unsupervised segmentation of collagen fiber distribution...
TRANSCRIPT
Unsupervised Segmentation of Collagen Fiber Distribution in Different Stages of OSF
Tathagata Raya, Jyotirmoy Chatterjeeb, Anirban Mukherjeea, Mousumi Palc, Keya Chaudhurid, Ranjan
Rashmi Paulc, Pranab K. Duttaa
a Department of Electrical Engineering, Indian Institute of Technology, Kharagpur, 721302, W.B.
b School of Medical Science and Technology, Indian Institute of Technology, Kharagpur, 721302, W.B. c Department of Oral and Maxillofacial Pathology, Gurunank Institute of Dental Science and Research,
Panihati, Kolkata 700114, W.B. d Molecular and Human Genetics Division, Indian Institute of Chemical Biology, Kolkata 700 032, India.
Corresponding Author: Pranab K. Dutta, E-mail: [email protected]
Abstract
The objective of this paper is to describe the comparative efficacy of three signal
processing based feature extraction methodologies for classifying normal oral mucosa,
early and advanced stages of Oral Submucous Fibrosis (OSF) by unsupervised
segmentation of sub-epithelial collagen fibers. Wavelet and discrete cosine transform
(DCT) based energy features are extracted from Transmission Electron Micrographs
(TEM) of collagen fibers of different stages of OSF in non overlapping blocks which in
turn are classified by fuzzy c-means clustering. The overall efficacy of DCT based
features have been found better in comparison to its wavelet based counterpart.
Keywords: DCT mask, wavelet transform, segmentation, OSF, collagen fiber, rotational invariance 1. Introduction
A high incidence of oral cancer is mainly due to late diagnosis of potential precancerous
lesions and conditions.1-3 There is a consistent evidence that prognosis of oral cancer is
better when it is diagnosed at early stage i.e in precancerous condition.4 Oral Submucous
Fibrosis (OSF) is such a precancerous condition of oral cavity and oro-pharynx having
insidious chronic progressive nature and a high degree of malignant potentiality.5
Histopathologically various changes in epithelium with concurrent sub-epithelial fibrosis
are major characteristics of OSF, but very few studies have addressed the collagen
fibrosis in a definite quantitative manner. In this regard computer aided diagnostic (CAD)
approach aided with statistical modeling has been successfully employed to analyse
collagen ultrastructure.6 The transmission electron micrographs of sub epithelial fibrillar
collagen population of early and advanced stages of OSF have also been analyzed by
CAD approach coupled with wavelet-Artificial Neural Network (wavelet-ANN) to
compare with the fibers of normal oral mucosa.7 The wavelet features have been
compared for the supervised stage classification of OSF.7 The Haar features were used in
for ANN based supervised classification of different stages of OSF.7 Few reports are
available regarding successful applications of machine learning in precancerous
diagnosis.8,9,10,11,12,13
Porter showed that energies (averaged 1l norm) of the combined LH and HL channels for
all three level decompositions along with the energy of the LL channel of the lowest level
decomposition of 3 level wavelet transform using Daubechies wavelet filter of support 8
(DB8) can serve as rotational invariant features in case of texture classification.14 In the
present study this philosophy has been used to mark different regions of the input image.
Now this study mainly aims at differentiating axial, transverse and collagen free regions
present in OSF in an unsupervised way as a texture classification problem. The energy of
three levels DB8 wavelet function is used as the discriminating test features. In general,
neighboring pixels within an image tend to be highly correlated. The DCT has been
shown to be near optimal for a large class of images in energy concentration and
decorrelating.15,16 DCT has the construction of a decorrelated basis system which will
almost always reduce the dimensionality of a feature vector space. If the Markov-1 model
fits the image well, then the best option would be to use the DCT as the decorrelating
transform.17 It decomposes the signal into underlying spatial frequencies, which then
allow further processing techniques to reduce the precision of the DCT coefficients
consistent with the Human Visual System (HVS) model. The horizontal and vertical AC
coefficients of DCT have been selected for rotational invariant features to represent the
regularity, complexity and some texture features of an image. This feature extraction
process has been followed by Fuzzy c-means technique.18
Earlier the input TEM images were directly fed to feature extraction and classification
stage.7 But it has been observed that the content of section of the incision biopsy is
heterogeneous in nature. A single section contains both transverse and axially-cut
collagen. Moreover, this section contains some collagen free region. Naturally, there are
some voids (collagen-free portion) in any transverse or axial section of the incision
biopsy. The simultaneous presence of transverse, axial and collagen-free sections (as
Fig. 1. Less-advanced stage of OSF consisting of transverse, axial and collagen free regions.
