EARLY DETECTION OF CHOLESTEATOMA USING IMAGERY TECHNIQUES - A NOVEL APPROACH
Nupur Tewari Kirti Somani
AIM and ACT AIM and ACT
Banasthali Vidyapith Banasthali Vidyapith
Rajasthan (India) Rajasthan (India)
Khandakar Faridar Rahman
Saurabh Mukherjee
AIM and ACT AIM and ACT
Banasthali Vidyapith Banasthali Vidyapith
Rajasthan (India) Rajasthan (India)
Abstract— In pathological terms cholesteatoma can be stated as a
collection of squamous epithelium (i.e. abnormal skin growth)
behind the ear drum in the middle ear. In this paper, we have
studied the various imaging techniques that help in the diagnoses
of cholesteatoma and after carefully analyzing them; we have
developed two algorithms to overcome the difficulties faced during
our research period and have also done a comparative study of the
two proposed algorithms. Experiments are performed on the
secondary image data set available for middle ear cholesteatoma
using MATLAB Tool. Experiment results show the performance
measure of the two algorithms.
Keywords — Medical imaging, Computed Tomography,
Magnetic resonance imaging, Gray Level Co-occurrence
Matrix, Mathematical morphology, Image Segmentation.
I. INTRODUCTION
Cholesteatoma is an abnormal growth of skin in the middle ear
space or in the mastoid bone behind the ear drum. The growth is
classified as benign (i.e. non-cancerous).
Cholesteatoma can be clinically defined as an extension of skin
into the middle ear through mastoid air cell space.[1] In
pathological terms it is simply benign keratinizing squamous
epithelium that forms a cyst within the middle ear. The
cholesteatoma is mainly classified as:
• Congenital (from birth) Cholesteatoma
• Acquired (after repeated infection) Cholesteatoma
Primary Acquired Cholesteatoma
Secondary Acquired Cholesteatoma
The generation of cholesteatoma is due to the poor working of the
Eustachian tube which conveys the air from back of the nose into
the middle ear in order to equalize the ear pressure secondly it may
be caused due to repeated infection in the middle ear. Often due to
allergy, cold or sinusitis the Eustachian tube starts working poorly,
thus creating a partial vacuum in the middle ear. This vacuum
pressure sweeps over in order to cover it and form a pouch by starching the eardrum, weakened by the previous infections.
Here we have devised two different algorithms for segmentation of
cholesteatoma. In the first algorithm, the techniques that have been
used are: eliminating hue and saturation components, intensity
adjustment, addition of salt and pepper noise, median filtering for
denoising, conversion of labeled image into RGB image. Whereas,
in the second algorithm the techniques that have been used are:
elimination of hue and saturation components, contrast
enhancement, histogram equalization, and addition of Gaussian
noise, wiener filtering for the removal of noise, Morphology operation, and threasholding.
We have done a comparative study of the output received after
implementing both the algorithms by taking the GLCM of the
outputs. Here we have compared Energy, Correlation, Entropy,
Contrast and Homogeneity.
II. LITERATURE SURVEY
During our literature survey, we have studied various papers on the
detection of cholesteatoma using various imaging techniques
proposed by both national and international authors.
From the survey we came across various imaging techniques used
for the diagnosis along with their disadvantages.
A. Major Disadvantages in Existing techniques
1. DWI-NON-EPI-MRI
Disadvantages with this technique were: sometimes it
gives false negative results and failed to detect
cholesteatoma less than 5mm. [2], [3]
2. SINGLE-SHOT/MULTI-SHOT IMAGING
The main problem with this technique was that it
generates susceptibility and motion artifacts, due to
cardiac movement/blood vessel pulsation at the skull
base and takes longer imagining time. [4], [5]. Also it
failed to detect cholesteatoma of size 2-5mm. [6]
3. DELAYED MRI
The main disadvantage of Delayed MRI was : its
inability to detect cholesteatoma pearl less than 3mm
in size due to the effect of susceptibility artifacts. The
technique is promising but suffers from low spatial
resolution.[7][8]
Nupur Tewari et al, International Journal of Computer Technology & Applications,Vol 8(3),405-411
IJCTA | May-June 2017 Available [email protected]
405
ISSN:2229-6093
4. COMPUTED TOMOGRAPHY
Disadvantages of Computed Tomography were that it
was not able to differentiate between the
cholesteatoma tissue and other tissues such as
granulation or fibrosis present in middle ear cleft. [9],
[10].
5. DIFFUSION WEIGHTED ECHO PLANAR MRI
The drawbacks with this imaging modality are that it
is more prone to artifact at the air borne and air tissue
interfaces and can give rise to distorted image and
false positive signals[11].Another major disadvantage
is that small residual pearls, less than 5mm, can be
missed[12].
6. DIFFUSION WEIGHTED MRI
The study concluded that though DWMRI is helpful
in identifying repetitive cholesteatoma but still has the
inability to be consistently used for the evaluation of
the temporal bone as it is blocked by image distortion
caused by chemical shift artifacts, susceptibility
artifacts in the phase encoding direction. This is
mainly due to the high bone density of the inner ear
(present in the mastoid air cells and numerous air-
bone interfaces) [13].
