5th Intensive Course on Soil Micromorphology Naples 2001

5th Intensive Course on Soil Micromorphology

Naples 2001

12th - 14th September

Image Analysis

Lecture 9

Grey-Level Morphology and Multi-Spectral

Methods

5th Intensive Course on Soil Micromorphology - Naples 2001Image Analysis - Lecture 9: Grey-Level Morphology

Multi-Spectral Methods for Segmentation/Classification

useful where X-ray spectra of different colour information (e.g. RED/GREEN/BLUE/ U-V) information is available

Part 1

Part 2

Extension of Binary Morphology to Grey-Level Images

avoids need to segment images


•Segmentation (usually by thresholding) and attendant problems

•Erosion involves stripping pixels from edge of foreground areas according to selected criteria

•Dilation involves adding pixels to foreground areas

•Opening involves one cycle of erosion followed by one cycle or dilation

roughness aspects of feature are not recovered, no are particles smaller than 2 pixels

•Closing is the reverse of Opening.

Binary Morphology requires

Grey Level Morphology

• attempts to solve problems of BINARY MORPHOLOGY

by removing need for thresholding

• Grey Level Erosion replaces all intensities within a given

mask area by the minimum value in that area

• Grey Level Dilation replaces all intensities within a given

mask area by the maximum value in that area

• A grey level opening involves an erosion and a dilation phase

As with binary morphology, roughness is lost and features tend to become rounded until they finally disappear


Representation of binary morphology for feature sizing



Schematic of Intensity Profile along a line


Start of Erosion along line


Intensity lost after grey-level erosion (blue)


Intensity lost after grey-level erosion followed by dilation

Blue: Intensity lost: Green: Intensity recovered in dilation


Intensity lost after grey-level erosion of diameter 5

Cyan: New Intensity lost


Intensity lost after grey-level erosion followed by dilation(diameter 5)

Blue/ Cyan: Intensity lost: Green: Intensity recovered in dilation


Intensity lost after grey-level erosion of diameter 7

Purple: New Intensity lost


Intensity lost after grey-level erosion followed by dilation(diameter 7)

Blue/ Cyan/Purple: Intensity lost: Green: Intensity recovered in dilation

Effect of grey-levelopening at different radii


a) Radius 9 pixels

b) Radius 10 pixels

c) Difference Image

d) Complete particle loss


Particle size analysis using grey-level morphology

1

2

3

4


Core sample taken from estuary model.

[photograph courtesy of J.Alexander]


Halimeda needles from Great Barrier Reef


Halimeda needles from Great Barrier Reef - partly covered by nanograins


Halimeda needles from Great Barrier Reef - fully covered by nanograins


Question: Are nanograins biological or chemical in origin?

Evidence suggests nanograins increase in size with coverage - hence favouring chemical argument.


If particles lost at each radius are stored and finally added (B) - the resulting image should be comparable to original (A).

Except: All particles are reduced to their equivalent circular

diameter.

•Allows alternative methods for segmentation

•Enables separation of different mineral classes.

•Can be used in combination with Orientation Analysis as a combination method to overcome problem of large particles

5th Intensive Course on Soil Micromorphology - Naples 2001Image Analysis - Lecture 9: Multi-Spectral Analysis

Requirements:

Two or more images of same area at same magnification and pixel resolution and in exact registry.

Must be collected with different physical parameters - e.g. wavelength

Multi-Spectral Analysis

5th Intensive Course on Soil Micromorphology - Naples 2001Image Analysis - Lecture 9: Multi-Spectral Analysis

Examples:

Optical Microscopy:

•Red / Green / Blue images

•UV.

Electron Microscopy:

•Secondary Electron

•Back Scattered Electron

•Cathodoluminescence

•X-Ray Maps.

Requirements continued:

5th Intensive Course on Soil Micromorphology - Naples 2001Image Analysis - Lecture 9: Multi-Spectral Methods

Require 2 or more different images of same area

must be in exact registry

e.g. Optical Microscope

RED/GREEN/BLUE/UV

Or SE / BSE Image and CL or various X - Ray Maps in SEM

Multi-Spectral Methods

Hong Kong Marine Clay from M1 unit approximately 1m above upper most palaeo-desiccated layer. BSE Image


Hong Kong Marine Clay



BSE Image X-Ray Maps


From N images

and

Statistics from M classes

Output segmented image may be obtained.

Accuracy in segmentation relies on identification of suitable classes, and also sufficient classes


Are these two particles the same material?Classification was set at 98% confidence and some post-processing was done to produce classified image.

Procedure of segmentation is know as Mineral-Segmentation



large voids 2.21% matrix 73.49% matrix in aggregate 14.46% quartz 5.27% feldspar 2.70% chalk 0.64% rutile 0.15% magnetite 0.65% pyrite 0.43%


Particle Size Distribution for different mineral species


Binary Mask to assess orientation in matrix outside aggregate. Large mineral grains and voids are black as is aggregate.

Binary Mask to assess orientation in matrix inside aggregate.

Use Mineral Segmented image to generate binary masks.

Domain Segmentation of Matrix


Hong Kong Marine Clay

a) Matrix orientation c) Quartz grain orientation e) Weighted Quartz grain orientation

b) Aggregate orientation

d) Feldspar orientation f) Weighted Feldspar orientation



Index of Anisotropy

outside aggregate: 0.229

inside aggregate: 0.374

In both cases the predominant orientation is nearly vertical.

Vertical direction in field.


When does a particle warrant separate identification from matrix?

- depends on pixel resolution/magnification.

In supervised classification it is helpful to avoid “forced” classification as this will identify features / minerals which may have been missed.

Some post-processing of image in needed following Mineral-Segmentation to remove noise etc.

Concluding Remark on Multi-spectral Analysis.

5th Intensive Course on Soil Micromorphology Naples 2001

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