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.
Highlights
One method of evaluating clinical status is the counting of cell types based on statistical and moment invariant analysis
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)
Image Data Collection: The blood specimens were obtained from different patients with sickle cell anemia, sickle cell disease and normal volunteers
Summary
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). Westerman and Bacus classified cells in blood from sickle patients into 14 different classes based on six features: size (area), hemoglobin content. In this work MBPN is used to identify the given class of input image as normal and abnormal. A number of hidden units, learning rates, momentum and Mean Square Error (MSE) are investigated to achieve the best results
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