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Haemorrhages. Non vascular lesions. Non vascular lesions. Hard exudates. Non vascular lesions. Cotton wool. Non Vascular Lesions: Motivation. Diagnostic information from lesions: type of retinopathy severity. Non Vascular Lesions: Aims. Identification of position of abnormal regions

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non vascular lesions

Haemorrhages

Non vascular lesions

Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

non vascular lesions1
Non vascular lesions

Hard exudates

Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

non vascular lesions2
Non vascular lesions

Cotton wool

Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

non vascular lesions motivation
Non Vascular Lesions: Motivation
  • Diagnostic information from lesions:
    • type of retinopathy
    • severity

Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

non vascular lesions aims
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)

illumination and pigmentation variability
Illumination and pigmentation variability

Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

variability correction
Variability correction

Foracchia, Grisan, Ruggeri, Med Im An 3(9), 179-190, 2005

Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

supervised vs unsupervised
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)

clustering and markov fields
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)

preliminaries
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)

markov random fields
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)

selection of a region of interest
Selection of a region of interest

Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

markov field evolution t 0
Markov Field evolution: t=0

Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

markov field evolution t 1
Markov Field evolution: t=1

Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

markov field evolution t 2
Markov Field evolution: t=2

Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

markov field evolution t 3
Markov Field evolution: t=3

Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

markov field evolution t 6
Markov Field evolution: t=6

Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

markov field evolution t 10
Markov Field evolution: t=10

Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

markov field evolution t 20
Markov Field evolution: t=20

Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

slide20

Markov Field evolution

Manual Segmentation

MRF Segmentation

ROI (100x100 pixel)

Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

slide21

Markov Field evolution

Manual Segmentation

MRF Segmentation

ROI (90x90 pixel)

Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

results
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)

results hard exudates
Results: Hard Exudates

Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

results hard exudates1
Results: Hard Exudates

Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

results foveal micro aneurysm
Results: Foveal Micro Aneurysm

Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)

results foveal micro aneurysm1
Results: Foveal Micro Aneurysm

Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)