The Best Texture Features for Leukocytes Recognition
DOI: 10.4103/jmss.JMSS_7_17
Abstract
diagnose many diseases such as leukemia, and infections. An accurate process for recognizing
leukocytes is to evaluate a blood smear under a microscope by an expert. Since, this procedure is
manual, time-consuming and tedious, making the procedure automatic would overcome these problems.
In an automated CAD (Computer-Aided-Design) system for this purpose, a crucial module is leukocytes
recognition. In this paper, we are looking for the best features in order to recognize five types of
leukocytes (Monocyte, Lymphocyte, Neutrophil, Eosinophil and Basophil) from microscopic images of
blood smear in an automated cell counting system. Methods: In this work, we focus on the texture
features and seven categories: GLCM features, Haralick features, Spectral texture features, Waveletbased features, Gabor-based features, CoALBP and RICLBP are analyzed to find the best features for
leukocytes detection. The best features of each category are selected using stepwise regression and
finally three well-known classifiers called K-NN, LDA and NB are utilized for classification. Results:
The proposed system is tested on a self-provided dataset composed of 200 cell images. In our
experiments, to evaluate the process, the accuracy of each leukocyte type and the mean accuracy
are computed. RICLBP features achieved the best mean accuracy (85.53%) for LDA classifier.
Conclusions: In our experiments, although the maximum mean accuracy (85.53%) went with RICLBP
features, but the accuracies of all five leukocyte types weren’t maximized for RICLBP features. This
result directs us to design and develop a system based on multiple features and multiple classifiers to
maximize the accuracies even for each individual cell type in our future work
Keywords
Full Text:
PDFReferences
Saraswat M, Arya KV. Automated microscopic image analysis for leukocytes identification: A survey. Micron 2014;65:20-33.
Sarrafzadeh O, Rabbani H, Dehnavi AM, Talebi A. Detecting different sub-types of acute myelogenous leukemia using dictionary learning and sparse representation. Quebec City, QC: IEEE International Conference on Image Processing (ICIP); 2015. p. 3339-43. doi: 10.1109/ICIP.2015.7351422.
Sarrafzadeh O, Dehnavi AM. Nucleus and cytoplasm segmentation in microscopic images using K-means clustering and region growing. Adv Biomed Res 2015;4:174-9.
Sarrafzadeh O, Dehnavi AM, Rabbani H, Talebi A. A simple and accurate method for white blood cells segmentation using K-means algorithm. IEEE Workshop on Signal Processing Systems (SiPS): Design and Implementation, vol 2015, 2015.
Supardi NZ, Mashor MY, Harun NH, Bakri FA, Hassan R. Classification of blasts in acute leukemia blood samples using k nearest neighbour. 8th IEEE International Colloquium on Signal Processing and its Applications, 2012, pp. 461-5.
Sarrafzadeh O, Rabbani H, Mehri Dehnavi A, Talebi A. Analyzing features by SWLDA for the classification of HEp-2 cell images using GMM. Pattern Recognit Lett 2016;82:44-55.
Shirazi SH, Umar AI, Naz S, Razzak MI. Efficient leukocyte segmentation and recognition in peripheral blood image. Technol Heal Care 2016;24:335-47.
Theera-Umpon N. White blood cell segmentation and classification in microscopic bone marrow images. Fuzzy Systems and Knowledge Discovery. Berlin, Heidelberg: Springer; 2005. p. 787-96.
Jung C, Kim C, Chae SW, Oh S. Unsupervised segmentation of overlapped nuclei using Bayesian classification. Biomed Eng IEEE Trans 2010;57:2825-32.
Sarrafzadeh O, Dehnavi AM, Rabbani H, Ghane N, Talebi A. Circlet based framework for red blood cells segmentation and counting. IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation, vol 2015, 2015.
Rawat J, Bhadauria HS, Singh A, Virmani J. Review of leukocyte classification techniques for microscopic blood images. 2nd International Conference on Computing for Sustainable Global Development (INDIACom), 2015, pp. 1948-54.
Huang D-C, Hung K-D, Chan Y-K. A computer assisted method for leukocyte nucleus segmentation and recognition in blood smear images. J Syst Softw 2012;85:2104-18.
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.
Habibzadeh M, Krzy ak A, Fevens T. White blood cell differential counts using convolutional neural networks for low resolution images. Artificial Intelligence and Soft Computing. Berlin, Heidelberg: Springer 2013. p. 263-74.
Sabino DMU, Da Fontoura Costa L, Rizzatti EG, Zago MA. A texture approach to leukocyte recognition. Real-Time Imaging 2004;10:205-16.
Sabino DMU, Costa LF, Rizzatti EG, Zago MA. Toward leukocyte recognition using morphometry, texture and color. 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano, vol 2, 2004, pp. 121-4.
Zhao J, Zhang M, Zhou Z, Chu J, Cao F. Automatic detection and classification of leukocytes using convolutional neural networks. Med Biol Eng Comput 2016:1-5.
Li X, Cao Y. A robust automatic leukocyte recognition method based on island-clustering texture. J Innov Opt Health Sci 2016;9:1650009.
Sarrafzadeh O, Rabbani H, Talebi A, Banaem HU. Selection of the best features for leukocytes classification in blood smear microscopic images. Proc. SPIE 9041, Medical Imaging 2014: Digital Pathology, 90410P, 2014. doi: 10.1117/12.2043605.
Zuiderveld K. Contrast limited adaptive histogram equalization. In:
Paul SH, editor. Graphics gems IV. San Diego, CA, USA: Academic Press Professional, Inc.; 1994, p. 474-85.
Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern 1973;3:610-21
Gonzalez RC, Woods RE, Eddins SL. Digital Image Processing Using MATLAB. Upper Saddle River, NJ: Prentice Hall; Pearson Education. 2004.
Arivazhagan S, Ganesan L. Texture classification using wavelet transform. Pattern Recognition Letters 2003;24:1513-21.
Ji H, Yang X, Ling H, Xu Y. Wavelet domain multifractal analysis for static and dynamic texture classification. IEEE Trans Image Process 2013;22:286-99.
Haghighat M, Zonouz S, Abdel-Mottaleb M. CloudID: Trustworthy cloud-based and cross-enterprise biometric identification. Expert Syst Appl 2015;42:7905-16.
Nosaka R, Ohkawa Y, Fukui K. Feature extraction based on cooccurrence of adjacent local binary patterns. 5th Pacific Rim Conference on Advances in Image and Video Technology, 2012, pp. 82-91.
Nosaka R, Suryanto CH, Fukui K. Rotation invariant co-occurrence among adjacent LBPs. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Berlin, Heidelberg: Springer; 2013. p. 15-25.
Draper NR, Smith H, Pownell E. Applied Regression Analysis. Vol 3 New York: Wiley 1966.
Mitchell TM. Machine Learning. New York City, New York, US: McGraw-Hill; 1997.
Guo Y, Hastie T, Tibshirani R. Regularizedlinear discriminant analysis and its application in microarrays. Biostatistics 2007;8:86-100.
Refbacks
- There are currently no refbacks.
https://e-rasaneh.ir/Certificate/22728
ISSN : 2228-7477