Document Type : Original Article (s)
Authors
1
MSc Student, Department of Bioelectrical Engineering, School of Advanced Medical Technology AND Student Research Committee, Isfahan University of Medical Sciences, Isfahan, Iran
2
Associate Professor, Department of Bioelectrical Engineering, School of Advanced Medical Technology, Isfahan University of Medical Sciences, Isfahan, Iran
3
Associate Professor, Department of Pathology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
Abstract
Background: According to the progress of microscopic imaging technology and suitable image processing techniques in the past decade, there is a tendency to use computer for automatic diagnosis of microscopic diseases. Automatic border detection is one of the most important steps in computer diagnosis that accuracy and specificity of the subsequent steps crucially depends on it. Microscopic images are colored to be seen more accurate and easier; after coloring, the image artifacts increases, so the boundary detection of objects is very important in order to find the exact feature extraction.Methods: In this study, leishman bodies existed in microscopic images taken from bone marrow samples of patients with visceral leishmaniasis underwent automatic-segmentatio using Otsu and Savoulla thresholding methods besides K-means clustering method. For data acquisition, a digital camera (Sony DSC-H9) coupled on an optical microscope (Olampus-CH40RF200) were used. Proposed method was tested on 20 images. For automatic diagnosis of the leishman bodies from all found objects, some geometric features like eccentricity, area ratio, roundness and solidity and some texture features like mean, variance, smoothness, third moment, uniformity and entropy were extracted. Found objects were classified into healthy and non-healthy groups using Feed-Forward Neural Network classifier.Findings: To find the best mode for each method, a comparison were made and determined that using stage 5 for Otsu, threshold 0.1 for Sauvola and 5 clusters for k-means had minimum automatic boundary extraction error.Conclusion: After compartment of obtained result with specialist, we found that Sauvolla method had minimum error of border detection, and Otsu method was more accurate for automatic detection of leishman bodies.
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