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7/30/2019 Automated Detection and Classification of Parasitic and Non-Parasitic Diseases
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Automated Detection and Classification of Parasitic and Non-Parasitic Diseases
Department of Biomedical Engineering Rajalakshmi Engineering College, Chennai - INDIA
Automated Detection and Classification
of Parasitic and Non-Parasitic Diseases
D.Kanchana (21108121027), Nalini.A.Krishnan (21108121034)
Guide: Mrs. A. N. Nithyaa,
Senior Lecturer,Rajalakshmi Engineering College.
7/30/2019 Automated Detection and Classification of Parasitic and Non-Parasitic Diseases
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Automated Detection and Classification of Parasitic and Non-Parasitic Diseases
Department of Biomedical Engineering Rajalakshmi Engineering College, Chennai - INDIA
Aim and Objective
Aim
To automate the classification of Parasitic and Non-Parasitic diseases usingDigital Image processing techniques.
Motivation
Visual inspection of microscopic images - labor-intensive, repetitive and time
consuming task.
Non-existence of automated technique in life science laboratories.
Automation - important for medical diagnostics, planning, and treatment.
Objective
Feature extraction of known samples
Master feature set creation ( Mean, Variance, Moments, Entropy) of knownsamples
Feature set creation of test sample
To classify the disease based on the minimum Euclidean distance calculation
7/30/2019 Automated Detection and Classification of Parasitic and Non-Parasitic Diseases
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Automated Detection and Classification of Parasitic and Non-Parasitic Diseases
Department of Biomedical Engineering Rajalakshmi Engineering College, Chennai - INDIA
Data
Normal Blood Smear Elephantiasis Blood Smear Malaria Blood Smear
Trypanosomiasis Blood Smear Polycythemia Blood Smear Sickle Cell Anaemia Blood Smear
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Automated Detection and Classification of Parasitic and Non-Parasitic Diseases
Department of Biomedical Engineering Rajalakshmi Engineering College, Chennai - INDIA
Materials & Methods
Image Acquisition System
Materials
Software
MATLAB 7.5.0(R2009)
Microscopic Image samples
Known samples - 20 Test samples - 20
Methods
Reading pixel values
Size normalisation Grey level conversion
Feature extraction
Minimum Euclidean distance
calculation
7/30/2019 Automated Detection and Classification of Parasitic and Non-Parasitic Diseases
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Automated Detection and Classification of Parasitic and Non-Parasitic Diseases
Department of Biomedical Engineering Rajalakshmi Engineering College, Chennai - INDIA
Block Diagram
Pre
Processing
Feature
ExtractionImage
Pre
Processing
Feature
ExtractionImage
Minimum
Euclidean
distance
calculation
Classification
Output
Known Samples
Test Sample
7/30/2019 Automated Detection and Classification of Parasitic and Non-Parasitic Diseases
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Automated Detection and Classification of Parasitic and Non-Parasitic Diseases
Department of Biomedical Engineering Rajalakshmi Engineering College, Chennai - INDIA
Algorithm Methodology- Feature Extraction
Read the input microscopic blood
smear image.
Convert the image to a Grayscale
image.
Resize the input image.
Convert the image type to double datatype.
Extract Mean, Variance, 3rd order & 4th
order Moment, Entropy of the image
and store it in an array.
Average the feature values and form a
Master feature set for Normal,
Elephantiasis, Trypanosomiasis,
Malaria, Polycythemia and Sickle Cell
Anaemia.
Gray level conversion
Image resizing
Converting to double
Mean Calculation
Variance Calculation
Moment Calculation
Master feature set
creation
Image
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Automated Detection and Classification of Parasitic and Non-Parasitic Diseases
Department of Biomedical Engineering Rajalakshmi Engineering College, Chennai - INDIA
Gray level conversion
Image resizing
Converting to double
Mean Calculation
Variance Calculation
Moment Calculation
Master feature set creation
Image
Minimum Euclidean distance
calculation between the test
sample and the master feature set
Classification
Algorithm Methodology- Classification
Read the input microscopic blood
smear image that has to be tested. Convert the image to a Grayscale
image and resize it.
Convert the image to double data type.
Extract Mean, Variance, 3rd order & 4th
order Moment, Entropy of the test
image and store it in an array.
Calculate Minimum Euclidean distance
of the feature set of the test sample
with various Master feature set for
Normal, Elephantiasis, Malaria,
Trypanosomiasis, Polycythemia and
Sickle Cell Anaemia and store it in anarray.
Find the minimum value of the array.
Based on the minimum value, the
blood smear image is classified.
