An efficient technique for white blood cells nuclei automatic segmentation

Abstract

Blood tests are of the most important and often requested clinical examinations. Manual microscopic assessment is a must do when a blood sample is suspicious of abnormality. This manual process is tedious, time consuming and subjective. Automating microscopic blood classification is desirable to help the pathologists to speed-up and enhance the results accuracy. Segmentation is the first and most important step in automatic blood cell classification. In this paper, we present an effective technique for automatic blood cell nuclei segmentation. The technique is based on gray scale contrast enhancement and filtering. Minimum segment size is implemented to remove false objects. The technique is tested on 365 blood images. The segmentation performance is quantitatively evaluated on the test set to be 79.7%. This performance is high compared to other published algorithm executed on the same dataset. Evaluation is done on each of the five normal white blood cell types to compare separate performance. The lowest segmentation accuracy is for Eosinophil with 69.3% and the highest is Monocyte with 86.3%. The MATLAB source code and the blood images dataset are published on MATLAB file exchange website for comparison and re-production.