Transcript Biomedical images processing and analysis

Biomedical images
processing and analysis
Biomedical images processing and analysis
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Group members
Alfredo Ruggeri
Enrico Grisan
Alfredo Giani (2003-)
Massimo De Luca (2004-)
Fabio Scarpa (2006-)
Lorenzo Marafatto (2005-)
Associate professor
Marco Foracchia (-2003)
PhD student
Post doc
Post doc
PhD student
PhD student
Fellowship
Biomedical images processing and analysis
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Collaborations
J. Jaroszewski - Cornea Bank Berlin, Clinic of Ophthalmology, University
School of Medicine, Berlin, Germany
A. Neubauer - Dept. of Ophthalmology, Ludwig Maximilians University,
Munich, Germany
P. Gain - Ophthalmology Department, Bellevue Hospital, Saint-Etienne,
France
S. Klyce – Eye Center, Louisiana State University, New Orleans (LO), USA
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
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
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Grants
University of Padova: € 60.000 (shared)
University of Padova: € 15.000 (shared)
Ministry of University: € 20.000
CARIPARO Bank Foundation: € 40.000
Nidek Technologies: € 25.000 (+ 4 PhD fellowships)
TESI Imaging: under negotiation
Biomedical images processing and analysis
Publications
8
Intl Journals
7
Intl Conf Papers
6
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Intl Conf Abstracts
5
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In
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2001
2002
2003
2004
2005
Biomedical images processing and analysis
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1.Cell contour recognition for in-vivo
microscopy of corneal endothelium
Cell contour recognition
1. Contour extraction
Artificial Neural Network with
weight-filters arrays
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Mathematical morphology
2. Contour completion
Connection of floating facing boundaries
Cell contour recognition
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The ENDO software is a module of the
system for ophthalmology.
Cell contour recognition
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Human visual processing is very powerful and
complex …
Kanisza triangles
Kanisza square
Cell contour recognition
Human visual processing is very powerful and
complex …
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Cell contours appear nice and clear on
a broad view….
… but local gray-scale
values do not give all the
information necessary to
identify all cell contours:
false contours
missed contours
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A glimpse of tomorrow …
Biomedical images processing and analysis
2.Fourier analysis for the estimation of
endothelium cell density on eye bank images
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Fully automatic technique in eye banks without cell contour detection.

A repetitive pattern of cell
borders is clearly visible.
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Spatial frequency of this
pattern is proportional to cell
density.

Frequency information is
available through Fourier
analysis.
Frequency-based density estimation
Gray-scale image of 2D-DFT log-magnitude.
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A circular band
indicates that the
endothelium
image contains a
repetitive pattern
at a specific
frequency.
Spatial frequency is the radius of the band
Radius of circular band
can be used to estimate
cell density.
(Foracchia et al., Med Biol Eng Comput, 2004)
Frequency-based density estimation
Automatic density (cell/mm2)
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3500
3000
2500
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1500
2000
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3500
2
Manual density (cell/mm )
(Ruggeri et al., Br J Ophthalmol, 2005)
Nidek Technologies NAVIS-EyeBank system
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•
The EyeBank software is a module of the
system for ophthalmology.
Biomedical images processing and analysis
3. Tracking techniques for vessel-like structure
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Applications to:
• vessels in retina
• nerves in cornea
Clinical parameters:
• length
• tortuosity
• bifurcations
• caliber course
• optic disc detection
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Tracking techniques in retina
(Foracchia et al., Med Image Anal, 2005)
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Tracking techniques in retina
(Foracchia et al., IEEE TMI, 2004)
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Tracking techniques in cornea
Biomedical images processing and analysis
4.Methodologies in eye fundus analysis for the
diagnosis of retinopathy
Hypertensive and diabetic retinopathies are characterized by
presence of fundus lesions.
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Automatic and objective tools
for image analysis:
• patient screening
• disease assessment &
monitoring in time
• (new) drugs efficacy
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Eye fundus analysis
Steps:
• Detection
• Classification
• Measurement
• Clinical
assessment
(Grisan et al., EMBEC’05 Conf., 2005)
Biomedical images processing and analysis
5. Design and realization of an adaptive optics
fundus camera
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Eye
Retinal
Imaging
Flash
path
Wavefront
sensor
Image Processing
Adaptive optics fundus camera
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Simulation system with creation of aberrated
image and correction system
Acquired
image
Coma
Mirror
update
Image
Analysis
Defocus
Astigmatism
Corrected
image
(Grisan et al., IEEE EMBS Conf., 2005)
Biomedical images processing and analysis
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6. Automatic cariotyping
Automatic cariotyping
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1. Segmentation of single chromosomes
2. Classification and pairing
1. - image enhancement
- cluster segmentation
- touching and overlapping
elimination by cuts
2. - feature extraction (banding)
- classification