Segmentation of White Blood Cells using Image Segmentation Algorithms

Segmentation of images plays a key role in Image Processing as it simplifies further processing by separating a broad image into many parts. White Blood Cells (WBC) microscopic images allow haematologists to predict vulnerability to several diseases. Our intension is to identify Nucleus of White Blood cells. Colour is a significant reference point for discerning segmented WBCs from microscopic images. Results reveal that in identifying the nucleus of various types of WBC: including Neutrophils, Lymphocytes, Eosinophils, Monocytes, Basophils. In this study, we will evaluate the efficiency of the Edge detection Algorithm (log & Canny) methods, k-Means Algorithm, Linear Transformed Image, Color-based technique, and will compare the results with the original image. This will allow haematologists for clear identification of WBC. Analysis of the results using Algorithms shows that Edge detection and k-means based segmentation is the most suitable approach for segmenting WBC cells.