This document proposes an automated system for detecting and quantifying drusen from optical coherence tomography (OCT) images of the retina. Drusen are early signs of age-related macular degeneration and their detection can help assess disease progression. The system first denoises and segments the retinal pigment epithelium layer from OCT images. It then removes the retinal nerve fiber layer and projects the image to detect drusen, which are refined and smoothed to eliminate false positives. Extracted drusen features can characterize disease status and progression. The goal is to develop an effective automated method for drusen segmentation to help doctors monitor AMD.

1. AUTOMATED DRUSEN DETECTION
AND QUANTIFICATION SYSTEM
By : Gehad Hassan
Fayoum University
Dept. of Computer Science, Faculty of Computers and information
Member of the Scientific Research Group in Egypt

2. Scientific Research Group in Egypt
www.egyptscience.net

3. Agenda

Introduction.

Problem Definition
Main Objectives

Drusen Imaging Ways




Retinal Layers of A cross-sectional SDOCT Image (B-scan).
Problems in Consideration
The Proposed Recommender System

4. Introduction


Drusen are tiny yellow or white deposits in a layer of the
retina called Bruchs membrane.
They are the most common early sign of dry age-related
macular degeneration(AMD).

5. Introduction Cont..

Drusen are made up of lipids,
a type of fatty protein. They
may be the result of a failure
of the eye to dispose of waste
products that are produced
when the photoreceptors of
the eye drop off older parts
of the cell.

6. Introduction Cont..

There are several types
of drusen with different
levels of risk.
 Drusen can be small,
hard and scattered far
apart from each other.
 Some drusen can
become larger, softer
and closer together.

7. Introduction Cont..


An estimated 8 million persons at least 55 years old in the United
States have monocular or binocular intermediate AMD or monocular
advanced AMD (In, 2003). One of its clinical characteristics and, in
most cases, the first clinical finding is the presence of drusen.
it is considered the injury of Drusen of the biggest causes of
blindness in the whole world, and the incidence of drusen constitutes
75% of the causes of blindness, while other diseases (blue water cloud the cornea - the network separation - bleeding eye, etc. ..)
make up 25% of the causes of blindness .

8. Drusen Imaging Ways
Drusen Imaging Ways
CFP(color fundus
photographs )
OCT(Optical coherence
tomography)

9. Problem Definition

Detection of drusen is very important issue in
medical field to avoid some problems that affects
central vision or it can be used for the assessment of
change in disease status - response to treatment or
progression . So the detection of drusen part and
the calculation of its characteristics may help
doctors to determine diseases status.

10. Main Objectives


Developing an effective automated drusen
segmentation System.
Extracting qualitative features from drusen which
are useful for evaluating the progress of these
lesions.

11. Problems in consideration
This pervious methods have some
limitation
RPE and IS OS have
the same reflectivity
Limitations in small
drusen segmentation

12. Retinal layers of a cross-sectional SDOCT image (B-scan)




RNFL: retinal nerve fiber layer
IS/OS: photoreceptor inner/outer
segments.
RPE: retinal pigment epithelium. The
location of a druse is indicated with a
yellow arrow.
The RNFL complex (is indicated by the
orange dotted region)

13. Retinal layers of a cross-sectional SDOCT image (B-scan) Cont..
Bigger
area and
size of
Drusen
More Risk
of AMD
more
effect on
vision

14. Input image
OCT image
1
Image
denoising
2
RPE
segmentation
3
RNFL Layer
removal
Drusen
category status
of disease
Output
The Proposed
Recommender
System
Features Extraction
4
5
Drusen
Drusen
projection
segmentation
8
Drusen
smoothing
7
6
Drusen
refinemnton
projection
image
Elimination of
false positive
drusen

15. For further questions:
Gehad Hassan
gehad_hassan_cs@yahoo.com