Feature Analysis and Automatic Identification of Leukemic Lineage Blast Cells and Reactive Lymphoid Cells from Peripheral Blood Cell Images

Abstract

Background: Automated peripheral blood (PB) image analyzers usually underestimate the total number of blast cells, mixing them up with reactive or normal lymphocytes. Therefore, they are not able to discriminate between myeloid or lymphoid blast cell lineages. The objective of the proposed work is to achieve automatic discrimination of reactive lymphoid cells (RLC), lymphoid and myeloid blast cells and to obtain their morphologic patterns through feature analysis.

Methods: In the training stage, a set of 696 blood cell images was selected in 32 patients (myeloid acute leukemia, lymphoid precursor neoplasms and viral or other infections). For classification, we used support vector machines, testing different combinations of feature categories and feature selection techniques. Further, a validation was implemented using the selected features over 220 images from 15 new patients (five corresponding to each category).

Results: Best discrimination accuracy in the training was obtained with feature selection from the whole feature set (90.1%). We selected 60 features, showing significant differences (P < 0.001) in the mean values of the different cell groups. Nucleus-cytoplasm ratio was the most important feature for the cell classification, and color-texture features from the cytoplasm were also important. In the validation stage, the overall classification accuracy and the true-positive rates for RLC, myeloid and lymphoid blast cells were 80%, 85%, 82% and 74%, respectively.

Conclusion: The methodology appears to be able to recognize reactive lymphocytes well, especially between reactive lymphocytes and lymphoblasts.

Keywords: cytology; hematology; image processing; leukemia; pathology.

Figure 1

The complete methodology for the training stage, steps, and categories in each one is described in the scheme. A training set (TS) of 696 images were used to obtain a database of features, and a detailed feature analysis was performed: PCA, feature experiments, feature selection, and the statistical analysis of the selected features. The SVM classifier is tuned using the TS for further validation classification.

Figure 2

Examples of segmented cells: myeloid blast cells (MBC), lymphoid blast cells (LBC), and reactive lymphoid cells (RLC), respectively. The images show three different regions: nucleus (yellow line), cytoplasm (blue line), and peripheral zone around the cell (red line). (X 1,000, May Grünwald‐Giemsa stain).

Figure 3

First and second principal components of all set of features obtained by principal component analysis (PCA), showing a different position regarding these principal components in the groups of cells analyzed. Reactive lymphoid cells (RLC) showed a different pattern with respect to blast cells. Myeloid blast cells (MBC) and lymphoid blast cells (LBC) shared a small area in this PCA representation.

Figure 4

Feature comparison using box plots of features containing the central 50% of the images in the group. The line in the box represents the median.

Figure 5

Examples of individual cell images corresponding to the cells that were correctly classified. The first row corresponds to reactive lymphoid cells (RLC), the second to lymphoid blast cells (LBC) and the third row to myeloid blast cells (MBC). (X 1,000, May Grünwald‐Giemsa stain).