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FINAL PROJECT REPORT
  Automatic Classification of Different Geometries of
  Drusen in patients with Age related Macular
  Degeneration
  -Darshan Shah
PROJECT OVERVIEW
   What is the project about?
PROJECT OVERVIEW
   GOAL
       The goal of this project is to create a GUI to
        automatically segment Drusen and categorize the
        Drusen into its various types




                                             Image courtesy harbour.com
PROJECT OVERVIEW
   SCOPE
       This project will help the eye examiners to find Drusen in the
        image and help save time in categorizing Drusen thereby
        speedingHomogeneity (Comparing area between
                  up the examination of the retina.
                 RPE and Choroid) Homogeneity
                 (Comparing area between RPE and
                 Choroid)




                                                    Image courtesy ophmanagement.com
LITERATURE REVIEW
              KEY PAPER
Drusen Ultrastructure Imaging with Spectral Domain Optical
Coherence Tomography in Age-related Macular Degeneration -
Khanifar Aziz et al 2008
Quantitative Comparison of Drusen Segmented on SD-OCT versus
Drusen delineated on Color Fundus Photographs- Nieraj Jain et al
2010
Automatic segmentation of seven retinal layers in SDOCT images
congruent with expert manual segmentation - Stephanie J. Chiu
2010
HOMOGENEITY (COMPARING AREA
BETWEEN RPE AND CHOROID)

    Homogeneous – compare the entire layer for uniformity
    Non-homogeneous with a central core – varying
     internal reflectivity with a distinct single focus of hyper-
     reflectivity
    Non-homogeneous without a central core– varying
     internal reflectivity
ORIGINAL LAYERED IMAGE
REGION OF INTEREST
SEGMENTED REGION
ATTEMPTED METHOD 1

   Taking max values along the vertical gradients
ATTEMPTED METHOD 2

   Convolving with a binary mask depending on
    weights
METHOD 3

   GRAPH THEORY
     Implementation   of Stephanie Chiu’s algorithm


   Using Dijkstra Algorithm to implement
    lowest weighted path
SHIFTED AND SEGMENTED
AUTOMATIC INITIALIZATION
SO GETTING BACK TO SEGMENTED
DATA SET OF UNSEGMENTED IMAGES
                                          mean before              Std before
Image no        Separate layer exists?    segmentaion              segmentation
           51   yes                                     162.0504                  35.2787
           52   yes                                     156.1071                  39.2795
           53   yes                                     164.7804                  38.8632
           54   yes                                     162.8016                  37.2076
           55   yes                                     163.5681                  35.8284
           56   yes                                     172.4292                  39.5759
           57   yes but hardly                          182.4318                  37.7275
           58   yes but hardly                          185.2433                  31.3095
           59   yes but hardly                          178.0332                  33.8844
           60   yes but hardly                          167.4353                  34.3635
           61   not really                              179.9425                  35.9614
           62   really bright                           177.7636                  34.5737
           63   not really                              181.2146                  36.3007
           64   separable diff with RPE                 172.3632                  31.1072
           65   not really                              179.5103                  31.5538
           66   no                                      183.3249                  32.8943
           67   not really                               179.101                  34.1617
           68   no                                      184.9984                  31.9882
           69   no                                      184.9699                  34.9764
           70   no but some pecularity                  183.9566                  34.4765
BASED ON THIS ANALYSIS



    A cut-off value for applying the
segmentation was decided

     If the values was above the cut off it was
not segmented and the software proceeds
with classification directly

RUN CODE
RESOURCES

                    Literature
                    Paper of
                    Khanifar



                                       Reference
Matlab
                                       GUI code

                    Project




           GUI                     Web
         handbook                resource
RFERENCES
   Toth CA, Farsiu S, Khanifar AA, Chong G, Application of Spectral Domain OCT in AMD,
    in Optical Coherence Tomography in Age-Related Macular Degeneration, Gabriel
    Coscas (Editor), (Springer Medizin Verlag Heidelberg, 2009), pp. 15-34:
   S.J. Chiu, X.T. Li, P. Nicholas, C.A. Toth, J.A. Izatt, S. Farsiu. “Automatic Segmentation
    of Seven Retinal Layers in SDOCT Images Congruent with Expert Manual
    Segmentation.” Optics Express. Vol. 18, No. 18, pp. 19413-19428, August 2010.
   N. Jain, A. A. Khanifar, S. Farsiu, S. Bearelly, S. J. Chiu, K. P. Winter, J. A. Izatt, and C.
    A. Toth, Qualitative Comparison of Segmented Drusen on SD OCT versus Drusen
    Delineated on Color Fundus Photograph Presented at the Association for Research in
    Vision and Ophthalmology (ARVO) Annual Meeting, Fort Lauderdale, FL. May 2009.
   Dijktra code by Joseph Kirk-matlab mathworks
   Adjancey matrix code by Kevin Murphy - matlab mathworks
QUESTIONS?
APPENDIX
LAYERS OF THE RETINA-1




                         Image courtesy pcli.com
LAYERS OF THE RETINA-2




                         Image courtesy retinal physician
PRL

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Automatic Classification of Drusen Geometries in AMD Patients

