Its about automatic identification of malaria-infected RBCs using correlation algorithm

2.  Introduction
 Digital Holographic Interferometric Microscope
 Thickness Determination of RBC
 Cell Identification
 Future Scopes
 Conclusion

3.  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

4.  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

5. Digital Holographic Interferometric
Microscope

8.  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

10. IDENTIFICATION USING
SINGLE RECONSTRUCTION PLANE
Cell identification using single plane

11. Top row shows phase-contrast images of four different healthy RBCs.
Bottom row depicts cross-sectional thickness profile along the center line.

12. Top row shows phase-contrast images of four different malaria-infected RBC
Bottom row depicts cross-sectional thickness profile.

13. Average correlation coefficient from shape comparison of different
cell pairs using data from a single reconstruction plane
(■ healthy, ▲ malaria infected, ------ threshold)

14.  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%

15. IDENTIFICATION USING
MULTIPLE RECONSTRUCTION PLANES
Cell identification using multiple planes
 Average shape correlation is found at different axial
planes to compute correlation coefficient

16. 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.

17. Average correlation coefficient from shape comparison of different cell
pairs using data from 20 axial planes (■ healthy, ▲malaria infected, ---threshold)

18.  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

19.  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

20.  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

21.  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

22. REFERENCES
 www.ieeexplore.com
 www.google.com
 www.howstuffworks.com
 www.wikipedia.org

23. Thank You