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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