Improving patient care along with quality service and at the same time cutting costs might seem totally opposite goals, but that is exactly the promise of telemedicine. The term telemedicine covers an ever increasing number of techniques that put patients in touch with medical expertise over great distances. Techlead Software Engineering Pvt. Ltd. India is coming up with one stop solution for ophthalmologist, the Digitech Ophthalmologist. Digitech Ophthalmologist Digital ophthalmologist is a software tool developed by Techlead software Engineering. It has been developed with understanding of the ophthalmologist’s concerns. This smart assistant to doctors will perform many of their routine and time consuming tasks in a much efficient way. Panorama ConstructionCDR AnalysisGlaucoma DetectionBlood Vessel Analysis3D Retina ReconstructionRetinopathy

1. Digitech
Ophthalmologist
Product Overview

2. Digitech Ophthalmologist
What is Digitech Ophthalmologist ?
Software tool for Ophthalmologist
Its like a smart assistance who will carry out the routine tasks with
ease freeing more time for the important work.
Advantages
This software operates on the images taken by Fundus Camera
Leaves more time for the doctor to actually focus on the important
diagnostic part rather than menial work.
Presents information and statistics in such a way to help doctor take
the best decision
Improves patient care at reduced prices as telemedicine is possible
due to this innovation

3. Digitech Ophthalmologist
Features: The major features are illustrated as below.
Patient Database Management
Distortion Correction
Panorama Image Construction
Cup to Disk Ration (CDR ) Analysis
Retina 3D Reconstruction
Blood Vessel Analysis
Glaucoma Detection
Retinopathy
Each Feature is discussed at a greater length in the following slides.

4. Digitech Ophthalmologist Features

5. Patient Database Management
Archiving and maintaining the medical data is one of the
most important tasks
A tool for sophisticated archival and easy retrieval of the
data is necessary when dealing with the large databases
Techlead Patient Database system avails doctors the
following facilities
Store the database per patient basis for keeping track of history
Access the database for a particular clinical manifestation
Allow Multiple doctors to comment on the single case and all visible
to the user
A portable format of database which can be shared among doctors
with simpler means such as cell phones
Simple User Interface

6. Patient Database Management Cntd…

7. Patient Database Management Cntd…

8. Distortion Correction
Images are distorted due to the deviation of the light from direct
Path. The distortion is induced by
EYE Lens
Camera Lens
Figure shows example
of Camera Lens Distortion
Distortion Correction
Achieved by applying Polynomial Correction on the input image
Correction is applied on radial axis with the coefficients based on
camera lens
Complete removal is impossible as eye lens characteristics of
individual are not known

9. Panorama Construction
Complete retina surface is segmented in 7-9 virtual
overlapping parts and individually photographed
Retina is photographed in such a way to compensate the
distortion caused by the Eye lens and the curvature of
retina.
Figure shows the method of photography of retina

10. Panorama Construction Cntd..
Panorama Construction Features
Image Distortion is not 100% correctable
making the alignment difficult
Manually Stitching Images is a tedious Job
Panorama construction utility performs this
operation in no time
Testing
Tested on more than 100 datasets
Stitching time is < 40 seconds for 7
images (Each Image 9MPixel)
Time varies depending on the features in
a image.
Panorama Image is constructed from the 7
images 6 out of which are shown in the
above slide taken at different positions (as
shown with purple circles)

11. CDR Analysis
What is CDR ?
It’s the ratio between surface area occupied by Optic Cup to Surface area
occupied by Optic Disc.
What’s its Clinical Significance
When Cup surface area increases, CDR increases.
This is indication of variation of intra ocular pressure and sometimes
can indicate progress of glaucoma
Current Method
Manual marking – Ambiguous
Optic Cup Contour
Optic Disc Contour

12. CDR Analysis Cntd…
Accurate Extraction of the contours will result in the accurate
prediction of clinical manifestation
How to increase the accuracy of detected contours
Active Contours or SNAKE algorithm
Like the snake , when put near the object, it fits the object contour with
minimum error distance i.e. maximally touches the actual boundary
Example Video of Snake
Operation

13. CDR Analysis Reports
CDR Analysis is used to monitor the intra ocular and
dilation of Cup surface area
CDR is measured from the centre of the optic disk and
traversing axially at a particular angle towards the disc
contour. The distance where it cuts cup contour is marked as
CUP while the distance at which it cuts the disc contour is
marked as disc.
Centre point
Cup Intercept
Distance @ 0
Degree
This procedure is repeated for
0 -360 degrees and is plot
Angle 0 Degree
as a graph
Disc Intercept
Distance @ 0
Degree

14. CDR Analysis Reports
Figure shows the graph of CDR plot for two different dates for chronological studies

15. Blood Vessel Analysis
Tracing the blood vessels accurately is the backbone of the
entire application
Blood vessels are used as the markers of information for panorama
Image construction
AV Ratio gives vital information about Arterial narrowing and hence
the systemic hypertension as well as Diabetics and optic atrophy
ALR or arterial reflex is a measure of thinning of the artery walls
which in turn alarm about arteriosclerosis and atherosclerosis
AV Ratio, Artery width to Vain Width

16. Development of the Blood Vessel Analysis Algorithm
• Development of a robust algorithm for vessel tracing (Vessel
Strokes algorithm )
• Development of Adaptive Vessel tracing algorithm
(Accurate boundary detection for exact calculation of AV
ratio and ALR are essential for Clinical manifestation)
• Vessel classification and Tree formation (with some
domain knowledge and fine tuning)
• Fine tuning of the algorithm is required so that the best
results are obtained for most of the images
– The most important challenge still remains is to identify the
algorithm which works for almost all the input images. Even the best
algorithm needs to be fine tuned for the performance on a given
dataset.

