45 24-bit 3264*2448 microscopic images taken from bone marrow samples including leshman bodies.
Visceral leishmaniasis is a parasitic disease that affects liver, spleen and the bone marrow. According to World Health Organization report, definitive diagnosis is possible just by direct observation of leishman body in the microscopic image taken from bone marrow samples.
In this study, leishman bodies existed in microscopic images taken from bone marrow samples of patients with visceral leishmaniasis underwent automatic-segmentation 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 and 45 data were captured for Automatic Boundary Extraction of Leishman Bodies.
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Please cite the following paper if you download the dataset:
M. Farahi, H. Rabbani, A. Mehri, “Automatic Boundary Extraction of Leishman Bodies in Bone Marrow Samples from Patients with Visceral Leishmaniasis”, Journal of Isfahan Medical School, vol. 32, no. 286, 3rd week, July 2014.
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-segmentation 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.
Keywords: Automatic disease diagnosis, Visceral leishmaniasis, Leishman body, Segmentation, Border detection