Collagen free region
Axial region
Transverse region
shown in Fig. 1) of the input image makes the processing task challenging. In order to
enhance the performance of the ANN-based classifier the input image has to be clustered
first. Out of the three clusters, the zone showing the transverse section of collagen will be
identified. The ANN-based staging of the OSF will be performed on this transverse
section of the collagen image identified in the previous step. The complete scheme is
shown in Fig 2. In order to check the performance of this clustering technique, the
computed output has been compared with the reference segmentation map manually
drawn by two oral oncologists. These medical experts have marked the three regions
(transverse, longitudinal, and collagen free) independently. The independently marked
common area has been taken as desired segmentation map and the region segmentation
accuracy has been evaluated for various stages of OSF images. Basically study for
identifying certain features from TEM images has been done here. The features
considered are based on wavelet transform, wavelet packet transform and DCT.
Rotational invariance of these features has been verified also. These features will be
subsequently used to ascertain that under certain circumstances whether one feature set
outperforms other feature sets or not. The scope of this paper has been marked in Fig. 2.
Region wise segmentation is carried out in block wise decomposition due to lesser
computational time than that of pixel wise processing. Apart from this rotational invariant
method based on DCT proposed here has been found to be superior to rotational invariant
wavelet based methods. Also the proposed method has the advantage of easiness of
fabrication and implementation as it is derived from DCT.
This paper has been organized as follows: Introduction has been covered in Section 1.
Section 2 deals with selection of patients, clinical classification of OSF stages and
transmission electron microscopic (TEM) study. In section 3 and 4, feature extraction and
clustering methodology have been discussed respectively. The results and observations
have been presented in section 5. Finally, Section 6 is devoted for conclusion.
2. Selection of Patients, Clinical Classification of OSF Stages and Transmission Electron Microscopic (TEM) Study In selection of study-subjects, primarily oral health was examined as per standard
procedures and provisional clinical diagnosis was made for OSF patients with oral
lesions. Subsequently OSF cases were confirmed histopathologically (still regarded as
the gold standard of OSF diagnosis), through routine H&E light microscopic evaluation
of incisional biopsy under their (patient’s) prior consent at the Department of Oral and
Maxillofacial Pathology, R. Ahmed Dental College & Hospital, Kolkata.19 The examined
cases are given as Less advanced(n=55), Advanced(n=60), Normal(n=30). The
classification /grading of OSF (less-advanced and advanced) has been done according to
the degree of trismus (i.e. reduction in the overall mouth opening, visual observation etc.
Region-wise segmentation
ANN-based staging of OSF evaluated on transverse/axial section
Identification of cluster (transverse or axially cut area)
Evaluation of clustering accuracy
Desired common segmentation map by two oral oncologists
Input image
Fig. 2 Block diagram for staging process of OSF.
by experienced oral pathologists) which has direct correlation with the degree of fibrosis,
progression of the disease and location of OSF lesion in oral mucosa.20 Clinically, varied
degrees of trismus (inability to open the mouth) in these patients are also evident which
has a direct correlation with the oral location of the OSF lesion and degree of fibrosis due
to excessive formation of subepithelial collagen fibers.20 However, trismus is not the only
basis of diagnosis of OSF i.e. the reverse is not always true. The unaffected area of the
biopsy was treated as the representative of normal mucosa (test normal). In histologically
confirmed OSF cases (less advanced and advanced) both the test normal and affected
mucosal regions of all biopsies were further subjected to transmission electron
microscopic evaluation for capturing ultra-structural features of sub-epithelial collagen
fibers.20 It is to be noted that the test normal sample has been taken from the visually and
clinically unaffected zones of oral mucosa. In this work, the ultrastructural changes in
TEM images are captured by the feature extraction and classification techniques as
discussed in section 3. The clinical diagnosis of OSF at macroscopic and microscopic
level by the oncologists provides the ground truth for less advanced stage and advanced
stage.
