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.


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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


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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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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


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Block Diagram

Pre

Processing

Feature

ExtractionImage

Pre

Processing

Feature

ExtractionImage

Minimum

Euclidean

distance

calculation

Classification

Output

Known Samples

Test Sample


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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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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.


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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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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


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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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A t t d D t ti d Cl ifi ti f P iti d N P iti Di


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Documents

Automated Detection and Classification of Parasitic and Non-Parasitic Diseases