The Best Texture Features for Leukocytes Recognition

omid sarrafzadeh, Alireza Mehri Dehnavi, Hossein Yousefi Banaem, Ardeshir Talebi, Arshin Gharibi

DOI: 10.4103/jmss.JMSS_7_17

Abstract


Background: Differential counting of white blood cells (WBCs or leukocytes) is a common task to
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


Automatic leukocytes recognition, best texture features, blood smear, computer-aided design (CAD) system, microscopic images

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