17. Robust Algorithm Development
• Many algorithms were tried and tested for reliable extraction of the
blood vessels from background in variety of images
• Vessel Stroke algorithm performed the best in all kinds of input image
conditions
• Following figure shows example of vessel extraction

18. Adaptive Vessel Tracing Algorithm
Adaptive Vessel Tracing Algorithm
Drawing the centre line of the vessel
Drawing the boundaries of the vessel
Identifying the branch points
Identifying the crossover points
Identifying the end of the vessel
Accurately adjusting the boundaries as per local statistics
Identify and Join the broken vessels without addition of errors
Identify Blood vessel type: Artery / Vein
Build the Vessel Tree Data structure
For each of the above parameters, the algorithm has been tested for
more than 300 images at a time. This dataset consists of input
images of various kinds.

19. Blood Vessel tracing Utility
The blood vessel tracing utility allows user to calculate the
following
Traced Vessels
Vessel Classification- Artery and Veins Tree
Vessel Tree – Connected tree starts from Optic Disc
AV ratio at the selected region
ALR Ratio at the selected region
Following figure shows the snapshot of the utility

20. Vessel Analysis Utility

21. 3D Retina Reconstruction
3D analysis is a powerful tool for in biomedical applications
and already popular in radiology
Retina Can be viewed as a 2D image as well as a 3D
structure
3D visualization is possible if the stereo view of the Retina
is available
Once the stereo imaging data is in hand getting the 3D
output is a two step job
Generation of 3D data from 2D Images using stereo algorithms
Visualizing the 3D data with some viewer

22. Generation of 3D Data from 2D images
Stereo imaging takes 2 images with a lateral shift, preferably from two
different cameras.
The two images are processed to identify the disparity between the
matching structures to calculate the depth of the feature.
Figure below shows one such example

23. Features of Techlead Stereo Algorithm
• Robust algorithm even works for images taken by single
camera. Ideal if the input is provided from a standard stereo
imaging setup with 2 cameras
• Automatic Y shift correction
• Automatic Disparity range calculation
• Automatic Cropping of the Area of Interest
• Automatic correction of rotation
• Auto Identification of left and right images
• Facility to fine tune parameters (defaults are stored)
• View the output in a interactive 3D viewer

24. Snapshot of the 3D Viewer

25. Glaucoma Detection
What is Glaucoma?
Glaucoma is a disease which can result in loss of vision.
It’s early detection and treatment is of prime importance.
Physiological Appearance:
Fan-like pattern
near Optic Cup
Extended Optic
Cup

26. Glaucoma Detection (Contd..)
Evaluation of Nerve Fiber layer(NFL) is essential for early
detection of Glaucoma.
Physiological Appearance of NFL:
Striated Parabolic Arcs
Methods of Glaucoma Detection
Median Radial Transform
Fourier Series Descriptors
Hough Transform

27. Glaucoma Detection Results
Glaucoma Detection
NFL Detection

28. Retinopathy
Retinopathy refers to a group of diseases which can cause
severe vision loss or blindness.
This retinal damage can be a result of ruptured blood
vessels, swelling in optic nerve etc.
Digital Retinopathy identifies these symptoms by the
abnormal coloring as shown below:

29. Retinopathy (Contd..)
Retinopathy
Algorithm
Detected Abnormal
Patch

30. Retinopathy : Screening
Screening
If the input image is
normal or abnormal
User can train the
software with
Selecting and deleting the
reference Images.
Accepting / rejecting the
outcome of the software
Adjusting thresholds
Internal Algorithm will
tune itself to give best
results

31. Retinopathy: Training
Salient Features:
Training – the doctor can train this software to classify what are
normal and abnormal images.
Input
Algorithm
Output
Training
Feedback

32. Retinopathy (Contd..)
Salient Features:
Intelligent Disease Locator
Uses biological domain knowledge to extract the diseased areas
Train the algorithm to separate a particular pattern of data
Use this knowledge for future reference with systematic classification

33. Retinopathy (Contd..)
Retinopathy Disease Tracker Tool:
Analyze large data
View Patient History
Patient’s Response
Graphical representation

34. Retinopathy (Contd..)
Retinopathy Disease Tracker Tool(Contd..)
Shows statistics along with data sets.
Finds similar patient history.
Software will present analysis and hypothesis to doctors to be
accepted or rejected.
Content based image retrieval used to identify similar cases.
Doctor can share the data of a particular patient case with another in
a standard format like DICOM including all the analysis and expert
comments.

35. Retinopathy (Contd..)
Multiple Examinations
When one patient comes more than one time
The current analysis is checked with history and the results are
shown under multiple examinations
Newly identified area
Marked with Blue Circle

36. Thank You