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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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Presentation Transcript
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
- 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
Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)
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
- 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
- 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
- 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
- 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
Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)
Markov Field evolution: t=0
Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)
Markov Field evolution: t=1
Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)
Markov Field evolution: t=2
Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)
Markov Field evolution: t=3
Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)
Markov Field evolution: t=6
Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)
Markov Field evolution: t=10
Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)
Markov Field evolution: t=20
Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)
Manual Segmentation
MRF Segmentation
ROI (100x100 pixel)
Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)
Manual Segmentation
MRF Segmentation
ROI (90x90 pixel)
Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)
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
Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)
Results: Hard Exudates
Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)
Results: Foveal Micro Aneurysm
Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)
Results: Foveal Micro Aneurysm
Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)
