Segmentation and detection Of white blood cells Using fuzzy based algorithms

Abstract

White Blood Cell WBC detection is one of the most key steps in the automatic WBC recognition system Its accuracy and stability greatly affect the recognition accuracy of the whole system In this work computer based segmentation and classification of the four main classes of WBC Neutrophils Eosinophils Lymphocytes and Monocytes were completed Soft computing algorithms including Neural Network NN and Polynomial Classifiers PC were used for WBC classification while watershed and thresholding based on size shape colour and texture characteristics were used to segment WBC from Red Blood Cells RBC platelets cell fragments and stains Automating the segmentation and classification of WBC could provide a useful tool in medical diagnoses newlineImage processing method concerned five basic mechanism which are image acquisition image preprocessing image segmentation image post processing and image analysis The most serious step in image processing is the segmentation of the picture In this work the analysis is on some of the common segmentation technique that have found request in classification in biomedical image processing particularly in blood cell image processing The information that the segmented image should hold maximum useful information and remove unwanted information makes the entire procedure critical newline newline