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Agents, V-ants and
3D Reconstruction for
Lung CT Imaging
Sorin Cr istian Cheran - ASP, INFN sez. Torino, Università degli Studi di Torino
Gianfranco Gargano - INFN sez. Bari, Università degli Studi di Bari
Pisa Meeting, 7 - 9 February 2005
Sorin Cristian Cheran, Gianfranco Gargano
Index
• Materials
• 2D Segmentation
• Slides Interpolation
• 3D Matrix/World Creation
• Bronchial Tree 3D Reconstruction
• Nodules Detection
• Conclusions
Pisa Meeting, 7 - 9 February 2005
Sorin Cristian Cheran, Gianfranco Gargano
The Lung and The CTs
[LUNG]
1.Either of the pair of organs occupying the cavity of the thorax that effect the aeration of the blood.
2.Balloon-like structures in the chest that bring oxygen into the body and expel carbon dioxide from
QuickTime™ and a
YUV420 codec decompressor
the body are needed to see this picture.
[TYPES]
1.Small Cell Lung Cancer (SCLC)
- 20% of all lung cancers
2.Non Small Cell Lung Cancer
(NSCLC) - 80% of all lung cancer
[Risks]
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In the United States alone, it is YUV420 codec decompressor
estimated that 154,900 died are needed to see this picture.
from lung cancer in 2002. In
comparison,is estimated that
126,800 people died from
colon, breast and prostate
cancer combined, in 2002.
[LUNG CANCER]
Lung Cancer happens when cells in the lung begin to grow out of control and can than
invade nearby tissues or spread throughout the body; Large collections of this out of
control tissues are called tumors.
Pisa Meeting, 7 - 9 February 2005
Sorin Cristian Cheran, Gianfranco Gargano
Starting Point
Framework
-pending collaboration’s approving,
discussion, ideas and improvements
-Mapping of the DICOM structure into
classes.
-Tools for manipulating the DICOM Files.
-“We can do it ” experimental GUI
Border Detection
-At the moment two approaches are
available.
-Left the algorithm developed at Pisa
-Right the algorithm developed at
Lecce
Pisa Meeting, 7 - 9 February 2005
Sorin Cristian Cheran, Gianfranco Gargano
Image Interpolation - Theory
[IDEA]
In order to provide a richer environment we are thinking of using interpolation methods that will
generate “artificial images” thus revealing hidden information.
[RADON RECONSTRUCTION]
Radon reconstruction is the technique in which the object is reconstructed from its projections. This
reconstruction method is based on approximating the inverse Radon Transform.
[RADON Transform]
The 2-D Radon transform is the mathematical relationship which maps the spatial domain (x,y) to
the Radon domain (p,phi). The Radon transform consists of taking a line integral along a line (ray)
which passes through the object space. The radon transform is expressed mathematically as: {R}( p, ) (x, y)(x cos y sin p)dxdy
[FILTERED BACK PROJECTION - INVERSE R.T.]
It is an approximation of the Inverse Radon Transform.
[The principle] Several x-ray images of a real-world volume are acquired
[The Data] X-ray images (projections) of known orientation, given by data samples.
[The Goal] Reconstruct a numeric representation of the volume from these samples.
[The Mean] Obtain each voxel value from its pooled trace on the several projections.
[Resampling] At this point one can obtain the “artificial slices”
[Reslicing] An advantage of the volume reconstruction is the capability of obtaining new perpendicular slices on the
original ones.
Pisa Meeting, 7 - 9 February 2005
Sorin Cristian Cheran, Gianfranco Gargano
Image Interpolation - Graphical Representation (I)
y 0 d
Rz0 (x,90) (x, y,z )dy
0
y0
l
Rz0 (y,0) (x, y,z )dx0
0
Pisa Meeting, 7 - 9 February 2005
Sorin Cristian Cheran, Gianfranco Gargano
Image Interpolation - Graphical Representation (II)
Pisa Meeting, 7 - 9 February 2005
Sorin Cristian Cheran, Gianfranco Gargano
Agents - Artificial Intelligence
[AUTONOMY] in controlling itself
[PRO-ACTIVENESS] in taking the initiative to select
[REACTIVITY] to the change of the environment What is an Agent? adequate behaviour to reach the goal
[SOCIAL ABILITY] of interact with other agents via languages
[AGENTS LANGUAGES]
Are software systems for programming and experimenting with agents.
