Feature Extraction and Classification of Blood Cells Using Artificial Neural Network

Problem statement: One method of evaluating the clinical status is counting of cell types based on features that it contains. There is a need for a rapid, reproducible method, superior to human inspection and for the classification of cells. For solving these problems, quantitative digital-image analysis is applied and a novel method for classifications of affected blood cells from normal in an image of a microscopic section is presented. These blood cell images are acquired from different patient with sickle cell anemia, sickle cell disease and normal volunteers. Approach: The segmentation of blood cells is made by morphological operations such as thresholding, erosion and dilation to preserve shape and size characteristics. These features are extracted from segmented blood cells by estimating first, second order gray level statistics and algebraic moment invariants. In addition geometrical parameters are also computed. The analysis of extracted features is made to quantify their potential discrimination capability of blood cells as normal and abnormal. The results obtained prove that these features are highly significant and can be used for classification. In addition, we use back propagation neural network to classify the blood cells more efficiently. Results: For testing purposes, different sizes and various types of microscopic blood cell images were used and the classification efficiency is 80% and 66.6% for normal and abnormal respectively. Conclusion: The proposed system has a good experimental result and can be applied to build an aiding system for pathologist.