3. Feature Extraction 3a. Wavelet Transformation The 2-D wavelet transform performs a spatial frequency analysis on an image by
repeatedly decomposing the image in the lower frequency sub-bands.21 The output
depends on the type of wavelet, the decomposition and wavelet filter specifications. The
features have been derived from three level wavelet decomposition coefficients. The
energy level of the main channels of the wavelet decomposition has been found to be
effective as rotational invariant features for texture segmentation.14 In general, the texture
always has components in both vertical and horizontal frequencies whatever the amount
of rotation it has may be. Feature based on plain wavelet transform represent these
frequencies. So these features have been found to be rotational variant. Therefore, in the
proposed scheme rotation invariance has been achieved by combining pairs of diagonally
opposite wavelet channels (HL and LH) to form single features. This approach is thus
entirely based on the composition of spatial frequencies within the texture and is not
heavily dependent on the texture’s directionality. The LH and HL channels (as shown in
Fig 3.) in each level of decomposition are grouped together to produce four main
frequency bands. The HH channels are not used owing to their poor signal to noise ratio
which degrades the classification accuracy. The energy levels in each of these chosen
bands are calculated as the mean of the magnitudes of their wavelet coefficients. The four
dimensional feature vector Twwwww ecececdcF ][ 3211= has been used as the characteristics
of MxN block. The region classification has been performed on this four dimensional
feature space. The energy or averaged 1l -norm for the thn channel is given by
( )∑∑= =
=M
i
N
jLLn
wn jix
MNdc
1 1,1 for 1=n (1a)
( ) ( ){ }∑∑= =
+=N
i
N
jLHnHLn
wn jixjix
MNec
1 1,,1 for 3,2,1=n (1b)
where the channel is of dimensions MxN (usually NM = ), i and j are the indices of
row and column of the channel and LLnx is LL channel wavelet coefficient of level n .
3b. Wavelet Packet Transformation Wavelet packets are a generalization of orthonormal and compactly supported wavelets
.22 In wavelet analysis a signal is split into an approximation and a detail. The
approximation is then itself split into a second level approximation and detail and the
process is repeated. But in wavelet packet analysis the detail as well as the approximation
can be split. The difference between wavelet transform and wavelet packet transform is
that later recursively decomposes higher frequency components thus constructing a tree-
structured multiband extension of the wavelet transform. An example of a three level
wavelet packet transform is shown in Fig. 4.
LL HL
HH LH
1 2 2
3
3 4
4
Fig. 3 Grouping of wavelet channels to form 4 bands to produce rotation invariant features.
The decomposition of root image (top image shown in Fig. 4) creates four components at
the next level ((1,1),….(1,4)). Likewise 64 channel wavelet coefficients ((3,1)..(3,64))
have been obtained from the 3 level wavelet packet transform. In this wavelet packet
transform 17 rotational invariant energy features (eq. 2) have been considered for 3 level
wavelet packet transform. Therefore, the wavelet packet feature may also have been used
for stage classification. Here feature vector is Twpwpwpwpwp ecececdcF ]......[ 17211=
where
( )∑∑= =
=M
i
N
jLLn
wpn jix
MNdc
1 1,1 for 1=n (2a)
(1,1) (1,2) (1,3) (1,4)
(2,1)(2,2) (2,3)(2,4) . . . . . . . (2,13)(2,14)(2,15)(2,16)
(3,1)(3,2)(3,3)(3,4)…………………………………………… (3,61)(3,62)(3,63)(3,64)
Root image
Fig. 4 Tree structure of three level wavelet packet transform.
