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]

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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.

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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.

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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)

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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)

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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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