ISIJ Int. 54(6): 1222-1227 (2014)ISIJ International, Vol. 54
(2014), No. 6, pp. 1222–1227
Quantitative Analysis of Mineral Composition of Iron Ore Sinter
Based on Comprehensive Image Processing Techniques
Hong-wei GUO,1,2)* Bu-xin SU,2) Zhen-long BAI,3) Jian-liang ZHANG2)
Xin-yu LI2) and Feng LIU2)
1) Shangang School of Iron and Steel, Soochow Uniwersity, Suzhou,
215021 China. 2) School of Metallurgical and Ecological
Engineering, University of Science & Technology Beijing,
Beijing, 100083 China. 3) School of Automation and Electtrical
Engineering, University of Science & Technology Beijing,
Beijing, 100083 China.
(Received on October 11, 2013; accepted on January 9, 2014)
To acquire the mineral composition of iron ore sinter, the research
work until now mainly focuses on two aspects which are traditional
manual method often called Point Counting Method and Image
Processing Method respectively. Point Counting Method is of high
labor intensity and of low efficiency compared with Image
Processing Method. Meanwhile, existing Image Processing Method
always encounters low accuracy when it is applied to analyze the
sinter with special microstructure such as large pores. This paper
presents an improved method based on modified gray-level images and
extracted texture images with knowledge base rules, producing the
final images more suitable for computer to deal with. The
experimental results demonstrate that this improved technique is
valid for quantifying the mineral compo- sition of sinter and it
has a higher-level accuracy of recognition than existing image
processing methods do.
KEY WORDS: sinter; quantitative analysis of the mineral
composition; digital image processing.
1. Introduction
Iron ore sinter is one of important ferrous burdens charged into
blast furnace. Stability of blast furnace opera- tion to some
extent depends upon the properties of sinter, especially when it
occupies a very large fraction. Generally speaking, sinter quality
is determined by the mineral com- position and microstructure. The
sintering theory of iron ore fines and practical production show
that the bonding phases present during sintering process make iron
ore fines bond together. It is worth noting that among these
different bond- ing phases, complex Silico-Ferrites of Calcium and
Alu- minium (SFCA) is the best. Both the amount of SFCA and its
morphology play an important role in influencing key sinter-quality
parameters such as mechanical strength and reducibility.1–5)
Therefore, it is urgent to find an effective way to qualitatively
and quantitatively analyze mineral composition of sinter with a
certain level of accuracy so as to optimize the ore-blending and
sintering process. Howev- er, traditional Point Counting Method is
laborious,6) besides, the accuracy of experimental results is
always affected by the experience and skill of the operator.
Digital image processing is a new kind of analysis tech- nique by
using a computer, and now it has been applied widely in many
fields. For mineralogical analysis, several famous systems or
methods have already been developed to quantitatively measure
mineral composition. Sato et al.9)
lead this field in 1982, correlating sinter mineral composi-
tion to sinter quality for the first time. IRSID (Institute of
Research of French Steel Industry) and the School of Mines of
Paris7) devised a system based on operational morpholo- gy in 1983
where Jeulin introduced a method to calculate the phase area
through morphological operations such as image corrosion, image
dilation, and border detection. Another system is in 1985, which
was developed by Nippon Corporation and Dr. Yanaka of the
University of Tokyo based on pixel statistics.8) This method
determined the phase area by counting the pixels of the phase in an
image. In recent years, a set of intelligent recognition and
quantifica- tion system was developed by Professor Bai Chenguang of
Chongqing University. In their system, the calculation of mineral
composition was conducted by applying genetic algorithm and the
extraction of image texture feature was introduced to do
mineralogical phase recognition. However, sometimes the large pores
existing in the sinter microstruc- ture render this system produce
errors inevitably.10–12) To solve this problem, this paper is aimed
at applying compre- hensive Image Processing Method not only to
quantify the mineral composition of sinter but also work out the
propor- tion of the pores.
