The application of Object Based Image Analysis to ...formatex.info/microscopy4/1526-1532.pdf · The application of Object Based Image Analysis to Petrographic Micrographs ... classification

The application of Object Based Image Analysis to Petrographic

Micrographs

R. Marschallinger and P.Hofmann

GIScience Research Institute, Austrian Academy of Sciences, Schillerstr. 30, A-5020 Salzburg, Austria

In this paper, we describe the application of Object Based Image Analysis for the knowledge-based, automated mineral

classification from petrographic micrographs. Digital color images, acquired by an optical, petrographic microscope at

different stage rotations and with parallel and crossed polarization filters, are the input to a sequence of different image

segmentation and fuzzy classification steps. As compared with traditional, pixel-based algorithms, Object Based Image

Analysis incorporates not only the spectral characteristics of various mineral phases, but also their topological and genetic

properties, resulting in superior image classification results. By means of rule sets, image analysis can be flexibly adapted

to different rock types.

Keywords Petrographical Microscopy; Petrographical Image Analysis; Object Based Image Analysis

1. Introduction

Petrographic thin section microscopy, based on transmitted as well as on reflected light, has a long tradition both in

applied and academic geosciences. A broad spectrum of microscopic techniques for the identification of rock-forming

minerals and ore minerals has been established over the past century [1]. As by education and experience, a geoscientist

can straightforwardly identify a rock’s constituent mineral phases, quantify fabric parameters and infer a rock’s genesis,

involving just rock thin sections and a petrographic microscope [2,3]. Despite these obvious advantages, there are

inherent drawbacks: petrographic microscopy is a time-consuming, iterative approach that involves expert knowledge

and lots of experience in combining multiple, mostly “soft” optical classification criteria (compare Table 1); optical-

based quantification of mineral chemistry is unreliable unless impossible and an optical microscope’s magnification

range is quite limited. With a palette of microscope add-ons like polarization filters, slots, specialized lens systems,

apertures and the application of associated microscopy “tricks”, petrographic microscopy is a typical expert domain that

has a tendency to yield irreproducible results. Although textual descriptions of micro-petrographical findings remain

important, the need for more quantitative and reproducible data from optical microscopy has long been recognized [4].

Digital image analysis and image classification applied to petrographic micrographs, in part necessitating sophisticated

hardware [5], have been important steps towards quantification. Today, petrographic image analysis systems work with

pixel-based image analysis algorithms [6]; they perform reliably in selected fields, e.g. for the automatic extraction of

reservoir rock properties [7,8]. However, routinely applied to magmatic or metamorphic rocks, pixel-based algorithms

commonly fail: on the one hand, there are overlapping RGB characteristics of the constituent minerals, on the other

hand, it is impractical to abstract the before-mentioned, multi-criteria expert handling by means of traditional, pixel-

based image analysis methods, because these address mainly the spectral characteristics of the minerals in a

petrographic thin section.

2. Material

For demonstrating our Object Based Image Analysis approach to the automatic classification of petrographic

micrographs, we choose a Metatonalite from the Pennine basement of the Tauern Window (Eastern Alps, Austria). The

sample has been selected because of its rich set of textural features that are directly linked to the rock’s history: the

Metatonalite has undergone a two-stage evolution covering the late Variscan, intrusive emplacement and the younger

Alpidic metamorphism [9,10]. The rock fabric mirrors this history (Fig. 1): on the one hand, the primary magmatic

texture with idiomorphic Plagioclases, Biotite clots, Quartz aggregates and minor occurrence of Potassium Feldpars has

been preserved. On the other hand, in the course of the Alpidic regional metamorphism, the rock has been re-

equilibrated to greenschist facies pressure-temperature conditions: originally Ca-rich, magmatic Plagioclases have been

transformed to Albite/Oligoclase, with concurrent growth of small Epidote/Clinozoisite and White Mica minerals inside

the Plagioclases. The distribution of Epidote minerals portrays an original, magmatic chemical zoning of the

Plagioclases. Biotite has been transformed to metamorphic greenish-brown variants, unmixing the titanium component

as Titanite. Almandine-rich Garnet is attributed to the Alpidic event, too. Accessories are Zircon, Apatite and Ore

minerals.

Microscopy: Science, Technology, Applications and Education A. Méndez-Vilas and J. Díaz (Eds.)

