Run-Length Encoding for Texture Classification Dong-Hui Xu Visual Computing Research Seminar CTI, DePaul University

Topics of Discussion Problem statement Motivation Background Run-Length Matrices and the Eleven Run-Length features Preliminary Results Future Work References

Problem Statement We want to develop a texture vocabulary that defines the different human body tissues in terms of low-level texture descriptors.

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.

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,

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.

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)

Ways to Characterize Texture Co-occurrence matrices Discrete Wavelet Transform The Power Spectrum features Run-Length encoding

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

Definition of Run-Length Matrices 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 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 8 1 1 2 2 1 1 3 3 1 1 2 2 1 1 2 3 1 1 3 1 2 2 1 1 1 1 3 2 2 2 2 3 1 1 2 2 0 °

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.

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.

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

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

Run-length Features (Continued) Gray-Level Non-uniformity Measures the similarity of gray level values through out the image The 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.

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.

Result Run-length features for one slice:

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

Results (Backbone - Sample)

Results (Backbone_P1)

Results (Backbone)

Results Correlation Coefficients for Run-Length Descriptors

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.

References S.A. Karkanis On the Importance of Feature descriptors for the Characterisation of Texture. Xiaoou Tang Texture Information in Run-Length Matrices