To process original micrographs, several processing tech- niques
are applied to acquire proper processed images. It can be
classified into two categories according to references:7–11)
1) both mineral composition recognition and quantification are
realized according to characteristics of the distribution of gray
level derived from the difference of reflectivity for different
minerals; 2) mineral composition recognition is carried out by
making use of texture features. However, the first method might
fail when two kinds of minerals with
* Corresponding author: E-mail:
[email protected] DOI:
http://dx.doi.org/10.2355/isijinternational.54.1222
ISIJ International, Vol. 54 (2014), No. 6
1223 © 2014 ISIJ
similar reflectivity are identified. For example, in some cas- es,
the difference of reflectivity for magnetite and hematite is
relatively small, merely 1–2%. As for the second method, its
application could be limited resulting from insignificant- ly
different texture features such as the similarity of texture
features for calcium ferrite, magnetite and hematite. There- fore,
to improve existing methods, this paper applied a more advanced
method to deal with the problems of composition recognition and
quantification for minerals with a high-level accuracy.
2. Experimental
2.1. Sinter Sample Preparation The composition of iron ore used for
sinter sample prep-
aration in this study was given in Table 1. The detailed sample
preparation procedures were as fol-
lows: Firstly, sample-making device was used to produce two green
cake samples (one was duplicated sample) for each kind of iron ore
fines with a specific chemical compo- sition mixed with
analytically pure CaO. In the sample-mak- ing process, the mixture
with the average particle size of iron ore fines 0.074 μm was
pressed into green cake samples with a dimension of 20 mm
(diameter) ×5 (height) mm. The second step was to heat these green
samples in a muffle fur-
nace. Sintering condition was following: 1) heating rate was 15
K/min before 1 173 K and 5 K/min after 1 173 K respec- tively. 2)
peak temperature was set as 1 553 K and held at this temperature
for 5 minutes, and then these samples were cooled down with the
furnace. 3) the experimental atmo- sphere was air. Finally, these
sintered cake samples were ground and polished by using
sample-grinding device con- sisted of grinding prototype, polishing
machine, glass plate, quartz sand and vanadium pentoxide aqueous
solution. These polished samples were hot mounted by using epoxy
resin and observed with the help of a polarized microscope to get
original micrographs. The field of view was ×500 and the number of
detected points was 500. Moreover, one hun- dred of pictures were
taken for every sample using a digital camera. The size of these
pictures was 3 456×2 304, and 2.5 M space was needed for every
picture. Finally, these pic- tures were saved as JPEG.
2.2. Procedures of Image Processing The original micrographs in
this study were processed
mainly in two aspects: 1) the distribution of image gray level: a
given micrograph would demonstrate particular distribu- tion
characteristics of gray level due to different reflective abilities
of different kinds of minerals for incident light, e.g. uniform
degree of the distribution of gray level, the peak value of
histograms of image, etc.; 2) the texture feature of image: a given
micrograph would present particular texture features because
different minerals have different crystalline structures as well as
different crystallization processes, e.g. the coarseness degree,
the clarity, the complexity and the similarity in different
directions and so on.
Compared with conventional image processing tech- niques, the
comprehensive image processing involving con- trast enhancement,
histogram equalization, and threshold operation aims at amplifying
the difference of gray values in an image. The extracted
characteristics of gray level dis- tribution is suitable for
primary composition recognition. Besides, based on above-mentioned
processed images, fur- ther processing are conducted, such as edge
location and texture feature extraction. In this research, Canny
operator
Table 1. Chemical composition of iron ore powders.
wt/%
Fig. 1. Overall image processing flow chart.
© 2014 ISIJ 1224
ISIJ International, Vol. 54 (2014), No. 6
and Gabor Filter methods were applied for these two goals. At last,
the images after extracting texture feature were com- bined with
previously preliminary processed images, i.e. only the distribution
characteristics of gray level were con- sidered, and then all the
images were used to recognize min- eral composition of sinter and
acquire relatively accurate quantitative information about minerals
under some certain rules and algorithms established in this paper.
Figure 1 shows the flowchart of the overall image processing.
2.2.1. Acquisition of the Original Micrograph Figure 2(a) is the
original image of the sinter sample C,
showing: 1) the pores existed but not very obvious; 2) the bonding
phase mainly was SCFA with a characteristic “platy” morphology; 3)
the amount of SFCA is relatively a little lot.
2.2.2. Contrast Enhancement and Histogram Equalization To reduce
negative effects of the noises which might
influence the accuracy of the experimental results the orig-
inal micrograph had and provide more suitable images for computer
to handle, contrast enhancement and histogram equalization were
applied.
Figures 2(b) and 2(c) show the processed images acquired by
enhancing contrast and doing histogram equalization respectively
for the original micrograph of sample C.