( ) ( ){ }∑∑= =
++=N
i
N
jnLHHLn
wpn jixjix
MNec
1 1)11(1 ,,1 for 16..3,2,1=n (2b)
where 62..8,5,21 =n and the channel is of dimensions MxN (usually NM = ), i and j
are the indices of row and column of the channel.
3c. Discrete Cosine Transform (DCT)
A local linear transform can be interpreted as a spatial filtering approach for image
decomposition and texture representation.24,18 The corresponding 2D DCT mask may be
obtained from its 1D version as it is a separable transform.
A 1D DCT basis vector um support N is expressed as
( )N
kum1
= for m=1
( )( )⎭⎬⎫
⎩⎨⎧ −−
=Nmk
N 2112cos2 π for m=2,……N …(3)
8 pixels
Fig. 5 DCT basis patterns Neutral gray represents zero, white represents positive amplitudes, and black represents negative amplitude.23
8 pi
xels
These 1D DCT vectors can then be used to generate 2D transform filters appropriate for
images. For this column basis vectors with the row vectors of identical length are
multiplied to produce a set of 2D filters of 2N entities where N is the vector length. The
mn -th entity in this filter bank, mnd , is given as
( ) ( )'kukud nmmn = where Nnm ≤≤ ,1 3c.1 Proposed Modification In Table 1 the highest filter coefficient mask of size 8X8 has been taken from rightmost
bottom corner of Fig 5. It has been assumed in this work that the 4X4 submask A serves
as approximation filter mask. Consequently V & H has been treated as vertical and
horizontal filter mask respectively. It is to be noted that these submasks V and H are the
horizontal and vertical version with a negative sign.
TARARARARAACACACACA ][],[ 43214321 == ; where jAC and jAR are
column vector.
( )[ ]12341 ACACACACV −= , ( )[ ]TARARARARH 12341−=
0.0396 -0.1127 0.1686 -0.1989 0.1989 -0.1686 0.1127 -0.0396 -0.1127 0.3209 -0.4802 0.5665 -0.5665 0.4802 -0.3209 0.1127 0.1686 -0.4802 0.7187 -0.8478 0.8478 -0.7187 0.4802 -0.1686 -0.1989 0.5665 -0.8478 1.0000 -1.0000 0.8478 -0.5665 0.1989 0.1989 -0.5665 0.8478 -1.0000 1.0000 -0.8478 0.5665 -0.1989 -0.1686 0.4802 -0.7187 0.8478 -0.8478 0.7187 -0.4802 0.1686 0.1127 -0.3209 0.4802 -0.5665 0.5665 -0.4802 0.3209 -0.1127 -0.0396 0.1127 -0.1686 0.1989 -0.1989 0.1686 -0.1127 0.0396
Table 1: Approximation, horizontal and vertical part of the highest detail DCT filter coefficient mask.
A
H
V
D
The energy measure of dc component of the convoluted image with the A part of lowest
DCT mask (left most top corner of Fig 5) and energy measure of some combination of
convoluted image with V and H part of 64 DCT masks are taken as rotational invariant
DCT features as given by feature vector [ ]Tddddd ecececdcF 64211 .......= where
( )∑∑= =
=M
i
N
jAn
dn jix
MNdc
1 1,1 for n =1
( ) ( )∑∑= =
+=M
i
N
jVnHn
dn jixjix
MNec
1 1,,1 for n =1 to 64. (4)
where VnAn xx , and Hnx denote input image, I convoluted with VA, and H part of n th
DCT mask given by eq 5.
VIxHIxAIx VnHnAn ⊗=⊗=⊗= ,, (5)
These are the modified version of conventional DCT features.
