The aim of this research is to automate the process of detection and classification of leukocytes using image processing techniques. White blood cell recognition and classification into various distinct subtypes is very important in clinical and laboratory tests. The nucleus features are adequate to identify the type of the cell in most of the case, the traditional morphology test which is done by a hematology expert to look at the cell under the microscope is a time consuming and tedious job, beside that the medical instrument which is used to do the test are costly and may not be exist in all the hospitals and clinics. An automatic image segmentation system can make the inspection procedure of blood smear much easier and faster and the amount of data that can be analyzed by such a clinician handle more data than they normally can handle. The most crucial step in such systems is in white blood cell segmentation. In this research we focus on white blood cell nucleus segmentation that can be used to separate the nucleus from the whole cell body by using a combination of automatic contrast stretching supported by image arithmetic operation, minimum filter and global threshold techniques. Results showed that the proposed method manages to obtain accuracy between 85-98%. The results showed that the proposed method is promising comparing to the result from the expert.
H.T. Madhloom, S.A. Kareem, H. Ariffin, A.A. Zaidan, H.O. Alanazi and B.B. Zaidan, 2010. An Automated White Blood Cell Nucleus Localization and Segmentation using Image Arithmetic and Automatic Threshold. Journal of Applied Sciences, 10: 959-966.