  • 1. FINAL PROJECT REPORT Automatic Classification of Different Geometries of Drusen in patients with Age related Macular Degeneration -Darshan Shah
  • 2. PROJECT OVERVIEW  What is the project about?
  • 3. PROJECT OVERVIEW  GOAL  The goal of this project is to create a GUI to automatically segment Drusen and categorize the Drusen into its various types Image courtesy harbour.com
  • 4. PROJECT OVERVIEW  SCOPE  This project will help the eye examiners to find Drusen in the image and help save time in categorizing Drusen thereby speedingHomogeneity (Comparing area between up the examination of the retina. RPE and Choroid) Homogeneity (Comparing area between RPE and Choroid) Image courtesy ophmanagement.com
  • 5. LITERATURE REVIEW KEY PAPER Drusen Ultrastructure Imaging with Spectral Domain Optical Coherence Tomography in Age-related Macular Degeneration - Khanifar Aziz et al 2008 Quantitative Comparison of Drusen Segmented on SD-OCT versus Drusen delineated on Color Fundus Photographs- Nieraj Jain et al 2010 Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation - Stephanie J. Chiu 2010
  • 6.
  • 7. HOMOGENEITY (COMPARING AREA BETWEEN RPE AND CHOROID)  Homogeneous – compare the entire layer for uniformity  Non-homogeneous with a central core – varying internal reflectivity with a distinct single focus of hyper- reflectivity  Non-homogeneous without a central core– varying internal reflectivity
  • 11. ATTEMPTED METHOD 1  Taking max values along the vertical gradients
  • 12. ATTEMPTED METHOD 2  Convolving with a binary mask depending on weights
  • 13. METHOD 3  GRAPH THEORY  Implementation of Stephanie Chiu’s algorithm  Using Dijkstra Algorithm to implement lowest weighted path
  • 16. SO GETTING BACK TO SEGMENTED
  • 17. DATA SET OF UNSEGMENTED IMAGES mean before Std before Image no Separate layer exists? segmentaion segmentation 51 yes 162.0504 35.2787 52 yes 156.1071 39.2795 53 yes 164.7804 38.8632 54 yes 162.8016 37.2076 55 yes 163.5681 35.8284 56 yes 172.4292 39.5759 57 yes but hardly 182.4318 37.7275 58 yes but hardly 185.2433 31.3095 59 yes but hardly 178.0332 33.8844 60 yes but hardly 167.4353 34.3635 61 not really 179.9425 35.9614 62 really bright 177.7636 34.5737 63 not really 181.2146 36.3007 64 separable diff with RPE 172.3632 31.1072 65 not really 179.5103 31.5538 66 no 183.3249 32.8943 67 not really 179.101 34.1617 68 no 184.9984 31.9882 69 no 184.9699 34.9764 70 no but some pecularity 183.9566 34.4765
  • 18. BASED ON THIS ANALYSIS A cut-off value for applying the segmentation was decided If the values was above the cut off it was not segmented and the software proceeds with classification directly RUN CODE
  • 19. RESOURCES Literature Paper of Khanifar Reference Matlab GUI code Project GUI Web handbook resource
  • 20. RFERENCES  Toth CA, Farsiu S, Khanifar AA, Chong G, Application of Spectral Domain OCT in AMD, in Optical Coherence Tomography in Age-Related Macular Degeneration, Gabriel Coscas (Editor), (Springer Medizin Verlag Heidelberg, 2009), pp. 15-34:  S.J. Chiu, X.T. Li, P. Nicholas, C.A. Toth, J.A. Izatt, S. Farsiu. “Automatic Segmentation of Seven Retinal Layers in SDOCT Images Congruent with Expert Manual Segmentation.” Optics Express. Vol. 18, No. 18, pp. 19413-19428, August 2010.  N. Jain, A. A. Khanifar, S. Farsiu, S. Bearelly, S. J. Chiu, K. P. Winter, J. A. Izatt, and C. A. Toth, Qualitative Comparison of Segmented Drusen on SD OCT versus Drusen Delineated on Color Fundus Photograph Presented at the Association for Research in Vision and Ophthalmology (ARVO) Annual Meeting, Fort Lauderdale, FL. May 2009.  Dijktra code by Joseph Kirk-matlab mathworks  Adjancey matrix code by Kevin Murphy - matlab mathworks
  • 23. LAYERS OF THE RETINA-1 Image courtesy pcli.com
  • 24. LAYERS OF THE RETINA-2 Image courtesy retinal physician
  • 25. PRL

Editor's Notes

  1. This template can be used as a starter file to give updates for project milestones.SectionsRight-click on a slide to add sections. Sections can help to organize your slides or facilitate collaboration between multiple authors.NotesUse the Notes section for delivery notes or to provide additional details for the audience. View these notes in Presentation View during your presentation. Keep in mind the font size (important for accessibility, visibility, videotaping, and online production)Coordinated colors Pay particular attention to the graphs, charts, and text boxes.Consider that attendees will print in black and white or grayscale. Run a test print to make sure your colors work when printed in pure black and white and grayscale.Graphics, tables, and graphsKeep it simple: If possible, use consistent, non-distracting styles and colors.Label all graphs and tables.
  2. What is the project about?Define the goal of this projectIs it similar to projects in the past or is it a new effort?Define the scope of this projectIs it an independent project or is it related to other projects?* Note that this slide is not necessary for weekly status meetings
  3. What is the project about?Define the goal of this projectIs it similar to projects in the past or is it a new effort?Define the scope of this projectIs it an independent project or is it related to other projects?* Note that this slide is not necessary for weekly status meetings
  4. What is the project about?Define the goal of this projectIs it similar to projects in the past or is it a new effort?Define the scope of this projectIs it an independent project or is it related to other projects?* Note that this slide is not necessary for weekly status meetings
  5. What are the dependencies that affect the timeline, cost, and output of this project?
  6. Prepare slides for the appendix in the event that more details or supplemental slides are needed. The appendix is also useful if the presentation is distributed later. BudgetDesign documentsMarketing planSupplemental documentsContact information