2.2.3. Threshold Operation Threshold operation is a special type of
quantization that
separates the pixel values in two classes depending upon a given
threshold value. Figure 2(d) shows the threshold oper- ation for
the processed micrograph of sample C.
Histogram equalization and threshold operation increase the
robustness of the following texture feature extraction. If original
micrographs are directly used to extract texture fea- ture, the
dark area and the area with non-uniform brightness in the original
micrograph will be poorly distinguished.
2.2.4. Canny Operator and Gabor Filter Methods Texture feature
extraction is a key procedure to accurately
Fig. 2. Original micrograph and Processed images of the sinter
sample C.
ISIJ International, Vol. 54 (2014), No. 6
1225 © 2014 ISIJ
describe image texture and classify different kinds of min- erals.
In order to extract texture feature, edge image is pre- requisite.
Canny operator was used to obtain the edge image, which was
processed with the image after threshold operation.
Canny operator:13) employs a set of relatively large, ori- ented
filters at multiple image resolutions and merges the individual
results into a common edge map. This operator tries to satisfy the
following three criteria: 1) excellent detection ability for edge;
2) accurate localization for edge; 3) single response. The Canny
operator substantially is a gradient method (based on the first
order derivative of Gaussian function), but it uses the zero
crossings of second derivatives for precise edge localization. In
the case of “step edges”, the directional derivative of Gauss
function and the convolution of an image are calculated by applying
the sym- metry and factorability of the two-dimensional Gauss func-
tion. In this paper, the first order derivative of Gaussian
function is chosen as suboptimum operator in the case of step
edges.
This paper used Gabor filter methods for texture feature
extraction. First of all, a group of Gabor filters were designed to
filter the texture of image. It is worthy to note that each Gabor
filter only allows the texture which has a particular frequency to
pass smoothly, and the other textures cannot get through. Thus
different texture features were extracted from the outputs of these
Gabor filters. Second, the pixel value at every point in texture
image was acquired by using a Gaussian filter to operate every
pixel value at every point in the Canny edge image Fig. 2(e). The
Gaussian filter is a special Gabor filter which involves in
Gaussian component. The Gaussian component and Gaussian filter
function are expressed as formula (1) (2) respectively.
.......................................... (1)
...... (2)
through σ = π, respectively and the wavelength was
λ = 8, N = 2λ.
The final step was summing all pixel values which were not equal to
zero by using Gaussian filter at each sampling center point to get
all the pixel values for texture image. Then stretch grayscale of
texture image to obtain Fig. 2(f).
2.2.5. Composition Calculation Assume that the gray level of every
mineral in sinter
obeys normal distribution in a specific range:
.............................. (3)
In the expression (3), Fi is the gray level distribution of mineral
i, and μ, σ 2 are the mean and variance respectively. The values of
μ, σ 2 are computed as following formulas:
..................... (4)
....................... (5)
.................. (6)
In the formulas (4) and (6), Imax and Imin are the maximum and
minimum gray level of corresponding mineral. In addi- tion, (i) is
the statistical frequency of Fi of mineral i. The statistical
calculation was conducted on the treated samples to obtain mean and
variance of different minerals.
In this paper, seven hundreds of pictures derived from texture
feature extraction and contrast enhancement for every specimen were
divided into two parts: five hundreds of these pictures acted as
training samples and the remaining as testing samples. These
training samples were used to acquire statistical frequency of gray
level. By using above- described expressions, the distribution
curve of gray level of a certain mineral was obtained.
However, region crossing phenomena might occur for two gray level
distribution curves of two different minerals. In order to obtain
superposition gray level distribution of two kinds of minerals, it
is necessary to consider two situations:
1) As shown in Fig. 3(a), there is a weak overlap between the
curves of distribution of gray level of two miner- als. Thus, two
peaks occur after summing.
2) As shown in Fig. 3(b), there is an extensive overlap between the
curves of distribution of gray level of two minerals. Thus, single
peak occurs after summing.
G x y x y x y
( , ) exp ( )
exp ( )
4 2
F x y f x x y y G x yd i j d i j y N
y N
x N
x N
μ = = × = ∑E F F ii i I
I
in
ax
( ) ( ) m
m
I
in
ax
( ) ( ) m
Fig. 3. Superposition distribution for 2 normal distribution curve.
(Online version in color.)
