Blood is a connective tissue with cellular components like RBC, WBC and platelets. If the blood cells are beyond or below a standard range it leads to disorders and hence blood cell counting is of great importance in pathology. Blood cell counting techniques such as microscopic method gives better results, however it takes more time to count the number of blood cells. Electronic techniques like the Coulter counter and flow cytometry provide instant blood cell count but it's known to give counting errors. Blood cell count using digital image processing technique can be considered to automate microscopic method and minimize the time taken in conventional method.
In this project, a digital image processing technique is proposed to accurately count all the blood cells in a given slide with maximum accuracy. Edge base segmentation gave results with 66% accuracy and marked non RBCs as RBCs leading to counting error. A method involving morphological operator gave 92% accuracy, texture based classification method gave
89% accuracy and Circular Hough Transformation (CHT) along with morpholog
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