al.  The Morphology of Human Blood Cells , 2019 .

[2]  G. Matheron.  Random Sets and Integral Geometry , 1976 .

[3]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[4]  W. Peizhuang.  Pattern Recognition with Fuzzy Objective Function Algorithms (James C. Bezdek) , 1983 .

[5]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .

[6]  H. Harms,et al.  Segmentation of stained blood cell images measured at high scanning density with high magnification and high numerical aperture optics. , 1986, Cytometry.

[7]  Edward R. Dougherty,et al.  An introduction to morphological image processing , 1992 .

[8]  I. Cseke,et al.  A fast segmentation scheme for white blood cell images , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol. III. Conference C: Image, Speech and Signal Analysis,.

[9]  B Palcic,et al.  Automated image detection and segmentation in blood smears. , 1992, Cytometry.

[10]  George J. Klir,et al.  Fuzzy sets and fuzzy logic - theory and applications , 1995 .

[11]  M Beksaç,et al.  An artificial intelligent diagnostic system on differential recognition of hematopoietic cells from microscopic images. , 1997, Cytometry.

[12]  James M. Keller,et al.  Fuzzy patch label relaxation in bone marrow cell segmentation , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[13]  Dwi Anoraganingrum,et al.  Cell segmentation with median filter and mathematical morphology operation , 1999, Proceedings 10th International Conference on Image Analysis and Processing.

[14]  James M. Keller,et al.  White blood cell detection in bone marrow images , 1999, 18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397).

[15]  Edward R. Dougherty,et al.  Random processes for image and signal processing , 1998, SPIE / IEEE series on imaging science and engineering.

[16]  Paul D. Gader,et al.  Counting white blood cells using morphological granulometries , 2000, J. Electronic Imaging.

[17]  Paul D. Gader,et al.  Training neural networks to count white blood cells via a minimum counting error objective function , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[18]  Paul D. Gader,et al.  Non-homothetic granulometric mixing theory with application to blood cell counting , 2001, Pattern Recognit..

[19]  Anders Heyden,et al.  Model-based segmentation of leukocytes clusters , 2002, Object recognition supported by user interaction for service robots.

[20]  Paul D. Gader,et al.  System-level training of neural networks for counting white blood cells , 2002, IEEE Trans. Syst. Man Cybern. Part C.

[21]  Qingmin Liao,et al.  An accurate segmentation method for white blood cell images , 2002, Proceedings IEEE International Symposium on Biomedical Imaging.

[22]  Nipon Theera-Umpon.  Automatic White Blood Cell Classification Using Biased-Output Neural Networks with Morphological Features , 2003 .

[23]  Kan Jiang,et al.  A novel white blood cell segmentation scheme using scale-space filtering and watershed clustering , 2003, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).