[AGENTS ARCHITECTURE]
Reactive: Intelligence behaviour arises as a result of agent’s interaction with
environment.
Belief - Desire - Intention: What we want? How can we achieve that?
[TYPE OF AGENTS]
Interface Agents: It act like a filter or interface between the user and a source
of information
Information Agents: It can retrieve information for the user from different
sources (Internet Search Engine)
Believable Agents: It simulates emotions such that can pass as a human
being
Cooperative Problem Solving and Distributed AI: It could do almost anything
like: system management, air-traffic control and CT image processing
[DEFINITION]
An Agent is a computer system situated in an environment and that is able of
autonomous action in order to meet its designed objectives.
Pisa Meeting, 7 - 9 February 2005
Sorin Cristian Cheran, Gianfranco Gargano
Virtual Ants - Artificial Life
[DEFINITION]
1.Artificial Life is the study of man-made systems that exhibit behaviors characteristic of natural living systems.
2. The goal AL is to provide biological models and also to investigate general principles of life.
[Emergence] [Self-Organization]
Property of a system as a whole not contained in Spontaneous formation of complex patterns
any of its parts that results from the interaction of or complex behavior emerging from the
the elements of such a system, which act interaction of simple lower-level
following local, low-level rules. elements/organisms
[Communication]
[Virtual Ants]
Through Stigmergic Interactions
V-ants are computer simulated societies
-interactions mediated by modifications of the
from the insects colonies present in
[No Mainframe]
nature. Thus the behavior of the insects
is mapped onto artificial beings. No central coordinator is needed to environment (depositing pheromones),
organize the search for, and storage of
food.
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TIFF (LZW) decompressor
are needed to see this picture.
[Advantages]
The parallel application of simple local rules solves a complex problem in a much more flexible and efficient way.
Social insects are individuals which “working together in parallel” create a super-organism capable of solving even the most
complex problems without any central organizer. .
Pisa Meeting, 7 - 9 February 2005
Sorin Cristian Cheran, Gianfranco Gargano
Our Goals - Their Roles
[COMPARISON]
AL is concerned with the generation of lifelike behavior. AI is concerned with generating intelligent behavior. AL and the new approaches in
AI both work bottom-up, combining many simple elements into more complicated ones, looking for emergence and principles of self-
organization, using the synthetic methodology.
[GOAL]
[WHO]
Create a Click’n’Clean approach
[ROLES] Two approaches might be tried
that will get rid of the Bronchial
INTELLIGENT [AGENTS] and
Tree after this has been Ant world A gent world
COLONIAL[V-ANTS]
completely identified with a
-workers -seekers
simple click.
-Queens -mappers
[Algorithm Idea]
1.Ants [Agents] are deployed in the newly constructed World (I.R.T)
2.They have the 2 main degrees of freedom but can gain points according to priorities to move on the third
3.They start moving towards the high intensities under different reasons.
4.Start communicating to others the position of the FOOD/GOAL.
5.Other agents/ants are arriving on the site and try to find the points on the surface of the bronchial tree.
6. These points are passes to mappers/Queens that are mapping around the points a mesh. Thus creating the
surface.
7. The reconstruction is thus done and the bronchial tree.
Pisa Meeting, 7 - 9 February 2005
Sorin Cristian Cheran, Gianfranco Gargano
Supporting Algorithms
Problem: They are good but not that good
Solution: Use a series of algorithms than can help the agents/ants during
searching, mapping and also can take their work further
Supporting Algorithms:
Bronchial Tree Reconstruction
Kohonen Self Organized Features Maps (Modeling Bronchial surface)
Active Shape Models (Modeling Bronchial Shape )
Centre of Maximal Balls (Modeling Bronchial Volume )
Skeletonization (Modeling Bronchial Structure)
Nodule Recognition
Centre of Maximal Balls ( pleura attached nodule)
Dot- Enhancement Algorithm
Pisa Meeting, 7 - 9 February 2005
Sorin Cristian Cheran, Gianfranco Gargano
[Questions?]
[Thank you for your
attention]
[Discussion…]
[Suggestions…]
Pisa Meeting, 7 - 9 February 2005
Sorin Cristian Cheran, Gianfranco Gargano
