Ppt on malarial RBCs identification

Introduction

Digital Holographic Interferometric Microscope

Thickness Determination of RBC

Cell Identification

Future Scopes

Conclusion

Malaria is one of the most

widespread and potentially fatal

diseases especially in Africa and Asia

Clinical diagnosis of malaria is based on microscopic

inspection of blood smears by visual inspection of a technician

Much beneficial when automatically discriminable

easy-to-use devices are used instead of visual identification

Use of Interference techniques, Digital Holographic

Microscopy and Interferometric Comparision

Digital holographic microscopy (DHM) is an effective

tool for 3-D imaging of micro-objects

Object phase information is provided by Interferometric

Comparision of phases of the object as well as its

background from the recorded holograms

Digital Holographic Interferometric

Microscope

The location of cells in the field of

view is obtained from the

thickness profile

CELL IDENTIFICATION

Thresholding the thickness distribution by the resolution

of the system, location of cells can be automatically

determined

IDENTIFICATION USING

SINGLE RECONSTRUCTION PLANE

Cell identification using single plane

Top row shows phase-contrast images of four different healthy RBCs.

Bottom row depicts cross-sectional thickness profile along the center line.

Top row shows phase-contrast images of four different malaria-infected RBCs.

Bottom row depicts cross-sectional thickness profile.

Average correlation coefficient from shape comparison of different

cell pairs using data from a single reconstruction plane

(■ healthy, ▲ malaria infected, ------ threshold)

A threshold of 0.88 yielded the best discrimination probability

69% malaria infected cells could be correctly identified when

compared with that of healthy cells (TPR)

FPR is found to be 27%

IDENTIFICATION USING

MULTIPLE RECONSTRUCTION PLANES

Cell identification using multiple planes

Average shape correlation is found at different axial

planes to compute correlation coefficient

Phase-contrast images of a healthy RBC obtained at various axial distances.

Phase-contrast images of a malaria-infected RBC obtained at various axial distances.

Average correlation coefficient from shape comparison of different cell

pairs using data from 20 axial planes (■ healthy, ▲malaria infected, ---threshold)

Probability of correct classification is increased to 84%

with reduced FPR of 11%

Hence use of thickness information at multiple axial planes

will lead to a better probability of identification

ROC curves for the detection of malaria-infected RBCs

Refractive index of blood plasma and RBC could vary from

person to person

Hence a variation of upto 5% is introduced to refractive

index of RBC, plasma and malaria parasites

The probability of discrimination

was found to be 86% and 91% for

constant and correct RI values

respectively

Future of the work lies in using the technique to study other

diseases affecting RBCs

FUTURE SCOPES

Extraction of information along

the focus in a single shot will make

the method faster

A database of healthy and diseased

cells can be made, and a test cell can

be compared with this database to

determine its state of health

By using thickness profile from multiple axial planes,

the recognition performance can be improved

Integration of DHM and correlation algorithms acts as an

automated technique to discriminate different classes of RBCs

Comparison of the shape of the test cell with the database

of healthy and infected cells may indicate whether the cell

is healthy or not.

CONCLUSION

REFERENCES

www.ieeexplore.com

www.google.com

www.howstuffworks.com

www.wikipedia.org

Thank You