III. ALGORITHMS
On the basis of disadvantages encountered during the literature
survey, we have proposed two algorithms that help in overcoming
the discussed limitations.
Algorithm 1
Our first goal is to differentiate cholesteatoma tissue with other
granulation, fibrous tissue etc.
Step1: Resize the image and convert it into a greyscale image.
When we convert a RGB image into greyscale image, it eliminates
the saturation and hue information, leaving luminance information.
Moreover greyscale is sufficient for the tasks.
Step2: After changing the image into greyscale. We will enhance
the contrast of the image. This step is done to improve the intensity
values of the image.
Step 3: We add the ‘salt & pepper’ noise, it is a kind of noise that
present itself like white and black pixels.
We usually add noise externally because original CT images
contain some kind of noises, and denoising them is quite a difficult
process in general. Therefore simulated noise is added, in order to
understand the concept of denoising.
Step 4: Now we remove the noise, or say filter the image. As we
have used salt and pepper noise in the above step, to remove that
noise, median filter is used.
Median filter is kind of nonlinear digital filter, used to filter the
image in order to to improve the image for later processing
Step 5: This is the last step in the algorithm. In this step we have
converted the image again into RGB image. The function used in
this step converts the filtered image into RGB with the purpose of
visualizing the labeled regions. This function assign color to each
object based on the number of objects in the label matrix
After this we will be able to differentiate cholesteatoma tissue with
other fibrous and granulation tissue.
Algorithm 2
The goal of this algorithm is to detect cholesteatoma tissue having
size less than or equal to 2-5mm.
Step 1: Resize the image and convert it into a greyscale image.
When we convert a RGB image into greyscale image, it eliminates
the saturation and hue information, leaving luminance information.
Moreover greyscale is sufficient for the tasks.
Step 2: After changing the image into greyscale. We will enhance
the contrast of the image. This step is done to improve the intensity
values of the image.
Step3: We increase the contrast of the image using histogram
equalization, as it will increase the global contrast. Use of this step
is as this method is useful where image background and
foreground are both dark and bright and will lead to better views.
Step 4: Now the next step involves addition of ‘Gaussian’ noise.
We usually add noise externally because naturally image contains
some kind of noises, but it is difficult to denoise them, therefore
simulated noise is added, in order to understand the concept of
denoising.
Gaussian noise is a type of statistical noise which includes
probability density function (PDF) that equalize to the normal
distribution also called as Gaussian distribution.
Step 5: To remove the added Gaussian noise, the best filter is
wiener filter. This filter is used to minimize the mean square root
error between the estimated & desired process.
Step 6: Now we use morphological operator- imopen. Usually
morphological operators are operators that take binary images and
structuring element as input and perform set operators on them to
combine them. Here we have used ‘imopen’ operator which is
erosion followed by dilation
Step 7: The last step includes performed Otsu’s gray thresholding.
This method performs image thresholding using clustering.
In this method The Otsu method chooses the threshold in order to
minimize the variance of interclass of the white and black pixels.
Here in this step, a cholesteatoma tissue of small size is easily
detected.
Here we have deliberately added some nose and then removed it
through median and wiener filtering to give it a practical approach,
as images cannot be acquired without some form of added noise.
IV. EXPERIMENTAL SETUP
Following is the hardware and software configuration of our
system, used during the research period.
Nupur Tewari et al, International Journal of Computer Technology & Applications,Vol 8(3),405-411
IJCTA | May-June 2017 Available [email protected]
406
ISSN:2229-6093
A. Hardware
Hardware Configuration:
Processor: Intel® Core™ i3 CPU 540 @3.07GHz 3.06GHz
Installed memory (RAM): 4.00GB (3.18 GB usable)
System type: 32 bit Operating System
B. Software
Software Configuration:
Windows edition: Windows 7 Professional
Matlab edition: MATLAB R2013a
We have performed our experiment on several image dataset using
the two proposed algorithms.
But have presented only 8 dataset with the finest outcome, these
dataset are the CT scan images available from the source
https://radiopaedia.org/.
Following are the 8 input dataset used during the experiment along
with the output produced from the two algorithms.
(a) (b)
(c) (d)
(e) ( f)
( g) (h)
Fig 1: Image (a),(b),(c),(d),(e),(f),(g),(h) shows the input data set
on which the two proposed algorithms have been applied.
V. FORMULAE AND CONCEPT USED FOR THE
COMPARISON OF THE ALGORITHMS
GLCM is an abbreviation that stands for Gray Level Co-
occurrence Matrix. It is a matrix of rows and columns that is equal
to the no. of gray levels (G) in the image. GLCM is an imperative
2-D histogram. GLCM Method considers the spatial relationship
between the pixels of the different gray levels.
Now on the basis of the outcome of the two algorithms we have
calculated the features of GLCM.
