An Improved Computer Vision Method for White Blood Cells Detection

Computer Engineering

The analysis of blood cells in microscope images can provide useful information concerning the health of patients. There are three major blood cell types, namely, erythrocytes (red), leukocytes (white), and platelets. Manual classification is time consuming and susceptible to error due to the different morphological features of the cells. This paper presents an intelligent system that simulates a human visual inspection and classification of the three blood cell types. The proposed system comprises two phases: The image preprocessing phase where blood cell features are extracted via global pattern averaging, and the neural network arbitration phase where training is the first and then classification is carried out. Experimental results suggest that the proposed method performs well in identifying blood cell types regardless of their irregular shapes, sizes and orientation, thus providing a fast, simple and efficient rotational and scale invariant blood cell identification system which can be used in automating laboratory reporting.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.