Digital Image Processing for Diagnostics of Middle Ear Inflammation Jessica L’Heureux, Georgia McAdams, Joanna Wycech

Faculty Advisor: Professor Lin Cheng

Trinity College, 300 Summit Street, Hartford, CT, 06106, USA

Results

Training set: 23 images

Testing set: 10 images

9/10 images were correctly diagnosed

90% accuracy rate

Abstract Otitis Media, an ear infection present in children, is one of the most

commonly misdiagnosed infections in the country; a large

contributing factor to this is the use of otoscopes with the naked eye

for diagnosis. Recently, digital otoscopes have been created that

give the doctor the option of taking digital images that can then be

examined more closely and in greater detail. The objective of this

senior design project was to develop a program in MATLAB using

already existing image processing kernels and techniques, which

would determine if an infection is present and whether it is Acute

Otitis Media or Otitis Media with Effusion. This will provide a reliable

second opinion for a physician to make his diagnosis. Images taken

by Dr. Tulio Valdez from Hartford Children’s Hospital with digital

video otoscopy devices were used in this project. Filtering

techniques, segmentation, light levels, analyzing several biological

landmarks that are characteristic of this infection were all used to

create a final product that can detect Otitis Media with reasonable

accuracy.

REFERENCES & ACKNOWLEDGMENTS Professor Lin Cheng, Trinity College

Professor John Mertens, Trinity College

Dr. Tulio Valdez, Hartford Children’s Hospital

Alberti, P.W. The Anatomy and Physiology of the Ear and Hearing. Retrieved from

http://www.who.int/occupational_health/publications/noise2.pdf

Thrasher, R.D. (2013, April 25). Otitis Media With Effusion. Retrieved from:

http://emedicine.medscape.com/article/858990-overview#showall

Rachamanidou, A. Otitis Media. Retrieved from: http://www.ent-

matters.co.uk/files/Otitis_media.jpg

Berman, S. Normal Tympanic Membrane. Retrieved from:

http://meded.ucsd.edu/clinicalmed/EarTM.jpg

Keeley M.G. (2011, September). Acute Otitis Media: 6 Steps to Improve Diagnostic Accuracy.

Retrieved from: http://www.pediatricsconsultant360.com/content/acute-otitis-media-6-steps-

improve-diagnostic-accuracy

Rothschild M. Otitis Media with Effusion. Retrieved from:

http://www.kidsent.com/images/clinical/nl_ome.jpg

Cheng, L., Liu, J., Roehm, C.E., & Valdez, T.A. Ear Image Analysis from a Digital Video

Otoscope Prototype using Laser.

McAndrew, A. (2004). Introduction to Digital Image Processing with MATLAB. Thomson Course

Technology.

Bubble Analysis

The presence of bubbles is the main characteristic of OME

This part of the program uses double thresholding to reveal

all possible bubble-like shapes

They are then segmented out and filtered by pixel diameter

and area to determine the final number of bubbles present

This is an accurate indicator of OME

K-Means Clustering

The ability to segment out the auditory bone often rules out the

presence of an infection

K-Means color based segmentation is used to determine if the

bone is visible

The image is separated into specified number of clusters and

grouped based on the Euclidean distance between each point

and the centroid of the cluster

Cluster 3 is further filtered and evaluated to determine if the ear is

healthy or unhealthy

Background

The middle ear consists of the tympanic membrane, cavity, and

bones

Otitis Media is the inflammation of the middle ear and can be

separated into two categories:

Otitis Media with Effusion (OME) – the ear becomes filled

with a thick fluid

Acute Otitis Media (AOM) – the tympanic membrane

become inflamed and bulges outward

Diagnostic Uncertainties include:

Difficult to distinguish between OME & AOM

AOM is frequently over diagnosed

Central Concavity

The presence of a donut-like shape is the main characteristic

of AOM

After filtering, this part of the program allows a physician to

manually zoom in on the circular abnormality and place a

cursor in the center

Then the pixel intensity is evaluated at that specified point

If the intensity is less than the threshold, this indicates AOM

is present

If the intensity is greater than the threshold, this indicates no

central concavity

Figure 1: (a) Healthy Middle Ear (b) OME (c) AOM

Figure 4: Bubble Analysis

Figure 2:Color Based Segmentation K-Means Clustering of a Healthy Ear

Figure 3:Cluster 3 Evaluation for (a) Healthy Ear (b)OME (c) AOM

Figure 4: (a) Central Concavity Present (b) No Central Concavity Present

Vascularity

Branching vascularity is a prominent feature in OME,

whereas in AOM parallel alignment of vessels is present

along the donut

Matched filter detection is used to detect piecewise linear

segments of blood vessels

The green channel in the RGB image is selected for

processing, and after the filter kernel is applied only the

maximum of the response is retained

Based on the range of the response, categorization of the

infection type is made

Figure 5: Vessel Extraction in a AOM Infected Ear

Truth Table for Results

The truth table for results represents the process by which

a healthy ear, OME, or AOM was detected through the

MATLAB program

Table 1: Truth Table for Results