Transcript Document

MAZDA – A SOFTWARE
FOR TEXTURE ANALYSIS
Piotr M. Szczypinski, Michal Strzelecki, Andrzej Materka
Institute of Electronics, Technical University of Lodz,
Wolczanska 211/215, 90-924 Lodz, Poland
International Symposium on Information Technology Convergence, Jeonju, Korea, 2007
Functionality
A MaZda:
– is a software package for 2D and 3D image texture analysis
– computes a variety of textural features within arbitrarily shaped regions
of interest
– computes feature maps of images
– performs statistical analysis of computed feature sets
– aids in image texture classification
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Functionality
MaZda has been under development since 1998, to satisfy the needs
of the participants of COST B11 European project "Quantitative
Analysis of Magnetic Resonance Image Texture" and the subsequent
COST B21 "Physiological modelling of magnetic resonance image
formation". The program code has been written in C++/Delphi and
compiled for PC computers that use MS Windows® XP operating
system.
[MaZda Manual]
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The long story short
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development started in 1996 with the Mammo program
(Łódź-Warszawa)
combining procedures from NMRWin (DKFZ-Heidelberg)
in 1999 b11, a computer program for data analysis, classification,
and vizualisation was developed as a complementary software to
MaZda
1998-2002 development within COST B11,
2004-2007 COST B21 project
aimed at analysis of magnetic resonance images texture
The name of the program is an acronym derived from ‘Macierz Zdarzen’
that is Polish counterpart of the English term ‘co-occurrence matrix’.
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Texture
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perceived by humans as complex patterns composed of
spatially organized, repeated subpatterns, which have
characteristic somewhat uniform appearance
carries substantial information about the structure of physical
objects – analysis is an important issue in image processing
and understanding
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Analysis pathways
Input data
2D grayscale image
MaZda
2D color image
3D image data
selection of color channel
2D visualization
3D visualization
defining regions of interest
computation of feature vectors
computation of feature maps
data aggregation
feature selection
b11
feature space reduction
image segmentation
data classification
features space visualization
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Image loading
Gray-scale images formats to load:
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Siemens NUMARIS 2 and 3
Siemens Vision
ACR NEMA
GE Advantage
GE IDBM
IGE – YMS
Bruker Aspect 3000
Picker
Dicom
Windows Bitmap
Unformatted 8 or 16 bits/pixel
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3D image loading
Loading:
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Window bitmaps
3D Bmf format
3D Dicom data
View adjustment:
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cross-sections selection
adjustment of angles
zoom
gray-scale window
gray-scale thresholds
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Region of interest
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Region of interest (ROI) is a set of pixels in 2D image or voxels in 3D
image selected for processing.
ROIs concentrate computation effort on image fragment that is relevant
to a goal of computation and thus helps avoid processing of
unnecessary image fragments.
2D ROI Editor in MaZda
a) window title bar, b) menu bar, c) image panel, d) load file
button, e) copy and move buttons, f) graphics toolbar for ROI
edition, g) morphological tools for ROI edition, h) drawing mode
selection buttons, i) ROI color selector, j) ROI on/off switches,
k) zoom in/out buttons, l) sliders for adjustment of grey-scale
palette, m) image view mode selector, n) status bar
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3D ROI Editor
Defining ROI with interactive tool of
elastic surface
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ROI found with flood-fill algorithm
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Textural features computation
Options for the analysis
Input image
(2D or 3D) with
the defined ROIs
(selection of feature groups to
compute, algorithms parameters,
image normalization options, etc.)
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Resulting list of textural features
(columns of the report correspond
with the defined ROIs)
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Feature lists analysis
A Goal:
Finding a way
of texture classification
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Tools:
– Combining reports for further analysis
– Defining class names of regions (columns)
– Selection of most discriminative features
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Feature selection
1.The number of features computed by MaZda
may reach several-hundred per region, which is
difficult to handle.
2.The several-hundred features turns into the
problem of analysis of a several-hundreddimensional space => statistically not reliable.
3. Usually only a limited number of features carry
relevant information needed for texture
discrimination.
MaZda allows for selection of most effective
features and rejection of the others.
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Feature space visualization
selected
feature
names
names of
classes
List of selected features loaded
into b11 module. The features are
computed for textures of two
different classes.
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Visualization of feature
space in b11 module.
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Feature analysis & classification
Feature analysis:
Feature classification
• Principal Component Analysis
• 1 – NN classifier
• Linear Discriminant Analysis
• Artificial neural network (training/testing)
• Nonninear Discriminant Analysis
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Feature Clustering
Clustering
Segmentation
• k-means
• Agglomerative Hierarchical Clustering
• Similarity Based Clustering
• k-means
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Applications
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brain research - amygdale activation detection
research of hipocampal sclerosis
investigation of healthy and cirrhotic livers
textural analysis of trabecular bone - osteoporosis detection
monitoring of atrophy and regeneration of muscles
monitoring of teeth implants
assessment of cellular necrosis in epithelial cells
evaluation of anti-vascular therapy of mammary carcinomas
discrimination between cooked and raw potatoes and potato varieties
analysis of apple ripening process
MRI image analysis of soft cheeses
The MaZda package has proven to be an efficient and reliable set of
software tools for analysis of textured images.
download:
http://www.eletel.p.lodz.pl/merchant/mazda/order1_en.epl
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