Parameter Optimization of Local Fuzzy Patterns Based on Fuzzy Contrast Measure for White Blood Cell Texture Feature Extraction
Chastine Fatichah*,**, Martin Leonard Tangel*,
Muhammad Rahmat Widyanto***, Fangyan Dong*,
and Kaoru Hirota*
*Department Computational Intelligence & Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan
**Informatics Department, Faculty of Technology Information, Institut Teknologi Sepuluh Nopember, Kampus ITS, Surabaya 60111, Indonesia
***Faculty of Computer Science, University of Indonesia, Kampus UI, Depok, Jawa Barat, Indonesia
The parameter optimization of local fuzzy patterns based on the fuzzy contrast measure is proposed for extracting white blood cell texture. The proposed method obtains the optimal parameter values of the nucleus and cytoplasm region of white blood cell image and the best accuracy rate of white blood cell classification can therefore be achieved. To evaluate the performance of the proposed method, 100 microscopic white blood cell images and the supervised learning method are used for white blood cell classification. Results show that the average accuracy rate of white blood cell classification using local fuzzy pattern features with optimal parameter values of a nucleus and a cytoplasm region is 4% more accurate than with uniform parameter values and is 5–18% more accurate than other feature extraction methods. White blood cell feature extraction is part of the white blood cell classification in an automatic cancer diagnosis that is being developed. In addition, the proposed method can be used to obtain the optimal parameter of local fuzzy patterns for other types of datasets.
-  N. Guo, L. Zeng, and Q. Wu, “A Method based on Multispectral Imaging Technique for White Blood Cell Segmentation,” Computers in Biology and Medicine, Elsevier, Vol.37, pp. 70-76, 2006.
-  S. Eom, S. Kim, V. Shin, and B. Ahn, “Leukocyte Segmentation in Blood Smear Images Using Region-Based Active Contours,” LNCS 4179, Springer-Verlag, pp. 867-876, 2006.
-  P. S. Hiremath, P. Bannigidad, and S. Geeta, “Automated Identification and Classification ofWhite Blood Cells (Leukocytes) in Digital Microscopic Images,” IJCA Special Issue on “Recent Trends in Image Processing and Pattern Recognition (RTIPPR),” 2010.
-  C. Pan, X. Yan, and C. Zheng, “Recognition of Blood and Bone Marrow Cells using Kernel-based Image Retrieval,” Int. J. of Computer Science and Network Security (IJCSNS), Vol.6, No.10, October, 2006.
-  P. Yampri, C. Pintavirooj, S. Daochai, and S. Teartulakarn, “White Blood Cell Classification based on the Combination of Eigen Cell and Parametric Feature Detection,” IEEE Conf. on Industrial Electronics and Applications (ICIEA), 2006.
-  P. Gómez-Gil, M. Ramírez-Cortés, J. González-Bernal, Á. G. Pedrero, C. I. Prieto-Castro, D. Valencia, R. Lobato, and J. E. Alonso, “A Feature Extraction Method Based on Morphological Operators for Automatic Classification of Leukocytes,” Seventh Mexican Int. Conf. on Artificial Intelligence, pp. 227-232, 2008.
-  N. Theera-Umpon and S. Dhompongsa, “Morphological Granulometric Features of Nucleus in Automatic Bone Marrow White Blood Cell Classification,” IEEE Trans. on Information Technology in Biomedicine, Vol.11, No.3, pp. 353-359, 2007.
-  J. Angulo, J. Klossa, and G. Flandarin, “Ontology-Based Lymphocyte Population Description using Mathematical Morphology on Colour Blood Images,” Cellular and Molecular BiologyTM, Vol.52, No.6, pp. 2-15, October, 2006.
-  C. Reta, L. Altamirano, J. A. Gonzales, R. Diaz, and J. S. Guichard, “Segmentation of Bone Marrow Cell Images for Morphological Classification of Acute Leukemia,” Proc. of the Twenty-Third Int. Florida Artificial Intelligence Research Society Conf. (FLAIRS 2010), pp. 86-91, 2010.
-  S. H. Rezatofighi, K. Khaksari, and H. Soltanian-Zadeh, “Automatic Recognition of Five Types ofWhite Blood Cells in Peripheral Blood,” LNCS 6112, Springer-Verlag, pp. 161-172, 2010.
-  L. Caponetti, C. Castiello, A. M. Fanelli, and P. Górecki, “Texture Segmentation with Local Fuzzy Patterns and Neuro-fuzzy Decision Support,” LNAI 4252, Springer-Verlag, pp. 340-347, 2006.
-  P. G’orecki and L. Caponetti, “Color Texture Segmentation with Local Fuzzy Patterns and Spatially Constrained Fuzzy C-Means,” LNAI 4578, Springer-Verlag, pp. 362-369, 2007.
-  http://www.cellavision.se/cellatlas
-  M. B. de Almeida, A. de Pádua Braga, and J. P. Braga, “SVM-KM: Speeding SVMs Learning with a priori Cluster Selection and k-Means,” VI Brazilian Symposium on Neural Networks (SBRN’00), p. 162, 2000.
-  C. J. C. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition,” Data Mining and Knowledge Discovery, Vol.2, pp. 121-167, Kluwer Academic Publishers, Boston, 1998.
-  C. Fatichah, M. L. Tangel, M. R.Widyanto, F. Dong, and K. Hirota, “Parameter Optimization of Local Fuzzy Patterns for Extracting White Blood Cell Texture Feature,” World Congress of Int. Fuzzy Systems Association 2011, Indonesia, June, 2011.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.