1526 ©FORMATEX 2010

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Fig. 1 Petrographic photomicrographs of Metatonalite H343, identical views, with different rotation of polarization filters. Upper

row: plan parallel light, lower row: crossed polarizing filters. Polarizing filters have been rotated by 45° between left and right

columns. RGB channel histograms refer to upper right image. They lack explicit peaks that can be related to dedicated mineral

phases; therefore, traditional pixel-based image classification methods fail. See text for details.

Fig. 1 highlights some petrographical features of Metatonalite H343: in plan-parallel light (upper row), Quartz clusters,

Plagioclase with Epidote minerals, greenish-brown Biotite and Garnet grains are visible. With crossed polarizers (lower

row) and using different polarizing filter rotations, individual Quartz and Plagioclase grains as well as White Mica can

be discerned. As visible in the RGB channel statistics (insert to upper right image), none of the histograms shows

distinct peaks that can be unambiguously related to the observed mineral phases. For example, the small peaks at low

RGB intensities relate to Biotite; however, also part of the pixels of general grain boundaries and cracks in Garnet or the

Epidote minerals do have similar RGB intensities. The overlapping spectral characteristics of most rock-forming

minerals makes traditional, pixel-based image classification algorithms fail, be it automatic or supervised approaches,

hard or soft classifiers. This is why, for a robust and automatic mineral classification from petrographical microscopy

images, an approach that mimics the traditional petrographic thin section analysis is necessary. A flexible combination

of spectral, morphological and contextual (i.e., geological) information is crucial, with an automation scheme that

allows for the incorporation of expert knowledge while maintaining reproducibility. Object based image analysis

(“OBIA”) is a timely candidate that provides the above-mentioned characteristics.

3. Image Acquisition and pre-processing

3.1Petrographic micrographs

The Metatonalite was prepared according to petrographic standard: the rock sample was mounted on a microscope slide,

ground down to a thickness of 20µm, and protected by a cover slip. We used a Zeiss petrographic microscope with an

attached Sony CCD camera for acquiring the images. The microscope is equipped with a stage that allows rotation. A

set of polarizing filters enables the investigation with plan polarized light as well as with crossed (90°) polarizers. With

that equipment, the optical properties of rock-forming minerals have been acquired; these serve as discriminative

criteria for the following OBIA based petrographical image classification. For the sake of clarity, external knowledge

on mineral color ranges with parallel and crossed polarizing filters, mineral morphology, typical inclusion types and

topological relationships is reproduced in Table 1.


©FORMATEX 2010 1527

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Quartz Plagioclase K-Feldspar Biotite Garnet Epidote White

mica

Opaque

(Ore)

Colors //

polarizers

White White to light-

greenish

White Brownish-

green

Yellowish-

white

+/- White White Black

Colors X

polarizers

White-dark Grey

White-dark Grey White-dark Grey

3rd-order biref. colors

Black 1-2nd order biref.colors

2nd-order brief. colors

Black

Shape &

size

Xenomorph

rel. large

Hypidiomorph

rel. large

Xenomorph

rel. large

Hypidiomorph

elongated

Hypidiomorph

compact shape mixed sizes

Xenomorph

microlithes

Xenomorph

small

Hypdiom.

mixed size

Inclusions none Epidote & Wh.

mica microlites

Quartz,

Plagioclase

Titanite,

Opaque

none none none none

Topology

& other

Monomin. groups

Twin lamellae Microcline grid

Cleavage Accented cracks Microlithes within

plagioclase

Cleavage Within Biotite

Tab1e 1: Diagnostic microscopic characteristics of mineral types in Metatonalite H343.

Browsing Table 1, the overlapping optical characteristics and the “softness” of many of these diagnostic criteria is

apparent (e.g. color and birefringence ranges per mineral, shape definitions like “hypidiomorphous” or

“xenomorphous”). For incorporating a larger range of diagnostic optical data per mineral in sample H343, we captured

the images of five microscope stage positions, successively rotated by 22.5 degrees, each with crossed and parallel

polarizers. Thus, we could include data on Quartz and Plagioclase grain boundaries and sub-grains as well as data on

the birefringence of White Mica (compare Fig. 1).

3.2 Image co-registration

In order to analyze all polarizations and all rotation angles of the probe simultaneously, a layer stack with geometrically

corrected images was generated. The images were co-registered with clearly identifiable control points in each image.

