Lecce, April 13, 2011 – PhD discussion
A Computer Assisted Detection system
for juxta-pleural nodule identification in chest
Computed Tomography images
PhD tutors: Dr. Ivan De Mitri
Dr.Giorgio De Nunzio
PhD student: Andrea Massafra
MAGIC-5
(Medical Applications on a GRID Infrastructure Connection)
Detection (CAD)
The Project isComputer
funded byAssisted
INFN - the
Italian National Institute of
Distributed Computing Infrastructure (GRID)
Nuclear Physics - and coordinated with hospitals and Universities
6 Research Groups in Italy
(Bari, Genova, Lecce, Napoli,
Pisa, Torino, ~ 40 Researchers)
Medical (Imaging) Applications:
-Analysis of Digital Images
MAGIC-5
Mammography- (breast cancer 2002-2006)
Lung CT (nodules 2004 - present)
Brain RMI (Alzheimer' s Disease 2006 – present)
2
MAGIC-5 research lines
Mammography
- automatic detection of masses and micro-calcifications
in mammography
Lung CT
- detection of lung nodules in CT (Computed
Tomography) images
Brain RMI
- early detection of Alzheimer’s disease in MR (Magnetic
Resonance) and PET (Positron Emission Tomography) images
PhD work
Lung Segmentation algorithm
1
gray value threshold
2
region growing for lung
reconstruction
3
wavefront algorithm for
vascular tree extracion
4
lung fusion problem solving
5
masks extraction
building of a CAD system for juxtapleural nodule detection,composed of a
segmentation tool, a “nodule hunter”
(a concavity-patching method such as
the α−hull or morphological closing),
feature calculation, and classification
by an Artificial Neural Network
implementation of conformal mapping
algorithms for lung
surface uniformization, as an alternative
“nodule hunter” and
a non-standard visualization tool
fine tuning of the lung segmentation
algorithm used in the
MAGIC-5 Collaboration
Materials
Preliminary results from 57 CT scans from the MAGIC-5 DB: 78 juxta-pleural
nodules, of which 28 directly in contact with the pleura (“pleural nodules”, easily
detectable concavity in the segmentation mask!), the others connected by
peduncles (“sub-pleural nodules”, small or almost absent concavity entrance).
Internal Nodule:
It originates in the internal
part of the lung and it is fully
embedded in the lung
parenchyma.
Sub-pleural Nodule:
It originates in the internal
part of the lung, but it can
be found adjacent or
connected to the pleural
surface.
Pleural nodule:
It originates in the pleura
and grow towards the lung
parenchyma.
5
Structure of a CAD system
Preprocessing
- the aim of this stage is to reduce the amount of artifacts and noise in
the image, enahancing its quality
ROI detection
the acronym of Region of Interest. In this step structures or
regions in the image that contain possible pathological lesions are
located
-
Structure/ROI Analysis
- from each ROI, the system computes quantitative features that can
characterize it (form, size and location, texture features, and so on)
Classification
- in the last step of CAD building, after each ROI is analyzed, it is
individually classified as healthy or pathological. In CAD applications,
classification is generally supervised and classifiers are often based on
neural networks
ROI detection
Curvature Filtering
mm^-1
Alpha hull : a convex hull generalization
based on notion of shape of a point set
Morphological closing:
dilation followed by erosion of an image
Structure/ROI Analysis
Statistical texture analysis
represents texture indirectly by non-deterministic properties that govern
distribution and relationship between image gray levels
First order statistical parameters
measure the likelihood of
observing a gray value at a randomly-chosen location in the image. They can be
computed from the histogram of pixel intensities in the image. These depend only
on individual pixel values and ignore the spatial interaction between image pixels.
Second order statistical parameters
are defined as the likelihood of
observing a pair of gray values occurring at the endpoints of a segment of given
length placed in the image at a random location and orientation.
Geometrical Features:
of the image under inspection.
provides a good symbolic description
Classification with Neural Network
The k-th neuron consists of
input signals
having positive or negative
weight
associated with each input node
the activation potential
the activation function
Structure of ANN
the output signal
Feed Forward Neural Networks
patterns are presented to input units and
the signal crosses the network
from the input to the output layer
Activation function
Backpropagation algorithm
The classification problem concerns the distinction between healthy and pathological
subjects
During network training, the output signal is compared with a target. The training
error is
The back-propagation learning rule is based on
the gradient-descent optimization method.
