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Biomedical images processing and analysis. Biomedical images processing and analysis. Group members Massimo De Luca (2003-) Fellowship – PhD student Marco Foracchia (-2003) PhD student Alfredo Giani (2003-) Post doc Enrico Grisan PhD student – Post doc Lorenzo Marafatto (2005-) Fellowship
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Biomedical images processing and analysis Group members Massimo De Luca(2003-) Fellowship – PhD student Marco Foracchia(-2003) PhD student Alfredo Giani (2003-)Post doc Enrico GrisanPhD student – Post doc Lorenzo Marafatto(2005-)Fellowship Alfredo RuggeriAssociate professor
Biomedical images processing and analysis Cooperations J. Jaroszewski - Cornea Bank Berlin, Clinic of Ophthalmology, University School of Medicine, Berlin, Germany A. Neubauer - Dept. of Ophthalmology, Ludwig Maximilians University, Munich, Germany S. Piermarocchi – Dept. of Ophthalmology, University of Padova D. Ponzin - Veneto Eye Bank Foundation, Venice, Italy A. Pocobelli - Eye Bank, S. Giovanni-Addolorata Hospital, Rome, Italy P. Gain - Ophthalmology Department, Bellevue Hospital, Saint-Etienne, France A. Bezerianos - Dept. of Medical Physics, University of Patras, Greece G. Barbaro - Nidek Technologies, Padova, Italy P. Favaro - Siemens Corporate Research, Princeton (NJ), USA
Biomedical images processing and analysis Publications
Biomedical images processing and analysis Funding University of Padova: € 60.000 (shared) University of Padova: € 15.000 Ministry of University: € 20.000 Nidek Technologies: € 25.000 CARIPARO Bank Foundation: € 40.000
Biomedical images processing and analysis 1. Cell contour recognition for in-vivo microscopy of corneal endothelium
Cell contour recognition Statistical Correction ROI extraction Band-Pass Filtering ANN contour extraction Hole removal and Erosion Skeletonization Contour completion Perimeter extraction Correction
Cell contour recognition Statistical Correction ROI extraction Band-Pass Filtering ANN contour extraction Hole removal and Erosion Skeletonization Contour completion Perimeter extraction Correction
Cell contour recognition Statistical Correction ROI extraction Band-Pass Filtering ANN contour extraction Hole removal and Erosion Skeletonization Contour completion Perimeter extraction Correction
Cell contour recognition Statistical Correction ROI extraction Band-Pass Filtering ANN contour extraction Hole removal and Erosion Skeletonization Contour completion Perimeter extraction Correction
Cell contour recognition Statistical Correction ROI extraction Band-Pass Filtering ANN contour extraction Hole removal and Erosion Skeletonization Contour completion Perimeter extraction Correction
Cell contour recognition Statistical Correction ROI extraction Band-Pass Filtering ANN contour extraction Hole removal and Erosion Skeletonization Contour completion Perimeter extraction Correction
Cell contour recognition Statistical Correction ROI extraction Band-Pass Filtering ANN contour extraction Hole removal and Erosion Skeletonization Contour completion Perimeter extraction Correction
Cell contour recognition Statistical Correction ROI extraction Band-Pass Filtering ANN contour extraction Hole removal and Erosion Skeletonization Contour completion Perimeter extraction Correction
Cell contour recognition Statistical Correction ROI extraction Band-Pass Filtering ANN contour extraction Hole removal and Erosion Skeletonization Contour completion Perimeter extraction Correction
Nidek Technologies NAVIS-ENDO system • The ENDOsoftware is a module of the system for ophthalmology.
Biomedical images processing and analysis 2. Fourier analysis for the estimation of cell density on eye bank images of donor corneas
2250 cell/mm2 Fully automatic density estimation • AIM: • to develop a fully automatic technique for cell density estimation (no user intervention). • It must be without cell contour detection.
Frequency-based density estimation • A repetitive pattern of cell borders is clearly visible. • Spatial frequency of this pattern is proportional to cell density. • Frequency information is available through Fourier analysis. • Information from Fourier analysis can provide an estimation of cell density.
A circular band indicates that the endothelium image contains a repetitive pattern at a specific frequency. Spatial frequency is the radius of the band Frequency-based density estimation Gray-scale image of 2D-DFT log-magnitude. Radius of circular band can be used to estimate cell density.
Position of second peak provides estimated frequency f of cell borders. Frequency-based density estimation
Frequency-based density estimation (Ruggeri et al., Br J Ophthalmol, Mar 05)
Nidek Technologies NAVIS-EyeBank system • The EyeBanksoftware is a module of the system for ophthalmology.
Biomedical images processing and analysis 3. Tracking techniques for vessel-like structure • Applications to: • vessels in retina • nerves in cornea • Clinical outcomes: • length • tortuosity • bifurcations • caliber course • optic disc detection
Biomedical images processing and analysis 4. Methodologies in eye fundus analysis for the diagnosis of retinopathy Diabetic retinopathy characterized by fundus lesions • Automatic and objective tools: • wide screening • disease assessment & monitoring in time • (new) drugs efficacy
Eye fundus analysis • Three steps: • Detection • Classification & measurement • Clinical assessment
Biomedical images processing and analysis 5. Design and realization of an adaptive optics fundus camera Eye RetinalImaging Flashpath Wavefrontsensor Image Processing
Biomedical images processing and analysis 5. Design and realization of an adaptive optics fundus camera Imagecorrected Imageacquisition Defocus Coma Astigmatism Mirrorupdate Image Analysis
Biomedical images processing and analysis 6. Bio-inspired omni-directional vision Omnidirectional mirror Development ofbiomimeticalgorithmsfor vision Space variant sensorproviding with a foveated vision