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Leishmania

DATASET OF LEISHMANIA PARASITE IN MICROSCOPIC IMAGES

 

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-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 and 45 data were captured for automatic Automatic Boundary Extraction of Leishman Bodies.

 

Data Set1      Data Set2       Data Set3       Data Set4       Data Set5

Data Set6      Data Set7       Data Set8       Data Set9     Data Set10

Data Set11    Data Set12     Data Set13      Data Set14   Data Set15

Data Set16    Data Set17     Data Set18      Data Set19   Data Set20

Data Set21   Data Set22     Data Set23      Data Set24   Data Set25

Data Set26   Data Set27     Data Set28      Data Set29   Data Set30

Data Set31   Data Set32     Data Set33      Data Set34   Data Set35

Data Set36   Data Set37     Data Set38      Data Set39   Data Set40

Data Set41   Data Set42      Data Set43     Data Set44   Data Set45

 

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-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.

 

Keywords: Automatic disease diagnosis, Visceral leishmaniasis, Leishman body, Segmentation, Border detection

 

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