The algorithm stops when: fixed number of epochs
is achieved or the classification error starts
to increase on Validation Set (early stop method) or others conditions
The ROC curve
If the instance is really positive and it is classified as positive,
it is called True Positive TP
if the instance is positive, but it is classified as negative, it is
called False Negative FN
if the instance is negative and it is classified as positive, it is
called False Positive FP
if the instance is negative and it is classified as negative, it is
called True Negative TN
sensitivity
specificity
Juxta-Pleural nodule detection in CT images based
on multiscale α-hull and morphological closing
The procedure follows the classical scheme of a CAD system
Juxta-pleural nodule candidate detection
(ROI “hunting”)
Lung
segmentation
closing
Hierarchical
tree
Feature calculation
Classification
Alpha hull
features:
geometrical (span, depth…),
texture (1° order)
Conformal mapping
12
{
Overview of segmentation algorithm
A gray-value threshold for the segmentationm of the
respiratory apparatus is performed by analyzing the
image histogram
3D RG is applied to the CT volume. Voxels are included
in the grown region if their Hounsfield Number is
smaller than ϑ. The resulting binary mask containing
the trachea, the bronchi, and the lungs.
the external airways are extracted and removed by a
wavefront simulation model with appropriate stop
conditions. The resulting mask, containing only the
lungs, is labeled M'
partial volume effects reduction. The lungs from appear
as a single object “fusion”). If that happens, the fusion is
removed by threshold adjustment.
′′
Simple-threshold 3D RG is used, to grow the left
and the right lung respectively, generating two masks
with concavities (juxta-pleural nodules?) and vessels
A method for concavity patching: α-hull
In the Euclidean space a set B is said convex if
The convex-hull of a point set I as the intersection of
all the convex sets that contain I
An α-ball b is an open ball with radius α
the α−hull of a set S is defined as is the intersention of the complement of all the
closed circles of radius 1/α that contain all the points of S.
A methods for concavity patching: α-hull
If α=∞ the α-hull is the convex hull of S
If α=0 the α-hull is S itself
A method for concavity patching:
morphological closing
the eroded set Y of a set X of points in space is the locus of centres x of Bx
included in the set X
where Bx is the structuring element
and
A method for concavity patching:
morphological closing
The dilation is defined as
Where
is the complement
of the set
The closing of an input image
A by a structuring element B
is defined as
DETECTION OF NODULE CANDIDATES
Concavities: found slice by slice by calculating the border difference between the original lung
slice and the same treated by closing or α-hull : D(slice, α) = Aedge – Asm_edge
D is a list of pixels belonging to the original border but not to the smoothed one, therefore it
consists of a group of pixels identifying the concavities at the given value of α
D
Asm_edge
Aedge
–
=
NODULE
mm
Nodule radii histogram
Asm_edge
Aedge
–
D
=
α chosen: {8, 10, 12} mm
FALSE POSITIVE
18
Juxta-pleural-nodule candidate detection: multiscale hierarchy
By repeating, for a suitable set A of α
values, difference operation and
concavity search, a hierarchy of
concavities (for each slice) is determined
1
0 .9
N001 N002 N003 N004 N005 N006 N007 N008
N009
N010
N011
N012
N022 N023 N024 N025 N026 N027 N028 N029
N030
N031
N032
N040 N041 N042 N043 N044
N045
N046
N047
N048
N054 N055 N056 N057 N058
N059
N060
N061
N070 N071 N072 N073 N074
N075
N076
N077
N013
N014
N015 N016 N017 N018 N019 N020 N021
N034
N033
N035 N036
N037
N050
N049
N051 N052
N053
N062 N064 N066
N063 N065 N067 N068
N069
N078 N080 N082
N079 N081 N083 N084
N085
0 .8
Each row corresponds to a scale
level from the top (largest α) to
bottom rows (smallest α)
N038 N039
Concavities at scale α
Tree implementation
by an array
0 .7
0 .6
0 .5
0 .4
0 .3
N086
N087 N088
N089 N090 N091 N092 N093 N094 N096 N098
N095 N097 N099 N100
N101
N102
N103 N104
N105 N106 N107 N108 N109 N110 N112 N114
N111 N113 N115 N116
N117
N118
N119 N120
N121 N122 N123 N124 N125 N126 N128 N130
N127 N129 N131 N132
N133
Concavity n-ary tree (for a slice)
N134 N135 N136 N137 N138 N139 N141 N143
N140 N142 N144
N146 N147 N148 N149 N150 N151 N152 N155
N153 N154
0 .2
0 .1
N156
N157
N145
If a segmentation boundary contains nested
concavities, a set of α-hulls with successively
larger values of α can be constructed to identify
(by difference operation and concavity search)
concavities at different scales as defined by α.
