Abstract
Accurate identification and counting of White Blood Cells (WBCs) from microscopy blood cell images are vital for several blood-related disease diagnoses such as leukemia. The inevitability of automated cell image analysis in medical diagnosis results in a plethora of research for the last few decades. Microscopic blood cell image analysis involves three major steps: cell segmentation, classification, and counting. Several techniques have been employed separately to solve these three problems. In this paper, a simple unified model is proposed for White Blood Cell segmentation, feature extraction for classification, and counting with connected mathematical morphological operators implemented using the max-tree data structure. Max-tree creates a hierarchical representation of connected components of all possible gray levels present in an image in such a way that the root holds the connected components comprise of pixels with the lowest intensity value and the connected components comprise of pixels with the highest intensity value are in the leaves. Any associated attributes such as the size or shape of each connected component can be efficiently calculated on the fly and stored in this data structure. Utilizing this knowledge-rich data structure, we obtain a better segmentation of the cells that preserves the morphology of the cells and consequently obtain better accuracy in cell counting.
Highlights
Microscopic blood cell image analysis is crucial for the diagnosis of several blood-related diseases
This paper proposed a method of blood cell image segmentation, feature extraction, and counting using connected morphological operators implemented using the max-tree data structure
The performance of the White Blood Cells (WBCs) cell segmentation follows the performance of the nucleus segmentation
Summary
Microscopic blood cell image analysis is crucial for the diagnosis of several blood-related diseases. The capability of this knowledge-rich data structure has been www.ijacsa.thesai.org (IJACSA) International Journal of Advanced Computer Science and Applications, Vol 11, No 9, 2020 utilized for cell segmentation, feature extraction, and counting. Different types of connected operators can be obtained by the composition of any family of openings and closings by reconstruction Connected attribute operators such as attribute openings, closing, thickenings, and thinnings can be utilized to filter connected components based on their attributes such as size, shape, contrast, etc. The difference image, I − Ir should contain the structures that fail to meet the desired criteria Derivation of these size and shape based operators for grayscale images is straightforward and can be obtained from their binary counterpart
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