An experiment has been performed to show the efficacy of the above mentioned
proposed rotational invariant features based on DCT. Twelve images belonging to
normal, less advanced and advanced stages of OSF have been taken as test image. Now
each test image has been rotated by angle of 45, 90 and 135 degree. Thereafter
midportion of size 32X32 of unrotated and three rotated versions of each of the test
images are considered as input to the proposed feature extraction method. Sixty five
proposed features have been extracted for four versions of input image for rotation of 0,
45, 90 and 135 degrees. Centralised moments of order 1, 2 and 3 are calculated on
normalized 65 features. Three centralized moments calculated for four versions for each
of twelve input images are shown in Table 2. It can be inferred from the Table 2 that
centralized moments of the proposed features vary in a small amount among four
versions of a particular input image. So the proposed features are rotational invariant
indeed.
Input image Rotation
in degrees
Centralized 1st order moment
Centralized 2nd order moment
Centralized 3rd order moment
Sample 1 0 0 0.020902 0.0153 45 0 0.020961 0.015347 90 0 0.020909 0.015305 135 0 0.020969 0.015354 Sample 2 0 0 0.020891 0.015257 45 0 0.020913 0.015277 90 0 0.020885 0.015248 135 0 0.020913 0.015277 Sample 3 0 0 0.020995 0.015364 45 0 0.020994 0.015357 90 0 0.020999 0.015369 135 0 0.020999 0.015365 Sample 4 0 0 0.020876 0.015272 45 0 0.020937 0.015322 90 0 0.020874 0.01527 135 0 0.020934 0.015318 Sample 5 0 0 0.020963 0.015347 45 0 0.021008 0.015386 90 0 0.020964 0.015346 135 0 0.021009 0.015387 Sample 6 0 0 0.021046 0.015429 45 0 0.021079 0.015451 90 0 0.021047 0.015429 135 0 0.021078 0.01545 Sample 7 0 0 0.021037 0.0154 45 0 0.02105 0.015413 90 0 0.021036 0.015399 135 0 0.021049 0.015412 Sample 8 0 0 0.021079 0.01545 45 0 0.021099 0.015466 90 0 0.02108 0.015451 135 0 0.021101 0.015468 Sample 9 0 0 0.020935 0.015312 45 0 0.020961 0.015332 90 0 0.020933 0.01531
Table 2.Centralised moments of order 1, 2 and 3 with four rotated versions of each of the twelve number of input images of size 32X32
Input image Rotation in degrees
Centralized 1st order moment
Centralized 2nd order moment
Centralized 3rd order moment
135 0 0.020961 0.015332 Sample 10 0 0 0.02103 0.015401 45 0 0.021047 0.015412 90 0 0.021034 0.015406 135 0 0.02105 0.015416 Sample 11 0 0 0.020889 0.015283 45 0 0.020951 0.015333 90 0 0.020892 0.015286 135 0 0.020944 0.015324 Sample 12 0 0 0.020866 0.015241 45 0 0.020919 0.01529 90 0 0.020866 0.01524 135 0 0.02092 0.015292 4. Clustering of Feature Vectors In this proposed approach, the extracted feature vectors have been fed to clustering stage.
Among different clustering techniques, k-means and fuzzy c-means are used frequently
nowadays. Starting with an initial condition k-means algorithm finds a “hard partition” of
a given feature vector based on certain criteria that evaluates the goodness of a partition.
The hard partitioning assigns each cluster to only one class. This disadvantage is
eliminated in fuzzy c-means clustering methodology.25 The progression of OSF is
continuous in nature and the extracted feature points partially may belong to multiple
classes with different degree of membership. Then the rotational invariant features have
been fed to fuzzy c-means clustering.
5. Results and Discussions The entire process consists of two goals namely obtaining zonal segmentation area wise
and estimating severity of OSF. But the feature extraction and clustering are backbones to
achieve these goals. In the following table, the best results are shown with rotational
invariant wavelet transform, wavelet packet transform and DCT mask based feature
extraction technique in terms of percentage of misclassification. Percentage of
misclassification is given as
100% ×=consideredpixelsofnumbertotal
pixelsiedmisclassifofnumbericationmisclassifof
Number of misclassified pixels is the mismatched pixels between obtained segmentation
map and desired common segmentation map.
