American Journal of Applied Sciences 9 (5): 615-619, 2012 ISSN 1546-9239 © 2012 Science Publications 1 Corresponding Author: Magudeeswaran, V., Department of Electronics and Communication, PSNA College of Engg and Tech, Dindigul 615 Feature Extraction and Classification of Blood Cells Using Artificial Neural Network Magudeeswaran Veluchamy, Karthikeyan Perumal and Thirumurugan Ponuchamy Department of Electronics and Communication Enginering, PSNA College of Engineering and Technology, Dindigul, Tamil nadu, India Abstract: 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. Key words: Image segmentation, morphological operations, feature extraction, image classification, artificial neural network INTRODUCTION One method of evaluating clinical status is the counting of cell types based on statistical and moment invariant analysis. Cells are classified as normal and abnormal (Wheeless et al., 1994). Quantitative image analysis appears to offer sensitive and reproducible classification of blood cells. Quantitative digital image analysis has been previously applied to the study and classification of red blood cells (Bacus and Weens, 1977; Bacus, 1984; Gonzalez and Woods, 2009; Hasani et al., 2006). In a series of publications, Bacus and colleagues documented the utility of the technique in classifying erythrocytes from patients with various hematologic disorders. Westerman and Bacus classified cells in blood from sickle patients into 14 different classes based on six features: size (area), hemoglobin content (integrated optical density), central pallor, circularity, elongation and spicularity. This study reports the statistical and moment invariant features in the classification of cells from blood as normal and abnormal. This approach can be divided into several well-defined stages, presented in Fig. 1. Fig. 1: General structure of method After image collection, the image is first segmented in order to isolate the interesting parts and remove noise and undesired components. Next, the feature extraction process is applied, to extract the useful information from the segmented blood cells and