White blood cells classifications by SURF image matching PCA and dendrogram

Abstract

Determination and classification of white blood cells are very important for diagnosing many diseases. The number of white blood cells and morphological changes or blasts of them provide valuable information for the positive results of the diseases such as Acute Lymphocytic Leucomia (ALL). Recognition and classification of white cells as basophils lymphocytes neutrophils monocytes and eosinophils also give additional information for the diagnosis of many diseases. We are developing an automatic process for counting size determination and classification of white blood cells. In this paper we give the results of the classification process for which we experienced a study with hundreds of images of white blood cells. This process will help to diagnose especially ALL disease in a fast and automatic way. Three methods are used for classification of five types of white blood cells. The first one is a new algorithm utilizing image matching for classification that is called the Speed-Up Robust Feature detector (SURF). The second one is the PCA that gives the advantage of dimension reduction. The third is the classification tree called dendrogram following the PCA. Satisfactory results are obtained by two techniques. © 2018 Elsevier B.V. All rights reserved.