In Table 3, the results of the different feature extraction methods are tabulated. Three
block sizes namely 8X8, 16X16 and 32X32 have been tried as a compromise between
computational burden and classification accuracy. In the first row of Table 3, the feature
extraction techniques namely rotational invariant wavelet transform, rotational invariant
wavelet packet transform and rotational invariant DCT produce 38.43, 31.57 and 34.43 %
of misclassification respectively for three class clustering on can5 image for block size of
8X8. As per the result is concerned in can5, can17 rotational invariant wavelet transform
is better compared to rotational invariant DCT and rotational invariant wavelet packet
transform. Rotational invariant wavelet packet performs well in lessadv2. In the rest
images rotational invariant DCT performs well compared to other two feature extraction
methods. The highest and lowest misclassification accuracies are observed in rotational
invariant DCT mask based method. Choice of block size for best performance varies with
the image and with the feature extraction method chosen. Out of 36 cases given in Table
3, the rotational invariant DCT based feature extraction method is good in 22 cases
whereas that number for rotational invariant wavelet transform and rotational invariant
wavelet packet transform are 11 and 3 respectively. In Fig. 6, area wise segmented image
by 3 different feature extraction methods are shown for different stages of OSF. In Table
4, rotational invariant features based on wavelet transform, wavelet packet transform and
DCT followed by kmeans clustering show similar results as that of Table 3 where fuzzy
cmeans clustering instead of kmeans clustering is used. Now it can be inferred that
features are stable.
The methods discussed above can be used to segment the sections of different stages of
collagen fibers namely normal, less advanced and advanced. The efficacy of rotational
invariant DCT is well demonstrated in fig 7(a)-(d). In these two examples the test image
is created by the combination of normal, less advanced and advanced stages of axial
section of OSF. In these two examples the classification accuracy is 100%. In fact ideal
segmentation is found in 35% and 83% of all valid examples in the form of Fig 7(a). and
Fig. 7(c) respectively. The figures are 40% and 88% respectively for transverse section
of OSF.
The classification of ultrastructural features of OSF in an unsupervised manner is really
challenging. Because like any other precancerous condition, different phases of OSF
depict mixed features of normal and disease states (pre-malignancy). Accordingly the less
advanced stage of OSF comprises of both the normal and pre malignant features with
many overlapping and ambiguities. This complexity of the diseased tissue has been
reflected in connection with the unsupervised area-wise segmentation of different
structural features in less advanced stage of OSF in lower resolution (block size- 8X8
pixels) as shown in Table 3. To tackle these ambiguities in the structural level three
different unsupervised feature extraction techniques are used in this study. The data
Performance in terms of percentage of misclassification of Rotation
Invariant Feature extraction methods
Image Name
Block size