© 2014 ISIJ 1226
ISIJ International, Vol. 54 (2014), No. 6
In this paper, above two situations were treated in sepa- rate
ways. For Fig. 3(a), gray classification technique based on
contrast enhancement images was used to distinguish two minerals.
However, for Fig. 3(b), instead of gray clas- sification, Gabor
texture feature extraction was applied to distinguish two
minerals.
The necessary procedures for gray classification were fol- lowing
(these operations were conducted on testing sam- ples): Firstly,
the mineral set (the number of minerals is finite) was denoted by
{SN}; Secondly, for a given point (x, y), the gray level was
transformed into g(x, y) after con- trast enhancement; At last, by
putting resultant g(x, y)’s into formula (3), the distribution
curve of Si is thus obtained. Through setting different threshold
values, different miner- als were distinguished and
quantified.
When extensive overlap occurred between two distribu- tion curves,
to distinguish these two kinds of minerals in the case of the
failure of gray classification, Gabor texture fea- ture images were
introduced to get threshold values capable of recognizing
minerals.
The detailed algorithm was as follows: The first step: classified
the points (x, y) into four con- stituents: 1) Pores or cracks 2)
Silicates 3) Magnetite or calcium ferrite 4) Hematite The second
step: distinguished magnetite and calcium ferrite by applying Gabor
texture feature extraction. The third step: refined silicates by
detecting whether sil- icates surrounding pores and recounting them
as pores. According to Figs. 2(f) and 2(b), different minerals are
classified based on the following rule (called knowledge base
rules): Definition:
value1 = the pixel value at one point in Fig. 2(f); value2 = the
pixel value at one point in Fig. 2(b).
Rule: (// means “refer to”) if (value1 > A1&&
value2<B2) // Hematite else if (value1 > A2&& value2
<= A1&&value2>=B1) //Magnetite else if (value1 >A3
&& value2 >= B1) //SFCA else if (value1 < A3
&& value2 <B2) //pores else if (value1 >A3 &&
value1<=A2 && value2<B1) // Silicates
A1, A2, A3 and B1, B2 are all gray level threshold values
related to the different minerals in sinter. The primary deter-
mination of these threshold values was realized based on training
samples and then was tested and revised according to testing
samples. This paper set the final A1, A2, A3 and B1, B2 as 210,
178, 120 and 46, 156, respectively.
After calculation, the proportion of all minerals including pores
was: magnetite 39.1%, SFCA 34.8%, silicate 10.1%, pores 16%. It is
noteworthy that the proportion of magnetite could be ignored
because the specimens came from experi- ment room not from
practical production site.
3. Results and Discussion
3.1. Comparison with Point Counting Method In this research, seven
kinds of samples which are
sintered under the same sintering conditions that are T=1 553 K,
CaO/SiO2=2.0 (weight percentage ratio) are all conducting
observations under microscope and image pro- cessing to identify
and quantify the mineral composition. Table 2 shows the main
mineral composition and their pro- portions of these sinter samples
by using Point Counting Method and image processing analysis
respectively.
Table 2 shows the production ability of SFCA of these seven kinds
of iron ore fines can be arranged in the order of the amount of
SFCA: C>B>G>F>E>A>D. Besides, the amounts of
pores for different sinter samples are also sig- nificantly
different. By comparing with Point Counting Method, the results for
the amount of pores are reliable.
3.2. Comparison with Gray Classification Reference 7 applied gray
classification to acquire the gray
level distribution so as to identify and quantify mineral com-
positions. The differences of gray level for different miner- als
are as follows: the smallest difference is 9.5 resulting from
calcium silicate and pores; the difference between magnetite and
calcium ferrite is 11.6; the difference between magnetite and
hematite is 16.7. To distinguish different min- erals, this paper
set gray level between 16 and 256. The pro- cessed image obtained
from gray classification is shown in Fig. 4 which gave the
following calculation result: hematite 52.4%, SFCA 7.6%, Silicate
24.8%, pores 15.2%. In Fig. 5, comprehensive image processing
method was compared with gray classification.
Figure 5 shows that gray classification fails to recognize silicate
ferrite and hematite. Besides, the quantitative results for SFCA
obtained from these two methods vary widely. For
Table 2. Mineral composition and the proportion of pores for seven
sinter samples in two quantitative methods.
