Statistical pattern analysis of white blood cell nuclei morphometry

Quantitative microscopy has strengthened conventional diagnostic scheme through better understanding of microscopic features from clinical perspective. Towards this, pathological image analysis has gained immense significance among medical fraternity through visualization and quantitative evaluation of clinical features. Till today pathological inspection of human blood is solely dependent on subjective assessment which usually leads to significant inter-observer variation in grading and subsequently resulting in late diagnosis of certain disease. This paper introduced a systematic approach to morphologically characterize five types of white blood cells (WBC) through statistical pattern analytics. Marker controlled watershed segmentation embedded with morphological operator is employed to segment WBC and its nuclei from light microscopic image of blood samples. Henceforth, one cellular and eight nuclei-based geometric features are computed mathematically and analyzed statistically with t-test and kernel density functions to show their discriminating potentiality among the groups. Amongst all these features, only four statistical significant features are fed to Naïve Bayes classifier for pattern identification with 83.2% overall accuracy. Detailed results are also given here.