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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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Haemorrhages Non vascular lesions Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)
Non vascular lesions Hard exudates Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)
Non vascular lesions Cotton wool Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)
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)
Markov Field evolution Manual Segmentation MRF Segmentation ROI (100x100 pixel) Ing. Enrico Grisan – D.E.I. – Univ. Padova (Italy)
Markov Field evolution 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)