RBC Classification in Blood Smear Image using Neural Network

Abstract

Biomedical image processing becomes an emerging field due to automation in the field of medical science with the help of image processing techniques. In medical science it is very much essential to diagnosis a disease accurately and efficiently. Most of the disease which deals with the blood test report for diagnosis of the disease. This paper proposed a computer vision based method which extract the Red Blood Cells (RBC) from a blood smear image and classify it whether normal or abnormal. Then it will count the normal RBC as well as abnormal RBC. This method works in two parts, one is segmentation of blood cell and other is classification and counting of segmented blood cells using neural network. The Neural network trained and classified using shape and moment invariant features because this features are invariant to translation, scaling and rotation. The proposed method performs well and gives about 90 percent of correct result.