An enhanced threshold based technique for white blood cells nuclei automatic segmentation

One of the most important clinical examination tests is the blood test. In a clinical laboratory, counting different blood cells is important. Manual microscopic inspection is time-consuming and requires technical knowledge. Therefore, automatic medical diagnosis systems are required to help physicians to diagnose diseases in a fast and yet efficient way. Cell automatic classification has larger interest especially for clinics and laboratories; the most important step in automatic classification success is segmentation. This paper shows an efficient technique for automatic blood cell nuclei segmentation. This technique is relying on enhancing and filtering the gray scale image contrast. False objects are removed utilizing minimum segment size. 365 blood images were used to examine this segmentation technique. Quantitative analysis of the proposed segmentation technique on the blood image set gives 80.6% accuracy. In comparison to other techniques the proposed segmentation technique performance was found to be superior. The five normal white blood cells types were used for evaluation to compare isolated performance. Eosinophil was found to have the lowest segmentation accuracy which is 71.0% and Monocyte was the highest one with 85.9%. The blood images dataset and the source code are published on MATLAB file exchange website for comparison and re-production.