Here, 5 GLCM statistical features have been represented
graphically. They are as follows:
1. Energy =Σi, j P(i, j) 2 , Range :[0,1]
2. Correlation= Σ𝒊,j(i-𝝁𝒊 𝒋)(j—𝝁𝒋) P (𝒊,𝒋)/𝝈𝒊 𝝈𝒋 , Range: [-1,1]
3. Entropy = -sum (P.*log2 (P)) , Range: [0,1]
4. Contrast= Σ i, j|𝑖−𝑗|2 P (i, j) , Range: [0(size(GLCM,1)-1)^2]
5. Homogeneity = Σi, j P(𝑖, j)/1+| 𝑖−𝑗| , Range: [0,1]
VI. RESULT AND DISCUSSION
On the basis of the two algorithms i.e. algorithm 1 and algorithm 2,
results have been generated which shows that the algorithms have
produced the desired results. The Fig 2 shows the results of 8
samples of cholesteatoma after applying algorithm 1 to the input
data set (a),(b),(c),(d),(e),(f),(g),(h.) whereas the Fig 3 shows the
results of 8 samples of cholesteatoma after applying algorithm 2 to
the input data set (a),(b),(c),(d),(e),(f),(g),(h.).
From the result of the algorithm 1, we can see the cholesteatoma
tissue easily; as it can be differentiated from other tissues as the
cholesteatoma tissue becomes darker than the other parts.
Similarly, from the algorithm 2, we are easily able to extract the
cholesteatoma, despite of the differences in its size.
Now, for the texture analysis GLCM has been applied and its
graphical representation shows various statistical features which
can be used for comparing the various outputs of the inputs data set
used.
Nupur Tewari et al, International Journal of Computer Technology & Applications,Vol 8(3),405-411
IJCTA | May-June 2017 Available [email protected]
407
ISSN:2229-6093
The outputs of both the algorithms have been subjected to GLCM
and the five properties: Energy, Correlation, Entropy, Contrast, and
Homogeneity.
From the energy graph we can conclude that when GLCM is
applied to the raw input images data set the value of energy is less
due to the presence of various noise, whereas after performing
image processing the value of energy increases with the decrease
in noise and other unwanted data.
Correlation has been used to find the correlation between the two
neighboring pixels aver the whole image. It finds out the linear
dependency of the grey levels. Hence from our given data set we
can conclude that the correlation of the output image has been
increased in comparison to the given input set.
Entropy has been used to measure the randomness of the texture of
the input images. Hence with the refinements in the images the
entropy decreases.
On comparing the contrast of the input and the output image we
saw that the contrast decreases with the increase in the brightness.
Hence the background becomes brighter then the foreground
highlighting the cholesteatoma tissue.
Homogeneity measures the compression between the distributions
of elements in the GLCM. It is opposite to contrast, therefore from
the given graphs we can conclude that with increase in contrast the
homogeneity has been decreased for the given output images.
(a) (b)
(c) (d)
(e) (f)
(g) (h)
Fig 2: The Output results of 8 samples of cholesteatoma after
applying algorithm 1 to the input data set
(a),(b),(c),(d),(e),(f),(g),(h.)
(a) (b)
(c) (d)
(e) (f)
Nupur Tewari et al, International Journal of Computer Technology & Applications,Vol 8(3),405-411
IJCTA | May-June 2017 Available [email protected]
408
ISSN:2229-6093
(g) (h)
Fig 3: The Output results of 8 samples of cholesteatoma after
applying algorithm 2 to the input data set
(a),(b),(c),(d),(e),(f),(g),(h.)
Fig 4 : Table containing the results values of five statistical
features.
(i)
(j)
(k)
(l)
Nupur Tewari et al, International Journal of Computer Technology & Applications,Vol 8(3),405-411
IJCTA | May-June 2017 Available [email protected]
409
ISSN:2229-6093
(m)
Fig 5: Graphical representation of the statistical feature of
GLCM on the basis of the output generated from the two
algorithms.
VII. CONCLUSION
Cholesteatoma is a middle ear disease which when discovered in
an early stage can be cured through surgery, further its delay can
lead to severe outcomes, like deafness, facial paralysis, etc.
During our research work we came across two major difficulties:
first was the size problem which leads to the difficulty in
identifying cholesteatoma in an early stage whereas the other was
its differentiation with the other granule tissue.
In this research we have proposed two algorithms that will help to
overcome the difficulties encountered during the detection of
cholesteatoma. Our initiative can be further enhanced with more
quantitative measurements.
VIII. ACKNOWLEDGEMENT
We are highly grateful to Dr. G.N. Purohit (Dean, AIM & ACT)
and Prof. (Dr.) C.K. Jha, (HOD, AIM & ACT), Banasthali
University, for providing the opportunity to carry out internship
program from Banasthali University.
We would also like to express my gratitude to Dr.Saurabh
Mukherjee and Mr. Khandakar Faridar Rahman , without their
encouragement and guidance; it would have not been possible to
carry out our research.
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IJCTA | May-June 2017 Available [email protected]
411
ISSN:2229-6093
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