Iron ore fines
Magnetite Hematite SFCA Pores Magnetite Hematite SFCA Pores
A 0 50 12 23 0 47.5 12.8 23.4
B 0 33 36 18 0 40.8 34.3 21.2
C 0 24 45 17 0.5 25.4 43.5 16.7
D 48 14 5 15 47.1 13.5 8.4 14.0
E 5 56 12 36 4.9 52.6 11.2 36.4
F 0 42 14 32 0 42.8 12.4 30.2
G 15 16 30 35 14.4 17.0 30.5 31.5
ISIJ International, Vol. 54 (2014), No. 6
1227 © 2014 ISIJ
gray classification, the amount of SFCA is 7.6%, instead of 34.8%
by using comprehensive image processing method. Because of the low
resolution of gray classification, the pin- nate structure was
recognized as hematite and silicates. In contrast, comprehensive
image processing method consid- ered not only characteristics of
gray level distribution but texture features of different minerals
so as to capture more accurate distribution information.
4. Conclusions
This paper applies digital image processing techniques to quantify
the mineral composition. Start with the original micrograph, and do
contrast enhancement, histogram equal-
ization and threshold operation. Then extract texture feature by
using Canny operator and Gabor filter methods and clas- sify
different minerals based on knowledge base rules. The experimental
results show that the present technique is valid for quantifying
the mineral composition of sinter and it has a high-level accuracy
of recognition compared with gray classification.
Acknowledgement This work is supported by National Natural
Science
Foundation of China (No. 51204013) and Youth Education Talent Plan
in USTB (YETP0350) and National Key Tech- nology R&D Program in
the 12th Five Year Plan of China (NO.2011BAC01B02).
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Fig. 4. Final processed image of the sinter sample C.
Fig. 5. Comprehensive image processing method was compared with
gray classification.
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/FRA
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/ITA
<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>
/KOR
<FEFFc7740020c124c815c7440020c0acc6a9d558c5ec0020ace0d488c9c80020c2dcd5d80020c778c1c4c5d00020ac00c7a50020c801d569d55c002000410064006f0062006500200050004400460020bb38c11cb97c0020c791c131d569b2c8b2e4002e0020c774b807ac8c0020c791c131b41c00200050004400460020bb38c11cb2940020004100630072006f0062006100740020bc0f002000410064006f00620065002000520065006100640065007200200035002e00300020c774c0c1c5d0c11c0020c5f40020c2180020c788c2b5b2c8b2e4002e>
/NLD (Gebruik deze instellingen om Adobe PDF-documenten te maken
die zijn geoptimaliseerd voor prepress-afdrukken van hoge
kwaliteit. De gemaakte PDF-documenten kunnen worden geopend met
Acrobat en Adobe Reader 5.0 en hoger.) /NOR
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/PTB
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/SUO
<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>
/SVE
<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>
/ENU (Use these settings to create Adobe PDF documents best suited
for high-quality prepress printing. Created PDF documents can be
opened with Acrobat and Adobe Reader 5.0 and later.) /JPN
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>> /Namespace [ (Adobe) (Common) (1.0) ] /OtherNamespaces [
<< /AsReaderSpreads false /CropImagesToFrames true
/ErrorControl /WarnAndContinue /FlattenerIgnoreSpreadOverrides
false /IncludeGuidesGrids false /IncludeNonPrinting false
/IncludeSlug false /Namespace [ (Adobe) (InDesign) (4.0) ]
/OmitPlacedBitmaps false /OmitPlacedEPS false /OmitPlacedPDF false
/SimulateOverprint /Legacy >> << /AddBleedMarks false
/AddColorBars false /AddCropMarks false /AddPageInfo false
/AddRegMarks false /ConvertColors /ConvertToCMYK
/DestinationProfileName () /DestinationProfileSelector
/DocumentCMYK /Downsample16BitImages true /FlattenerPreset <<
/PresetSelector /MediumResolution >> /FormElements false
/GenerateStructure false /IncludeBookmarks false /IncludeHyperlinks
false /IncludeInteractive false /IncludeLayers false
/IncludeProfiles false /MultimediaHandling /UseObjectSettings
/Namespace [ (Adobe) (CreativeSuite) (2.0) ]
/PDFXOutputIntentProfileSelector /DocumentCMYK /PreserveEditing
true /UntaggedCMYKHandling /LeaveUntagged /UntaggedRGBHandling
/UseDocumentProfile /UseDocumentBleed false >> ] >>
setdistillerparams << /HWResolution [1200 1200] /PageSize
[596.000 795.000] >> setpagedevice