Statistical pattern analysis of white blood cell nuclei morphometry

Quantitative microscopy has strengthened conventional diagnostic scheme through better understanding of microscopic features from clinical perspective. Towards this, pathological image analysis has gained immense significance among medical fraternity through visualization and quantitative evaluation of clinical features. Till today pathological inspection of human blood is solely dependent on subjective assessment which usually leads to significant inter-observer variation in grading and subsequently resulting in late diagnosis of certain disease. This paper introduced a systematic approach to morphologically characterize five types of white blood cells (WBC) through statistical pattern analytics. Marker controlled watershed segmentation embedded with morphological operator is employed to segment WBC and its nuclei from light microscopic image of blood samples. Henceforth, one cellular and eight nuclei-based geometric features are computed mathematically and analyzed statistically with t-test and kernel density functions to show their discriminating potentiality among the groups. Amongst all these features, only four statistical significant features are fed to Naïve Bayes classifier for pattern identification with 83.2% overall accuracy. Detailed results are also given here.

[1]  Georges Flandrin,et al.  Automated Detection of Working Area of Peripheral Blood Smears Using Mathematical Morphology , 2003, Analytical cellular pathology : the journal of the European Society for Analytical Cellular Pathology.

[2]  Hamid Soltanian-Zadeh,et al.  A New Approach to White Blood Cell Nucleus Segmentation Based on Gram-Schmidt Orthogonalization , 2009, 2009 International Conference on Digital Image Processing.

[3]  Andrew G. Dempster,et al.  Analysis of infected blood cell images using morphological operators , 2002, Image Vis. Comput..

[4]  V. Piuri,et al.  Morphological classification of blood leucocytes by microscope images , 2004, 2004 IEEE International Conference onComputational Intelligence for Measurement Systems and Applications, 2004. CIMSA..

[5]  T. Subba Rao,et al.  Classification, Parameter Estimation and State Estimation: An Engineering Approach Using MATLAB , 2004 .

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

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

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

[9]  Leyza Baldo Dorini,et al.  A scaled morphological toggle operator for image transformations , 2006, 2006 19th Brazilian Symposium on Computer Graphics and Image Processing.

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

[11]  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.

[12]  N. Theera-Umpon.  Patch-Based White Blood Cell Nucleus Segmentation Using Fuzzy Clustering , 2005 .

[13]  Luciano da Fontoura Costa,et al.  A texture approach to leukocyte recognition , 2004, Real Time Imaging.

[14]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Régis Beuscart,et al.  Image Segmentation And Classification Methods To Detect Leukemias , 1991, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society Volume 13: 1991.

[16]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[17]  C. Dunphy,et al.  Applications of flow cytometry and immunohistochemistry to diagnostic hematopathology. , 2004, Archives of pathology & laboratory medicine.

[18]  S. Beksac,et al.  An automated differential blood count system , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[19]  Andrew Mehnert,et al.  An improved seeded region growing algorithm , 1997, Pattern Recognit. Lett..

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

[21]  F. Scotti,et al.  Automatic morphological analysis for acute leukemia identification in peripheral blood microscope images , 2005, CIMSA. 2005 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, 2005..

[22]  J. Wade Davis,et al.  Statistical Pattern Recognition , 2003, Technometrics.

[23]  R. Lobato,et al.  A Feature Extraction Method Based on Morphological Operators for Automatic Classification of Leukocytes , 2008, 2008 Seventh Mexican International Conference on Artificial Intelligence.

[24]  David G. Stork,et al.  Pattern Classification , 1973 .

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

[26]  Xinhua Zhuang,et al.  Image Analysis Using Mathematical Morphology , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Ma Yi-de,et al.  A new method for blood cell image segmentation and counting based on PCNN and autowave , 2008, 2008 3rd International Symposium on Communications, Control and Signal Processing.

[28]  A. G. Ramakrishnan,et al.  Blood Cell Segmentation Using EM Algorithm , 2002, ICVGIP.

[29]  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).

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

[31]  Korris Fu-Lai Chung,et al.  Applying the improved fuzzy cellular neural network IFCNN to white blood cell detection , 2007, Neurocomputing.

[32]  Nipon Theera-Umpon,et al.  Morphological Granulometric Features of Nucleus in Automatic Bone Marrow White Blood Cell Classification , 2007, IEEE Transactions on Information Technology in Biomedicine.

[33]  T. V. Sreenivas,et al.  Teager energy based blood cell segmentation , 2002, 2002 14th International Conference on Digital Signal Processing Proceedings. DSP 2002 (Cat. No.02TH8628).