Wavelet tr. (3 level)
(C3)
Wavelet packet tr. (3 level) (C4)
DCT (C5)
No. of clusters as suggested
by medical expert
Min (C3,C4,C5)
less adv1 8X8 39.02 38.13 37.12 Three 37.12(dct) 16X16 21.57 43.08 39.73 Three 21.57(w) 32X32 37.20 40.83 42.85 Three 37.20(w)
less adv2 8X8 10.84 12.29 11.68 Three 10.84(w) 16X16 8.72 10.48 10.26 Three 8.72(w) 32X32 7.68 9.39 10.84 Three 7.68(w)
less adv3 8X8 47.02 35.81 36.80 Three 35.81(wp) 16X16 41.92 41.18 37.29 Three 37.29(dct) 32X32 40.53 42.08 36.47 Three 36.47(dct)
less adv 4 8X8 15.18 24.86 13.55 Three 13.55(dct) 16X16 16.40 19.71 13.64 Three 13.64(dct) 32X32 18.51 20.31 13.15 Three 13.15(dct)
adv1 8X8 32.38 31.07 29.54, Three 29.54(dct) 16X16 23.80 29.11 26.92 Three 23.80(w) 32X32 24.16 25.91 23.23 Three 23.23(dct)
adv2 8X8 42.91 40.77 33.42 Three 33.42(dct) 16X16 41.27 42.76 32.57 Three 32.57(dct) 32X32 41.77 39.01 33.33 Three 33.33(dct)
adv3 8X8 18.48 19.37 17.35 Three 17.35(dct) 16X16 17.23 19.01 14.68 Three 14.68(dct) 32X32 16.82 13.92 15.29 Three 13.92(wp)
adv4 8X8 35.39 31.49 25.20 Three 25.20(dct) 16X16 34.90 36.60 24.59 Three 24.59(dct) 32X32 31.31 36.16 25.41 Three 25.41(dct)
adv5 8X8 44.89 47.17 39.22 Two 39.22(dct) 16X16 47.89 50.20 38.96 Two 38.96(dct) 32X32 52.55 49.75 42.00 Two 42.00(dct)
adv6 8X8 27.30 29.45 24.06 Two 24.06(dct) 16X16 28.44 30.52 26.05 Two 26.05(dct) 32X32 31.91 33.67 27.47 Two 27.47(dct)
Normal1 8X8 16.29 17.98 22.34 Three 16.29(w) 16X16 11.77 17.51 22.61 Three 11.77(w) 32X32 8.80 9.04 14.28 Three 8.80(w)
Normal2 8X8 34.71 37.77 36.99 Three 34.71(w) 16X16 36.24 35.74 36.26 Three 35.74(wp) 32X32 35.24 35.74 37.71 Three 35.24(w)
Table 3. Percentage of misclassification of feature extraction methods followed by fuzzy cmeans clustering with the different block sizes on images of OSF.
Performance of rotational invariant feature extraction
Input image name
Block size
Wavelet tr.
Wavelet packet tr.
DCT
No. of clusters
8X8 0.3420 0.2518 0.2949 Three 16X16 0.2375 0.2470 0.2747 Three
adv1
32X32 0.2464 0.5081 0.2264 Three 8X8 0.2975 0.3248 0.3785 Three
16X16 0.3022 0.3451 0.3055 Three less adv1
32X32 0.3029 0.3454 0.4302 Three 8X8 0.1067 0.1131 0.1138 Three
16X16 0.0925 0.1067 0.1008 Three less adv2
32X32 0.0961 0.1033 0.1049 Three 8X8 0.2957 0.2906 0.3776 Three
16X16 0.3236 0.3144 0.3950 Three lessadv3
32X32 0.3712 0.3050 0.3841 Three 8X8 0.1997 0.1564 0.1421 Three
16X16 0.1963 0.1557 0.1399 Three lessadv4
32X32 0.1755 0.1793 0.1161 Three 8X8 0.3435 0.3496 0.2490 Three
16X16 0.2993 0.3524 0.2438 Three adv4
32X32 0.2714 0.3208 0.2553 Three 8X8 0.4006 0.4049 0.3402 Three
16X16 0.3744 0.3707 0.3306 Three adv2
32X32 0.3798 0.3952 0.3491 Three 8X8 0.1726 0.1828 0.1752 Three
16X16 0.1807 0.1588 0.1469 Three adv3
32X32 0.1798 0.1516 0.1529 Three 8X8 0.4423 0.4692 0.3840 Two
16X16 0.4806 0.5058 0.3760 Two adv5
32X32 0.5227 0.4993 0.4247 Two 8X8 0.2638 0.2714 0.2314 Two
16X16 0.2813 0.2935 0.2547 Two adv6
32X32 0.3216 0.3296 0.2613 Two 8X8 0.1686 0.1594 0.2264 Three
16X16 0.0815 0.1874 0.2288 Three Nrm(a)
32X32 0.1118 0.0627 0.1408 Three 8X8 0.3403 0.3590 0.3181 Three
16X16 0.3233 0.3237 0.3709 Three Nrm(b)
32X32 0.3027 0.3333 0.3780 Three
Table 4. Percentage of misclassification of feature extraction methods followed by kmeans clustering with the different block sizes on images of OSF.