This creates a natural hierarchy of concavities
where the hierarchy is ordered by the α value, and
allows tracking relationship between concavities.
19
Features
Geometrical Features
SPAN,
DEPTH,
BORDER LENGTH,
AREA
DEPTH/SPAN
RADIUS
CIRCULARITY
SPAN
Grey mean
Textural Features:
circularity
Standard Deviation
Where x is the mean Hounfield intensity distribution and Npix is the number of pixel
DEPTH
Features
Skewness
where xi is the gray value and p(x) is the frequency and б the standard deviation
Kurtosis
Shannon's Entropy
where P(x) is the probability that X is in the state x and P lg2 P is defined as 0
if P = 0.
Classification
Classifier: supervised two-layer, 13 input, variable hidden neurons, 1 output feed forward
ANN, trained with gradient descent learning rule with momentum
k-fold cross validation (for us k = 3)
From the ANN output on the whole
dataset the Receiver Operating
Characteristic (ROC) curves were
drawn:
AUC = 0.76, almost identical for mc
and α-hull.
Positive findings
R O C c u r ve
1
0 .9
0 .8
1) Training: P1 U N1 U P2 U N2 - Test: P3 U N3 U Na3
2) Training: P1 U N1 U P3 U N3 - Test: P2 U N2 U Na2
3) Training: P2 U N2 U P3 U N3 - Test: P1 U N1 U Na1
s e n s it iv it y
0 .7
0 .6
0 .5
0 .4
0 .3
0 .2
0 .1
0
0
0 .2
0 .4
0 .6
1 - s p e c i fi c i t y
0 .8
1
22
Classification : number of neurons in hidden layer
Early stop method
200 epochs
Results:
The prototype of a complete CAD system for juxta-pleural nodule detection
based on the α−hull has been developed on a Dell T5500 Precision workstation
(two quad-core Intel-Xeon [email protected] Ghz, 12 GB RAM)
An ANN from the MatLab toolbox for neural network was used
The whole system was implemented using MatLab environment
The modular structure of the CAD system allowed the comparison of the
α−hull efficiency with that obtained by Multiscale Morphological Closing
the α−hull had better sensitivity (92.3% vs 84.6%) than morphological closing.
The number of juxta-pleural nodules detected by the α-hull is 72 out of 78, while only
66 are collected by morphological closing (detection level)
All the nodules lost by the α-hull are also lost by morphological closing
Results:
At classification level, the two methods proved roughly equivalent, with sensitivity
and specificity around 72 − 75% and AUC around 0.75 − 0.77
Two different methods to stop ANN have been tested:
• early stop
• fixed number of epochs (200)
From the computational point of view, the two methods are quite different: on an
average CT scan (300 slices)
• alpha hull nodule hunting 3 minutes for slice
• morphological closing 10 minutes
Paper
1)Giorgio De Nunzio, Eleonora Tommasi, Antonella Agrusti, Rosella Cataldo,Ivan De Mitri, Marco
Favetta, Silvio Maglio, Andrea Massafra, Maurizio Quarta, Massimo Torsello, Ilaria Zecca, Roberto
Bellotti, Sabina Tangaro, Piero Calvini, Niccolo Camarlinghi, Fabio Falaschi, Piergiorgio Cerello, and
Piernicola Oliva, Automatic Lung Segmentation in CT Images with Accurate Handling of the Hilar
Region, J0ournal of Digital Imaging ISSN0897-1889 (Print) 1618-727X (Online)
OI10.1007/s10278-009-9229-11 (2009), 10- 20.
2)Giorgio De Nunzio, Eleonora Tommasi, Antonella Agrusti, Rosella Cataldo, Ivan De Mitri, Marco
Favetta, Silvio Maglio, Andrea Massafra, Maurizio Quarta, Massimo Torsello, Ilaria Zecca, Roberto
Bellotti, Sabina Tangaro, Piero Calvini, Niccolò Camarlinghi, Fabio Falaschi, Piergiorgio Cerello, and
Piernicola Oliva, Automatic Lung Segmentation in CT Images with Accurate Handling of the Hilar
Region, Journal of Digital Imaging ISSN0897-1889 (Print) 1618-727X (Online)
DOI10.1007/s10278-009-9229-11 (2009), 10- 2.
