1. 3D Connected Operators and their application to interactive medical volume segmentation
Alberto Signoroni, Riccardo Leonardi
Signals and Communications Lab. – DEA
University of Brescia, Italy

2. A. Signoroni & R. Leonardi University of Brescia, Italy
Purpose
Present a 3D extension of Connected Operators
Show the properties and talk about applications (especially for 3D medical datasets)
DEMO: 3D Conn.Op. filtering + interactive medical volume segmentation
Simbologia di base
A. Signoroni & R. Leonardi University of Brescia, Italy

3. A. Signoroni & R. Leonardi University of Brescia, Italy
Introduction
Connected Operators (Conn.Op.) has became popular in MMSP for:
Contour preservation property
General and powerful formalization
Computational efficiency
Application fields: image and video analysis and indexing
There are some unexplored issues and app’s
Conn. Op. in the 3D space
Conn. Op. for volume segmentation (biomedical)
Simbologia di base
A. Signoroni & R. Leonardi University of Brescia, Italy

4. Connected Operators on Images
A traditional (linear and not) filter acts on a pixel basis and produce distorted images (impulse response, structuring elements)
A C.O. filter analyze each flat-zone - same colour(s) - of the image partition and decide to:
Remove the flat-zone (simplification effect)
Preserve the flat-zone (contour preservation)
A. Signoroni & R. Leonardi University of Brescia, Italy

5. Conn. Op. on Gray-scale Images
Definition: an operator  on the set of gray-scale images I is connected if the flat-zone partition P((I)) is coarser than P(I) for every image I  I.
Implementation: filter by flat zones analysis equivalent to:
Thresholding: define a set of thresholds k (e.g. gray levels)
Binarization: from I to set of Bk where Bk(i,j)=1 iff I(i,j)>k
Conn.comp. filtering (analysis & decision): k, BSk=(Bk)
Gray level re-assignment: k, if BSk(i,j) =1 then ISk(i,j) = k
Stacking: IS = (I) = ( ISk )
Figura a toni di grigio V k
A. Signoroni & R. Leonardi University of Brescia, Italy

6. Image Simplification by Con.Op.
Gray-level conn.op. 
Esempio con immagini e ricordo di contour preservation con nota critica sulla dinamica
Sergio p.55
 simplification effect
 contour preservation
 contrast preservation
 filtering:
A. Signoroni & R. Leonardi University of Brescia, Italy

7. Connected Operators on Volumes
Data-sets: MR, CT, angioCT, angioMR, US,…
“Natural” volumes: near isotrope voxels, noisy
Gray-level Conn.Op. on 3D data-sets:
Slice based (similar to the frame based for video apps) – does not requires a 3D extension and does not involve additional computational costs.
Volumetric – requires a 3D extension and involve an additional computational cost.
Figura p.58 sergio
Wich is the more suitable approach ?
A. Signoroni & R. Leonardi University of Brescia, Italy

8. 2D(z) vs. 3D Conn.Op. processingy x
A correct 3D Conn.Op.
processing should
guarantee the
properties of
surface
preservation
and
contour preservation
wrt any
“re-slicing” planes
Figura p.58 sergio
Scanning plane
Re-slicing
planes
A. Signoroni & R. Leonardi University of Brescia, Italy

9. A. Signoroni & R. Leonardi University of Brescia, Italy
2D(z) Conn.Op. filtering
Simplified re-slice after a 2D(z) Conn. Op. processing: area opening 30
Original re-slice
Figura p.58 sergio
stripping artifacts
A. Signoroni & R. Leonardi University of Brescia, Italy

10. A. Signoroni & R. Leonardi University of Brescia, Italy
3D Conn. Op. Extension
Extensions for a 3D flat-zone filtering
Connectivity
Voxel neighborhood systems
3D-1 : 6 voxel with face in contact
3D-2 : 18 voxel with edge in contact
3D-3 : 26 voxel with corner in contact
3D Conn. Comp. flooding: computational cost (data dependent)
Filtering criteria
Volume (volume opening)
Complexity = surface/volume
Compactness = volume/(surface)2 …
Example: 3D-1
Estensione 3d e proprietà
A. Signoroni & R. Leonardi University of Brescia, Italy

11. A. Signoroni & R. Leonardi University of Brescia, Italy
3D Conn.Op. filtering (1)
Simplified re-slice after a 3D Conn.Op. processing: volume opening 1000
Original re-slice
Figura p.58 sergio
surface preservation
A. Signoroni & R. Leonardi University of Brescia, Italy

12. A. Signoroni & R. Leonardi University of Brescia, Italy
3D Conn.Op. filtering (2)
Criteri di filtraggio e esempio slices
surface preservation
A. Signoroni & R. Leonardi University of Brescia, Italy

13. A. Signoroni & R. Leonardi University of Brescia, Italy
3D Conn.Op. filtering (3)
Criteri di filtraggio e esempio slices
A. Signoroni & R. Leonardi University of Brescia, Italy

14. A. Signoroni & R. Leonardi University of Brescia, Italy
3D Conn.Op. filtering (4)
Criteri di filtraggio e esempio slices
filtered data are suitable for segmentaion ?
A. Signoroni & R. Leonardi University of Brescia, Italy

15. Interactive segmentation
Interactive 3D Morphological Segmentation Technique
Off-line 3D Conn.Op. pre-processing (few minute of CPU time)
User guided procedure (e.g. by a neuroradiologist):
First loop:
marker and RoI selection
3D modified Watershed engine
Refinement loops: if needed
Usually, even guided segmentation is unsuccessful without 3D Conn.Op. pre-processing.
Esempio su slices per differenziare da 2d ripetuto
A. Signoroni & R. Leonardi University of Brescia, Italy

16. Interactive Segmentation: brain
Esempio su cervello
Manula Segm.Time: 4h
Interactive ST: ? demo
A. Signoroni & R. Leonardi University of Brescia, Italy

17. A. Signoroni & R. Leonardi University of Brescia, Italy
G1 system: demo
A. Signoroni & R. Leonardi University of Brescia, Italy

18. A. Signoroni & R. Leonardi University of Brescia, Italy
Others Applications
A 3D  filtering produces a simplified volume with good surfaces preservation, useful to:
Direct segmentation: Object analysis, detection and visualization – suitable for specific segmentation tasks
A 3D  analysis produces an highly structured volume description (the flat-zone partition stack) which can be exploited in various applications:
Indexing and retrieval: in 3D information description retrieval can be seen in conjunction with the problem of atlas building, navigation, comparison, tracking (follow-up), measurements… hot themes for biomedical applications.
Rivedere i prodotti di e la rappresentazione ad albero, commentare i vari utilizzi, inserire immagini (e.g. sclerosi multipla nell’object analysis, reni nella semplificazione…)
A. Signoroni & R. Leonardi University of Brescia, Italy

19. A. Signoroni & R. Leonardi University of Brescia, Italy
Conclusions
Conn. Op. 3D extension
Filtering criteria and peculiarities of 3D Conn.Op. processing
Use of 3D approach is justified despite the computational cost
3D simplification as pre-processing step to facilitate volume segmentation (experimental strong evidence)
Future research and applications
Computational aspects
Direct 3D segmentation: filtering criteria, specific tasks
Biomedical atlas: an indexing based application that exploits the flat-zone partition structure
Rivedere i prodotti di e la rappresentazione ad albero, commentare i vari utilizzi, inserire immagini (e.g. sclerosi multipla nell’object analysis, reni nella semplificazione…)
A. Signoroni & R. Leonardi University of Brescia, Italy