7/30/2019 Automated Detection and Classification of Parasitic and Non-Parasitic Diseases
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Automated Detection and Classification of Parasitic and Non-Parasitic Diseases
Department of Biomedical Engineering Rajalakshmi Engineering College, Chennai - INDIA
Quantitative Results
Sample Mean Variance Normalized
variance
Moment
3rd order
Moment
4th order
Entropy Normalized
entropy
Normal 1 0.8487 836.1780 0.2159 -0.0008 0.0002 4.8937 0.8088
Normal 2 0.8491 811.4493 0.2095 -0.0008 0.0002 4.9385 0.8163
Normal 3 0.8494 811.4828 0.2095 -0.0008 0.0002 4.7959 0.7927
Normal 4 0.8537 803.0929 0.2074 -0.0009 0.0002 4.7941 0.7924
Master
feature set
0.8502 815.5507 0.2106 -0.0008 0.0002 4.8556 0.8026
Elephant 1 0.7596 523.3367 0.1351 -0.0012 0.0004 5.7054 0.9430
Elephant 2 0.7586 538.0814 0.1389 -0.0013 0.0005 5.7038 0.9427
Master
feature set
0.7591 530.7091 0.1370 -0.0013 0.0005 5.7046 0.9429
Feature Value Extraction
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Automated Detection and Classification of Parasitic and Non-Parasitic Diseases
Department of Biomedical Engineering Rajalakshmi Engineering College, Chennai - INDIA
Cont.Sample With
normalblood
smear
With
elephantiasisblood smear
With
malariablood
smear
With
trypanosomiasisblood smear
With
polycythemiablood smear
With sickle
cell anaemiablood smear
Output
Unknown
2
0.0222 0.0128 0.0515 0.0348 0.0578 0.8455 elephantiasis
Unknown
9
0.4481 0.5032 0.5340 0.4851 0.3286 0.0365 sickle cell
anaemia
Unknown10
0.0094 0.0150 0.0320 0.0164 0.0460 0.7792 normal
Unknown
12
0.0096 0.0392 0.0192 0.00097 0.0428 0.6977 trypanosomasis
Unknown
13
0.0651 0.1813 0.0509 0.0791 0.1530 0.4556 malaria
Unknown
15
0.8243 0.8606 0.9032 0.8619 0.7380 0.2173 not available
Unknown
16
0.1772 0.1436 0.2788 0.2265 0.0820 0.3235 polycythemia
Minimum Euclidean Distance calculation
7/30/2019 Automated Detection and Classification of Parasitic and Non-Parasitic Diseases
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Automated Detection and Classification of Parasitic and Non-Parasitic Diseases
Department of Biomedical Engineering Rajalakshmi Engineering College, Chennai - INDIA
ConclusionsAdvantages
Gives quantitative results for accurate diagnosis
Minimal human intervention.
More accurate results
Synchronization of the results with interpretation from human experts.
More advantageous than manual inspection
Better and effective
Less time consuming and less laborious
Conclusions
Aimed at hospitals in rural areas where there is a crisis for skilled Pathologists and
Lab technicians.
Thus our project can be a valuable asset in the life science laboratories.
Future scope In future, our project can be further extended for the detection and classification of
other blood cell disorders. It can be also used for Automated Detection of cancer cells
and tumors.
A d D i d Cl ifi i f P i i d N P i i Di
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Automated Detection and Classification of Parasitic and Non-Parasitic Diseases
Department of Biomedical Engineering Rajalakshmi Engineering College, Chennai - INDIA
Some of the References[1] John Frean, Microscopic determination of malaria parasite load: role of image analysis 862:866, 2010.
[2] Poomcokrak. J and Neatpisarnvanit. C, Red Blood Cells Extraction and Counting 199:203, 2008.
[3] Hirimutugoda Y M and Dr. Gamini Wijayarathna Image Analysis System for Detection of Red Cell Disorders
Using Artificial Neural Networks, Sri Lanka Journal of Bio-Medical Informatics 2010; 1(1):35-42
[4] Haralick RM, Shanmugan K, Dinstein I: Textural features for image classification, IEEE Trans Syst Man Cyber
Vol SMC 3610-621, 1973.
[5] Neetu Ahirwar, Sapnojit. Pattnaik, and Bibhudendra Acharya, Advanced image analysis Based System forAutomatic Detection and Classification of Malaria Parasite in Blood Images, International Journal of Information
Technology and Knowledge Management, January-June 2012, Volume 5, No. 1, pp. 59-64
[6] Prof. Samir K. Bandyopadhyay and Sudipta Roy, Detection of Sharp Contour Of the element of the WBC and
Segmentation of two leading elements like Nucleus And Cytoplasm International Journal of Engineering
Research and Applications
(IJERA) ISSN: 2248-9622, Vol. 2, Issue 1, Jan-Feb 2012, pp.545-551
[7] S. S. Savkare and S. P. Narote, Automatic Detection of Malaria Parasites for Estimating Parasitemia,
International Journal of Computer Science and Security (IJCSS), Volume (5) : Issue (3) : 2011.
[8] Jigyasha Soni, Advanced Image Analysis based system for Automatic Detection of Malaria Parasite in Blood
Images Using SUSAN Approach, International Journal of Engineering Science and Technology (IJEST), ISSN:
0975-5462 Vol. 3 No. 6 June 2011.
[9] Horiuchi K, Ohata J, Hirano Y, Asakura T: Morphologic studies of sickle erythrocytes by image analysis. J Lab
Clin Med 115: 613-620, 1990.
[10] P.S.Hiremath, Parashuram Bannigidad and Sai Geeta, Automated Identification and Classification of White
Blood Cells (Leukocytes) in Digital Microscopic Images, IJCA Special Issue on Recent Trends in Image
Processing and Pattern Recognition RTIPPR, 2010.
[11] Bacus JW, Weens JH: An automated method of differential red blood cell classification with application to the
diagnostics of anemia. J Histochem Cytochem 25514-632, 1977.
A t t d D t ti d Cl ifi ti f P iti d N P iti Di
7/30/2019 Automated Detection and Classification of Parasitic and Non-Parasitic Diseases
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Automated Detection and Classification of Parasitic and Non-Parasitic Diseases
Department of Biomedical Engineering Rajalakshmi Engineering College, Chennai - INDIA