3) G. De Nunzio, A. Massafra, R. Cataldo, I. De Mitri, M. Peccarisi, M.E. Fantacci, G. Gargano, E.
Lopez Torres, Approaches to juxta-pleural nodule detection in CT images within the MAGIC-5
Collaboration, Journal of Digital Imaging, 24(2011), 11-27
Lectures
Analisi delle Immagini
Analisi statistica dei Dati
Programmazione "Object Oriented" in C++
Tecniche fisiche per la biomedicina
Modellistica Numerica e Analisi Dati
Elenco delle Pubblicazioni
Proceedings
1) DE NUNZIO, MASSAFRA A (Ieee Member).., E. TOMMASI, I.DE MITRI, R.CATALDO,
M.FAVETTA, S.MAGLIO. (2008). An innovative lung segmentation algorithm in computed
tomography images with accurate delimitation of the hilus pulmonis . NSS-MIC. Dresden, Germany.
2008.
2) G DE NUNZIO, MASSAFRA A.(Ieee Member)., L. MARTINA, R. CATALDO, S. MAGLIO, M.
QUARTA, A. RETICO, L. BOLANOS. (2008). Lung Uniformization for Juxta-Pleural Nodule Detection.
NSS-MIC. Dresden, Germany. 2008.
3) G. MERCURIO, S. MAGLIO, A. AGRUSTI, M. FAVETTA, MASSAFRA A(Ieee Member).., R.
DEMITRI, A. CAVALLO. (2008). An information management system for distributed proteomic
images: computational GRID technologies or a scale-free network of biobanks . NETTAB. Varenna, Como
Lake. 2008.
4) G. MERCURIO, S. MAGLIO, A. AGRUSTI, MASSAFRA A(Ieee Member).., R. CATALDO, I. DE
MITRI, M. FAVETTA A. MASSAFRA, G. MARSELLA, D. VERGARA, M. MAFFIA. (2008). Network P2P
for exploring and visualization of proteomic data produced by two dimensional electrophoresis . The
21th IEEE International Symposium on Computer-Based Medical Systems. Jyvaskyla, Finland. June
17-19, 2008. (pp. 197-202). WASHINGTON, DC,: IEEE Computer Society (UNITED STATES).
5) R.CATALDO, M.QUARTA, A.AGRUSTI, G.DE NUNZIO, S.MAGLIO, M.E.FANTACCI, F.BAGAGLI,
M.FAVETTA, MASSAFRA A (Ieee Member).., G.MERCURIO. (2008). Annotation of lung-screening
images and 2D-E proteomic analysis for early diagnosis of lung cancer through federated biobanks . The
Sixth International Conference on Bioinformatics of Genome Regulation and Structure. Novosibirsk,
Russia. June 22-28, 2008
Proceedings
6) G.DE NUNZIO, S.MAGLIO, R. DEMITRI, A. AGRUSTI, R. CATALDO, I. DE MITRI, M. FAVETTA, G.
MARSELLA, MASSAFRA A (Ieee Member)., M. QUARTA, AND G. MERCURIO (Ieee Member). (2008).
Integrated Models for the Analysis of Two-Dimensional Electrophoresis Gel Images. Medical Imaging
Conference.
7) Piergiorgio Cerello, Ernesto Lopez Torres, Elisa Fiorina, Chiara Oppedisano, Cristiana Peroni , Raul
Arteche Diaz, Roberto Bellotti, Paolo Bosco, Niccolo Camarlinghi, Andrea Massafra
Improving the Channeler Ant Model for lung CT analysis- Spie 2011
Workshop e Conferenze
1-2 Aprile 2008 Genova: pipeline lessons
3-10 june 2008 Alghero: School on software of nuclear physics
11-13 june 2008 Pisa: workshop on medical imaging
19-26 october 2008 Dresda(Germany): poster
19-20 jennuary 2009 Lecce: Meeting magic5
16-18 julay 2009 Pisa : Talk Comparison between methods for nodule
inclusion
19-24 september2009 London (United Kingdom): MICCAI 2009
28 september 2009 Bari:SIF
talk on lung segmentation algorithm
2 dicember 2009 Genova: Talk on Report of lung segmentation
12-13 may 2010 Lecce: workshop on medical imaging
18-19 september 2010: Pisa workshop on medical imaging
19-23 october 2010 Oporto( Portugal): TMSi conference
Talk on comparison between closing and alpha hull