For the parallel polarized images a polynomial model of 3rd

order with an individual number of control points ranging

from 11 to 18 was used. All images were registered to the image taken at a stage rotation of 0°, using a cubic

convolution. The cross-polarized images were co-registered similarly, however, for three of four images not enough

control points for a higher order polynomial correction could be identified. Consequently, an affine transformation was

chosen. From the co-registered images a respective image stack for parallel and crossed polarisation mode was

generated. The cross-polarized image stack was registered to the parallel polarized. For this final co-registration process

a polynomial model of 3rd

order was used. Finally, an image stack consisting of all polarisation modes and all rotation

angles has been generated.

4. Object based image analysis

4.1 Principles and methods

When analyzing the content of an image by means of OBIA, image objects need to be generated by arbitrary

segmentation and assigned semantically to real-world objects of interest. That is, image objects have to be generated

and classified according to their physical (color, shape and texture), spatial (location, neighbourhood, distances etc.) and

scale (structures and embedding) properties. This is usually done by describing the spatial and semantic

interrelationships among the intended object classes in terms of a semantic net [11] or a respective ontology [12, 13, 14]

and their expected or measured physical properties in the image. This way human domain expert knowledge is

conceptualized and made explicit for image analysis purposes. To formulate uncertainty and vagueness, methods of

fuzzy-logic and fuzzy-set-theory can be incorporated [15, 16, 17]. Recent developments in OBIA use domain expert

knowledge to describe and store structural knowledge in terms of class descriptions and class hierarchies; procedural

knowledge is stored to control segmentation and (re-)classification steps. This results in an iterative, stepwise enhanced

analysis process which is also known as “rule base” or “rule set” [18, 19]. For this article, the software package

eCognition 8 (www.definiens.com) has been used, which offers the “cognition network language” (“CNL”, [20]) for

rule set creation and image analysis. The software enables a variety of segmentation methods which generate a

topologically consistent and hierarchically structured net of image objects automatically. That is, each image object is

logically linked to its neighbour-objects, to its scale-hierarchical sub-objects and to its super-object. This way, scale-

hierarchical interrelationships [21] can be used for image analysis, as well as structural differences between classes. As

an example, the Epidote minerals are sub-objects of Plagioclase in our Metatonalite. When using CNL, all steps of

image processing and analysis are organized as so-called “processes”, whereas the processes can be organized

hierarchically. Additionally, a variety of programming-language-like mechanisms, such as variables, loops etc. are

available, which can all be parts of a process. Each process can be applied on dedicated objects fulfilling customizable

conditions - in the nomenclature of CNL a “domain”. That is, a process can be applied on all objects, just on objects of



______________________________________________

a certain class and/or fulfilling certain conditions. As an example, a process can be applied on all objects of a class

which are brighter than a certain threshold. This way it is possible to stepwise enhance the segmentation and

classification results from the initial segmentation by applying different processes on different “domains”.

4.2 Describing the ontology and synopsis

Describing the ontology for image analysis purposes usually starts with a relatively rough description of the intended

classes of image objects. That is, describing their physical properties (spectral, textural and shape properties) and their

spatial interrelationships (neighbourhood relationships and/or scale

differences). In the present case the fo

Biotite, Garnet, Plagioclase, White Mica, Epidote minerals and Opaque phases

4.3 Initial segmentation

Since each object based image analysis starts with an initial,

start with a multi-resolution segmentation [22]. This method generates image objects based upon their color

homogeneity. The average size of objects is determined by the so

“scale parameter” is chosen, the larger the objects are generated. Since for the differentiation of Quartz, Plagioclase,

Biotite and Garnet textural and/or structural features might be necessary, we decided to generate two

levels: on the top-level the generally larger objects mentioned before should be represented, and on the lower level

smaller objects representing White Mica and Epidote minerals (= Plagioclase sub

the initial multi-resolution segmentation we applied

Table 2: Segmentation parameters used for initial multi resolution segmentation.

