1 run-length encoding for texture classification dong-hui xu visual computing research seminar cti,...
Post on 21-Dec-2015
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Run-Length Encoding for Texture Classification
Dong-Hui Xu
Visual Computing Research SeminarCTI, DePaul University
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Topics of Discussion
Problem statement Motivation Background Run-Length Matrices and the Eleven Run-
Length features Preliminary Results Future Work References
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Problem Statement
We want to develop a texture vocabulary that defines the different human body tissues in terms of low-level texture descriptors.
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Motivation
Our hope is that our classification of tissues will help radiologists detect irregularities (ex. Tumors) in the tissues of the human body sooner.
Earlier detection can help save lives.
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Background
Q. What is texture?
A. Texture is the term used to characterize the surface of a given object or region. It is described as fine, coarse, grained, smooth, etc,
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Background: Examples of Textures
These images are taken from Brodatz Textures. They are benchmarks that researchers use in order to test if their algorithms are working properly.
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Background
Basic concepts for texture:
Texture primitives – maximum contiguous set of constant-gray-level pixels
Three features can be defined for textures: Tone of texture (Gray-Level) – Based mostly on
pixel intensity properties in the primitive Structure of texture (Direction) – Spatial
relationship between texture primitives Length of the primitive (long = coarse and small
= fine)
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Ways to Characterize Texture
Co-occurrence matrices Discrete Wavelet Transform The Power Spectrum features Run-Length encoding
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Definitions for gray level runs
Galloway proposed the use of a run-length matrix for texture feature extraction
For a given image: A gray level run is defined as
A set of consecutive, collinear pixels having the same gray level
Length of the run is The number of pixels in the run
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Definition of Run-Length Matrices
1 1 2 2 1 13 3 1 1 2 21 1 2 3 1 13 1 2 2 1 11 1 3 2 2 22 3 1 1 2 2
• The run-length matrix p (i, j) is defined by specifying direction. 0 °, 45 °, 90 °, 135 °• and then count the occurrence of runs for each gray levels and
length in this direction(i) Dimension corresponds to the gray level (bin values) and has a
length equal to the maximum gray level (bin values) n(j) dimension corresponds to the run length and has length equal to the
maximum run length (bin values).
j i
1 2 3 4 5 6
1 1 8 0 0 0 0
2 2 4 1 0 0 0
3 4 1 0 0 0 0
0 °
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Definition of Run-length Features
Short Run Emphasis
nr is the total number of runs in the image.M is the number of gray levels (bins)N is the number of run length (bins)The number of runs of pixels that have gray level i and length
group j is represented by p (i, j)
SRE feature measures the distribution of short runs The SRE is highly depend on the occurrence of short runs and
is expected large for fine textures.
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Definition of Run-length Features (Continued)
Long Run Emphasis
LRE feature measures distribution of long runs The LRE is highly depend on the occurrence of long runs and is
expected large for coarse structural textures.
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Definition of Run-length Features (Continued)
Low Gray-Level Run Emphasis
Measures the distribution of low gray level values
High Gray-Level Run Emphasis
Measures the distribution of high gray level values
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Definition of Run-length Features (Continued)
Short Run Low Gray-Level Emphasis
Short Run High Gray-Level Emphasis
Long Run Low Gray-Level Emphasis
Long Run High Gray-Level Emphasis
Measures the joint distribution of run and gray level distribution
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Run-length Features (Continued)
Gray-Level Non-uniformity
Measures the similarity of gray level values through out the imageThe GLN is low if the gray levels are alike through out the image.
Run Length Non-uniformity
Measure the similarity of the length of runs through out the image The RLN is low if the run lengths are alike through out the image.
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Run-length Features (Continued)
Run Percentage
Measures the homogeneity and the distribution of runs of an image in a given direction.
The RP is the highest when the length of runs is 1 for all gray levels.
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ResultResult
Run-length features for one slice:
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Results
Run run-length application on segmented images and the four quadrants of the segmented images
4 directions (0°, 45°, 90° and 135°) calculate 11 descriptors from the run-
length matrices
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Results (Backbone - Sample)
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Results (Backbone_P1)
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Results (Backbone)
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Results
Correlation Coefficients for Run-Length Descriptors
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Future Work
Investigate run-length matrices for volumetric data
Run run-length application over more patient images.
Use neural networks and statistic analysis technique to identify patterns for each organ.
Build a texture vocabulary that defines the different human body tissues in terms of low-level texture descriptors.
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References
S.A. Karkanis On the Importance of Feature descriptors for the Characterisation of Texture.
Xiaoou Tang Texture Information in Run-Length Matrices