Disease Diagnosis Using RBCs & WBCs Cell Structure by Image Processing

Abstract

The human blood consists of the RBCs, WBCs, Platelets and Plasma. The complete blood count defines the state of health. Blood is a health indicator therefore segmentation and identification of blood cells is very important. Complete Blood Count (CBC) includes counting of all the cells which determines person’s health. The RBC and WBC count is very important to diagnose various diseases such as anemia, leukemia, tissue damage, etc. Old conventional method used in the hospital laboratories involves manual counting of blood cells using device called Hemocytometer and microscope. But this method extremely monotonous, laborious, time consuming, and leads to the inaccurate results due to human errors. Also there are some expensive machines like Analyzer, which are not affordable by every laboratories. The objective of this paper is to produce a survey on an image processing based system that can automatically detect and count the number of RBCs and WBCs in the blood sample image. Image Acquisition, Pre-Processing, Image Enhancement, Image Segmentation, Image Post-Processing and Counting algorithm these are six steps involved in an image processing algorithm. The objective of this research is to diagnosed the different diseases of the blood cells.