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Non vascular lesions

Non vascular lesions

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Non vascular lesions

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  1. Haemorrhages Non vascular lesions Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

  2. Non vascular lesions Hard exudates Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

  3. Non vascular lesions Cotton wool Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

  4. Non Vascular Lesions: Motivation • Diagnostic information from lesions: • type of retinopathy • severity Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

  5. Non Vascular Lesions: Aims • Identification of position of abnormal regions • Accurate outline of abnormal region boundary Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

  6. Illumination and pigmentation variability Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

  7. Variability correction Foracchia, Grisan, Ruggeri, Med Im An 3(9), 179-190, 2005 Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

  8. Supervised vs unsupervised • Lesions are locally different from the normal fundus • No knowledge about what are the differences • Infer the differences given a lesion is present Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

  9. Clustering and Markov Fields • Markov Fields • N classes • M features for each pixel • Learn the classes under the assumption of • gaussian statistics • spatial constraints • Clustering • N classes • M features for each pixel • Learn the classes under the assumption of • gaussian statistics Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

  10. Preliminaries • For each pixel x: • feature vector f(x) • classification label y(x) • The labels of a neignorhood of x • The distribution P(f | y)=Py for each class Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

  11. Markov Random Fields • The energy of the segmentation y of a group of pixels depends on their features and on the structure of the labelling Likelihood of the segmentation in the feature space Connectivity of the segmentation Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

  12. Selection of a region of interest Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

  13. Markov Field evolution: t=0 Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

  14. Markov Field evolution: t=1 Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

  15. Markov Field evolution: t=2 Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

  16. Markov Field evolution: t=3 Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

  17. Markov Field evolution: t=6 Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

  18. Markov Field evolution: t=10 Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

  19. Markov Field evolution: t=20 Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

  20. Markov Field evolution Manual Segmentation MRF Segmentation ROI (100x100 pixel) Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

  21. Markov Field evolution Manual Segmentation MRF Segmentation ROI (90x90 pixel) Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

  22. Results • Manually segmented images • 200 ROIs • centered on a lesion • 50x50 pixels • Evaluation of pixel classification Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

  23. Results: Hard Exudates Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

  24. Results: Hard Exudates Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

  25. Results: Foveal Micro Aneurysm Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

  26. Results: Foveal Micro Aneurysm Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)