Segmentation level Scale parameter

Top-level 70

Base-level 10

For both segmentation levels most of the intended objects were somewhat over

objects were represented by more segments than necessary to describe their outline (Fig. 2). This segmentation strategy

allows for enhancing the object outlines by merging neighbouring, similarly classified objects. Also, in the subseq

analysis process, grow-and-shrink-methods can be applied more efficiently. For the initial segmentation all layers of the

layer stack (i.e., the RGB-channels of all polarisation modes and rotation angles) were used with equal weighting. In

subsequent, dedicated re-segmentations (see next section) we used the cross

Fig. 2 Initial multi resolution segmentation of the top

4.4 Rule set

A CNL rule set for image analysis is organized in so

program-language-like manner (see section 4.1). We started with an initial multi

the section before. On this initial segmentatio

(definitions in Table 1) in a fuzzy manner. For this purpose we referred to features describing the RGB

the parallel- and cross-polarized layers for each rotation angle

at a given rotation angle, the higher is the color

lass and/or fulfilling certain conditions. As an example, a process can be applied on all objects of a class


al segmentation by applying different processes on different “domains”.

Describing the ontology and synopsis


. That is, describing their physical properties (spectral, textural and shape properties) and their

spatial interrelationships (neighbourhood relationships and/or scale-hierarchical relationships, such as structural

differences). In the present case the following classes (i.e., major mineral phases) can be visually identified: Quartz,

ica, Epidote minerals and Opaque phases (Table 1).

Since each object based image analysis starts with an initial, more or less knowledge-free segmentation, we decided to

resolution segmentation [22]. This method generates image objects based upon their color

homogeneity. The average size of objects is determined by the so-called “scale parameter”, whereas the higher the


Biotite and Garnet textural and/or structural features might be necessary, we decided to generate two

level the generally larger objects mentioned before should be represented, and on the lower level

smaller objects representing White Mica and Epidote minerals (= Plagioclase sub-elements) should be generated. For

resolution segmentation we applied parameters as depicted in Table 2.

Segmentation parameters used for initial multi resolution segmentation.

Scale parameter Color vs. Shape Compactness vs.

Smoothness

Color 0.9,

Shape 0.1

Compactness 0.4,

Smoothness 0.6

Color 0.9,

Shape 0.1

Compactness 0.5,

Smoothness 0.5

For both segmentation levels most of the intended objects were somewhat over-segmented, that is,


allows for enhancing the object outlines by merging neighbouring, similarly classified objects. Also, in the subseq

methods can be applied more efficiently. For the initial segmentation all layers of the

channels of all polarisation modes and rotation angles) were used with equal weighting. In

segmentations (see next section) we used the cross-polarized layers only.

Initial multi resolution segmentation of the top- (left) and base- (right) level, both with black segment outlines.

nalysis is organized in so-called processes, whereas the process

like manner (see section 4.1). We started with an initial multi-resolution segmentation as outlined in

the section before. On this initial segmentation, we applied a classification scheme describing the intended classes

1) in a fuzzy manner. For this purpose we referred to features describing the RGB

polarized layers for each rotation angle - the more bluish an object appears in a given polarisation

at a given rotation angle, the higher is the color-ratio in the blue channel. Additionally, we referred to features

lass and/or fulfilling certain conditions. As an example, a process can be applied on all objects of a class


al segmentation by applying different processes on different “domains”.


. That is, describing their physical properties (spectral, textural and shape properties) and their

hierarchical relationships, such as structural

llowing classes (i.e., major mineral phases) can be visually identified: Quartz,

free segmentation, we decided to

resolution segmentation [22]. This method generates image objects based upon their color- and shape-

arameter”, whereas the higher the


Biotite and Garnet textural and/or structural features might be necessary, we decided to generate two segmentation

level the generally larger objects mentioned before should be represented, and on the lower level

elements) should be generated. For

Segmentation parameters used for initial multi resolution segmentation.

Layer weights

All equal

All equal

segmented, that is, most of the intended


allows for enhancing the object outlines by merging neighbouring, similarly classified objects. Also, in the subsequent

methods can be applied more efficiently. For the initial segmentation all layers of the

channels of all polarisation modes and rotation angles) were used with equal weighting. In

only.

(right) level, both with black segment outlines.

e process itself is arranged in a

resolution segmentation as outlined in

n, we applied a classification scheme describing the intended classes

1) in a fuzzy manner. For this purpose we referred to features describing the RGB-color-mixing of

the more bluish an object appears in a given polarisation

ratio in the blue channel. Additionally, we referred to features



______________________________________________

describing the smoothness or roughness

the object’s texture we compared the area of an object at the top

objects at the level below (see Eq. (1)).

already existing ones (so-called customized features). The color

spectral mean values of an object in the RGB

ratio of the blue layer in all cross-polarized images (mean_ratio_x_blue) or the brightness of all cross

