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Feature selection and classification of leukocytes using random forest

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Abstract

In automatic segmentation of leukocytes from the complex morphological background of tissue section images, a vast number of artifacts/noise are also extracted causing large amount of multivariate data generation. This multivariate data degrades the performance of a classifier to discriminate between leukocytes and artifacts/noise. However, the selection of prominent features plays an important role in reducing the computational complexity and increasing the performance of the classifier as compared to a high-dimensional features space. Therefore, this paper introduces a novel Gini importance-based binary random forest feature selection method. Moreover, the random forest classifier is used to classify the extracted objects into artifacts, mononuclear cells, and polymorphonuclear cells. The experimental results establish that the proposed method effectively eliminates the irrelevant features, maintaining the high classification accuracy as compared to other feature reduction methods.

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Acknowledgments

Authors are thankful to Defence Research and Development Establishment (DRDE), Gwalior, India, for funding a part of this work under the project (DRDE-P1-2011/Task-190). Authors are also thankful to Dr. S. C. Pant, Scientist ‘F’ at D.R.D.E., Gwalior, and Dr. Vinay Lomash, Ph.D., MVSc (Pathology) for tendering their valuable help in the analysis of microscopic images.

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  1. ABV-Indian Institute of Information Technology and Management, Gwalior, 474010, India

    Mukesh Saraswat & K. V. Arya

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  1. Mukesh Saraswat
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Correspondence to Mukesh Saraswat.

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Saraswat, M., Arya, K.V. Feature selection and classification of leukocytes using random forest. Med Biol Eng Comput 52, 1041–1052 (2014). https://doi.org/10.1007/s11517-014-1200-8

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