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Texture
ļ® Texture is regarded as what constitutes a macroscopic region. Its structure is simply attributed to the repetitive patterns in which elements or primitives are arranged according to a placement rule(Tamura et al, 1978).
ļ® Texture is both the number and types of its (tonal) primitive and their spatial arrangement (Haralick ,1979).
ļ® The term texture generally refers to repetition of basic texture elements called texels. The texel contains several pixels, whose placement could be periodic, quasi-periodic, or random. Natural textures are generally random, whereas artificial textures are often deterministic or periodic. Texture may be course, fine, smooth, granulated, rippled, regular, irregular, or linear (Jain, 1989).
ļ® Texture is intuitively viewed as descriptor in providing a measure of properties such as smoothness, coarseness, and regularity (Gonzales and Woods, 1990).
ļ® Texture is an attribute representing the spatial arrangement of the gray levels of the pixels in a region (IEEE, 1990).
ļ® Texture is both grey level of a single pixel and its surrounding pixels, which was coined as a unit texture, texels. These texels conformed repetitive patterns that dictated the effective texture analysis approach (Karu et al, 1996).
a. Patterns which characterize objects are called texture in image processing (JƤhne, 2005).
Technical Definition
1. Texture has no single definition.
2. Definitions from previous literature dedicated in
studying texture
3. The first three definitions, tells us texture is composed
of a building block that is spatially arranged based on
the placement rule (periodic, quasi periodic, or
random): like a brick a single brick is the building block,
the arrangement of the bricks that gives rise to a
texture of a brick wall
4. Texture is descriptors for smoothness, coarseness, and
regularity
5. In computer vision
6. Spatial arrangement of gray levels of the pixel
7. Pattern
1. As one of the hypothesis. Texture characterization emanate from
visual system closely emulates experts
2. Neuroscientist, studied perception of texture
3. Before disproving, he conjectured that second-order statistics is
processed in the vision system, and He claimed that two textures
with similar second-order statistic is not pre-attentively recognizable.
4. In other words without close inspections, two different texture with
same sec stat would seem to look similar.
5. After series of experiments, he finally suggested that textons are the
major player for texture discrimination.
6. And the textons are contrast, terminators. granularity
Texture discrimination
Second-order statistics Textons
Textons instead of second-order statistics that cause the
texture discrimination
Texture and Human Vision System
Frequency and Orientation
ļ® Multi-frequency and orientation analysis
decomposition (1968) āCampbell and Robson
ļ® Simple cells of the visual cortex respond to narrow
ranges of frequency and orientation, cells act as 2D
spatial filter-(1982) De valois et al.
ļ® Orientation-based texture segregation involves the
generation of a neural representation of the surface
boundary whose strength is nearly independent of the
magnitude of orientation contrast - Motoyoshi and
Nishida (2001)
ļ® More studies had been conducted in part to understand human vision.
ļ® This Campbell and Robson found that when signal received by the eye is
decomposed into multiple frequencies and orientation
ļ® Another work in the subsequent year that further support the previous
finding that simple cells are highly selective/tuned to narrow frequency
and orientation.
ļ® Another work found that neural representation of texture boundary is
formed that is independent of magnitude and orientation of the contrast
ļ® In this work in wavelet analysis will be used for segmentation. Frequency
and contrast
Texture
Method Design
ļ® Creation of relative color
images
ļ® Segmentation and
morphological filtering
ļ® Relative color feature
extraction
ļ® Design of tumor feature
space and object feature
space
ļ® Establishing statistical
models from relative color
features
Create Relative Color Skin
Tumor Images
ļ® Purpose
ļ® to equalize any variations caused by lighting, photography/printing or digitization process ļ® to equalize variations in normal skin color between individuals ļ® the human visual system works on a relative color system
ļ® Algorithm
ļ® Mask out non-skin part in the image to calculate the normal skin color ļ® Separate tumor from the image ļ® Remove the skin color from the tumor to get a relative color skin tumor image
ļ® CVIPtools functions were used to create
relative color skin tumor images
Calculate Skin Color
Original Noisy Skin Tumor Image
Non-skin Algorithm
Calculate
Mask out tumor
Skin Tumor Image W/O Noise
Average
R, G, B Value
of Skin
Skin-Only Image
Relative Color Tumor Image
SUBTRACT
Tumor Image
Average R, G, B Value of Skin
Relative Color Image of the Tumor
Segmentation and
Morphological Filtering
ļ® Image segmentation was used to find regions that represent
objects or meaningful parts of objects
ļ® Morphological filtering was used to reduce the number of
objects in the segmented image
ļ® Easy to use CVIPtools for experimenting and analysis