(brightness_x). The texture-describing feature (SubAreaIndex) was calculated as follows:

SubAreaInd

With: )( 0LObj

Area the area of the object at the top

the object’s sub-objects at the base-level and 0

In other words, the lower SubAreaIndex

4.4.1 Describing classes as fuzzy

For “translating” the expert knowledge as depicted in Tab

by appropriate fuzzy membership functions referring to the fea

Table 3 shows the descriptions of Biotite as a fuzzy set. The fuzzy

(minimum) as well as fuzzy-or (maximum) operators: for an expression containing a

class membership is given by the membership function leading to the lowest degree of membership. Membership

functions combined with a fuzzy-or-operator return a membership value equal to the membership function with the

highest degree of membership. Fuzzy expressions combined with fuzzy

Table 3).

Table 3

Class and description

Biotite

Border Index

Mean_ratio_x_green

Mean_ratio_x_red

Mean_ratio_p_green

Mean_ratio_p_red

Mean_ratio_p_blue

Mean_ratio_x_blue

The classes Plagioclase, Garnet and Quartz were described comparably, whereas for these classes the feature

SubAreaIndex (see section above) has been used to describe their textural homogeneity / heterogeneity. Applying the

fuzzy set descriptions to the initial image segmentation

multi resolution segmentation as depicted in Fig. 3.

4.4.2 Enhancing initial results

In order to detect White mica (partially bluish and reddish sub

at the base-level segmentation, this level was re

polarized layers were skipped because they lack relevant spectral differences.

White mica: the feature “Existence of super object plagioclase (1)

White mica to be a sub-element of Plag

function to express the binary relation of (non

1 (1) indicates the number of the super-levels relative to concerned level

describing the smoothness or roughness of object borders (border index) and their roundness (elliptic fit). To describe

the object’s texture we compared the area of an object at the top-segmentation-level with the average area of its sub

objects at the level below (see Eq. (1)). In eCognition it is possible to generate new object

called customized features). The color-description features were created by referring to the

spectral mean values of an object in the RGB-layers for given polarisation and rotation angles. Examples are the mea

polarized images (mean_ratio_x_blue) or the brightness of all cross

describing feature (SubAreaIndex) was calculated as follows:

))((

))((

0

)()(

0

)()(

10

10

LObjAreaArea

LObjAreaAreaexSubAreaInd

LObjLObj

LObjLObj

−

−

+

−

= (1)

the area of the object at the top-level measured in pixel, ()( 1 ObjArea

LObj −

level and 0 ≤ SubAreaIndex ≤ 1.

SubAreaIndex, the more homogeneous an object’s texture and vice versa.

Describing classes as fuzzy sets

For “translating” the expert knowledge as depicted in Table 1 into a set of fuzzy classes, each class has been described

by appropriate fuzzy membership functions referring to the features outlined in section 4.1. The example outlined in

3 shows the descriptions of Biotite as a fuzzy set. The fuzzy-rules for each feature can be combined by fuzzy

or (maximum) operators: for an expression containing a fuzzy-


operator return a membership value equal to the membership function with the

ghest degree of membership. Fuzzy expressions combined with fuzzy-and and fuzzy-or operators can be

Table 3: Biotite described as fuzzy set for image analysis

Feature Lower value border

Border Index 0.7

Brightness 100

Mean_ratio_x_green 0.2

Mean_ratio_x_red 0.2

Mean_ratio_p_green 0.2

Mean_ratio_p_red 0.2

Mean_ratio_p_blue 0.3

Mean_ratio_x_blue 0.3

Garnet and Quartz were described comparably, whereas for these classes the feature


fuzzy set descriptions to the initial image segmentation (see section 4.2) leads to the initial classification result of the

multi resolution segmentation as depicted in Fig. 3.

Enhancing initial results

In order to detect White mica (partially bluish and reddish sub-elements of Plagioclase in cross

level segmentation, this level was re-segmented based upon the cross-polarized layers only. The parallel

ere skipped because they lack relevant spectral differences. Table 4 shows the class description of

White mica: the feature “Existence of super object plagioclase (1)1” refers to the scale-hierarchical relationship of

element of Plagioclase. The fuzzy-membership function has been expressed as a singleton

function to express the binary relation of (non-) existence (Fig. 3).

elative to concerned level

roundness (elliptic fit). To describe

level with the average area of its sub-

generate new object-description features from

description features were created by referring to the

layers for given polarisation and rotation angles. Examples are the mean

polarized images (mean_ratio_x_blue) or the brightness of all cross-polarized images

))( 0LObj the mean area of

object’s texture and vice versa.

