Non vascular lesions - PowerPoint PPT Presentation

Haemorrhages

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

Hard exudates

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

Cotton wool

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

• Diagnostic information from lesions:
• type of retinopathy
• severity

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

• Identification of position of abnormal regions
• Accurate outline of abnormal region boundary

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

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

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

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

• 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)

• 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)

• 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)

• 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)

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

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

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

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

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

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

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

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)

• Manually segmented images
• 200 ROIs
• centered on a lesion
• 50x50 pixels
• Evaluation of pixel classification

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

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

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

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

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