shows that the performance of DCT is more consistent in comparison to wavelet
transform and wavelet packet transform based techniques. From the notch box
(a) (b) (c) (d)
(e) (f) (g) (h)
(i) (j) (k) (l)
Fig. 6. (a) less adv2 (b) results of rotational invariant 3 level wavelet transform, (c) results of rotational invariant 3 level wavelet packet transform, (d) results of rotational invariant DCT mask based features on a image blocks having size 8X8, (e) less adv4, (f) results of rotational invariant 3 level wavelet transform, (g) results of rotational invariant 3 level wavelet packet transform, (h) results of rotational invariant DCT mask based features on a image blocks having size 8X8, (i) adv3, (j) results of rotational invariant 3 level wavelet transform, (k) results of rotational invariant 3 level wavelet packet transform, (l) results of rotational invariant DCT mask based features on a image blocks having size 8X8.
presentation of the misclassification accuracy of different feature extraction techniques at
different levels of resolutions, the performance of DCT is superior to wavelet transform
and wavelet packet transform in respect to the error range (as a whole [Fig. 8] and in
different level of resolution [Figs. 9-11] ), uncertainty of median value is minimum. Thus
it may be stated that the DCT-based technique may be adopted for unsupervised
segmentation of the ultra structural features of OSF. Simultaneously it is also noted by
the researchers that more critical attention is required in identifying target features of
complex tissue texture like pre-cancerous conditions to minimize the misclassification
error.
Every image processing task is generally input data or image dependent. So the
classification accuracy is varying a great deal. Also there are overlapping between three
regions namely axial, transverse and void. The classification accuracy also dips due to
complex boundary between different regions
(m) (n)
(o) (p)
Fig. 6. (m) Normal1, (n) results of rotational invariant 3 level wavelet transform, (o) results of rotational invariant 3 level wavelet packet transform, (p) results of rotational invariant DCT mask based features on a image blocks having size 8X8.
(a) (b)
(c) (d)
Fig. 7. (a), (c) two input images consisting of normal, less advancedand advanced stages of axial section of OSF. (b), (d) segmented mapby rotational invariant DCT.
1 2 3
0
10
20
30
40
50
60
% o
f mis
clas
ifica
tion
feature extraction methods
Fig 8. Notch box plot of misclassification accuracy of different feature extraction methods namely wavelet transform(1), wavelet packet transform(2) and Discrete Cosine transform(3).
1 2 3
0
10
20
30
40
50
60
% o
f mis
clas
ifica
tion
feature extraction methods Fig 9. Notch box plot of misclassification accuracy of different feature extraction
methods namely wavelet transform(1), wavelet packet transform(2) and Discrete Cosine transform(3) for 8X8 resolutions.
1 2 30
10
20
30
40
50
60
% o
f mis
clas
ifica
tion
feature extraction methods
Fig. 10 Notch box plot of misclassification accuracy of different feature extraction methods namely wavelet transform(1), wavelet packet transform(2) and Discrete Cosine transform(3) for 16X16 resolutions.
6. Conclusion
In conclusion it may be stated that among three feature extraction techniques, the overall
performance of proposed DCT based technique is better at all levels of resolution tried.
Moreover, being an unsupervised segmentation technique it can be a very important tool
in characterizing different stages of OSF specially the early stage which not only depicts
structural ambiguities with the presence of both normal and diseased features but also
poses problem to the medical experts while making the decision. The region of transverse
section of collagen may be extracted successfully with the help of our novel
methodology, which otherwise is a very intensive task for human analyst. The ANN-
1 2 30
10
20
30
40
50
60
% o
f mis
clas
ifica
tion
feature extraction methods
Fig. 11 Notch box plot of misclassification accuracy of different feature extraction methods namely wavelet transform(1), wavelet packet transform(2) and Discrete Cosine transform(3) for 32X32 resolutions.
based supervised classifier will further be used on this extracted transverse zone in order
to finally detect the stage of the disease.20
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