1 into a set of fuzzy classes, each class has been described

tures outlined in section 4.1. The example outlined in

rules for each feature can be combined by fuzzy-and

and-operator the degree of


operator return a membership value equal to the membership function with the

or operators can be nested (see

Upper value border

1.8

110

0.4

0.4

0.33

0.33

0.35

0.4

Garnet and Quartz were described comparably, whereas for these classes the feature


(see section 4.2) leads to the initial classification result of the

elements of Plagioclase in cross-polarized image layers)

polarized layers only. The parallel-

4 shows the class description of

hierarchical relationship of

membership function has been expressed as a singleton



______________________________________________

Class and description

White mica

In the next step, unclassified and wrongly

area being classified as decomposition of Plagioclase (relative area of sub objects decomposition (1)

unclassified objects were then assigned to the class with the

differentiated into brighter and darker (crossed polarizers) Plagioclase objects and relevant, adjacent Plagioclase objects

were merged. This enabled us to conserve the grain boundaries between adjac

Quartz and bordering Biotite objects were merged, too. In order to reshape the fringy boundaries of the objects, a grow

and-shrink-algorithm was applied. To avoid a destruction of the already clearly detected bord

Biotite as well as growing of Plagioclase into bordering minerals, the respective growing was restricted: Quartz and

Biotite were allowed to grow into Plagioclase, whereas Plagioclases were not allowed to grow into neighbouring

minerals. Since there are clear grain boundaries inside Quartz and Garnet, these were finally re

resolution segmentation using the cross-

Fig. 3 Top-left: detected White Mica objects (red dots). Top

segmentation, applying initial fuzzy-membership descriptions of intended classes, superimposed on original image data for reference.

Non-colored objects are unclassified. Bottom

classified objects. Bottom-right: final result after grow

Feature Lower value border

Brightness_x 80

Existence of super

object Plagioclase (1) 0





Table 4: Class description of White mica

In the next step, unclassified and wrongly classified Quartz-objects were re-assigned to Plagioclase according to their

area being classified as decomposition of Plagioclase (relative area of sub objects decomposition (1)

unclassified objects were then assigned to the class with the largest common border (Fig. 3). Plagioclase was further


were merged. This enabled us to conserve the grain boundaries between adjacent Plagioclase grains (Fig. 3). Bordering

Quartz and bordering Biotite objects were merged, too. In order to reshape the fringy boundaries of the objects, a grow

algorithm was applied. To avoid a destruction of the already clearly detected bord



als. Since there are clear grain boundaries inside Quartz and Garnet, these were finally re

-polarized layers.

left: detected White Mica objects (red dots). Top-right: initial classification result of top

membership descriptions of intended classes, superimposed on original image data for reference.

colored objects are unclassified. Bottom-left: Classification result after knowledge-based re-assignment and merge of equally

right: final result after grow-and-shrink and dedicated re-segmentation (legend top

Upper value border

90

2

0.20

0.60

0.50

0.25

assigned to Plagioclase according to their

area being classified as decomposition of Plagioclase (relative area of sub objects decomposition (1)). All remaining

largest common border (Fig. 3). Plagioclase was further


ent Plagioclase grains (Fig. 3). Bordering

Quartz and bordering Biotite objects were merged, too. In order to reshape the fringy boundaries of the objects, a grow-

algorithm was applied. To avoid a destruction of the already clearly detected borders between Quartz and



als. Since there are clear grain boundaries inside Quartz and Garnet, these were finally re-segmented by a multi

ssification result of top-level multi resolution

membership descriptions of intended classes, superimposed on original image data for reference.

assignment and merge of equally

segmentation (legend top-right).



______________________________________________

5. Summary

Object Based Image Analysis is a promising candidate for the automated, reliable processing of petrographic

micrographs. Mimicking a petrographer’s approach to discriminating a rock’s mineral inventory under the optical

microscope, OBIA allows for incorporating multiple, typically soft criteria about the spectral, geometrical, topological

and genetic properties of mineral phases. The formulation of rule sets enables a flexible, quantitative and reproducible

mineral classification and structure analysis for a broad range of rock types.

Acknowledgement: We thank V. Höck for providing the sample, the microscope and for thorough petrographic discussions.

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