1. Texture Analysis Diagnosis of Osteoporosis
Suhani Pant2,3, Vasiliki N. Ikonomidou1
1George Mason University Bioengineering Department, 2Governor’s School @ Innovation Park, 3Osbourn Park High School
ResultsMethods
Conclusions
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Introduction
2015
ReferencesAcknowledgements
Data used for this research were obtained
from the Osteoarthritis Initiative Database.
George Mason University’s facilities and
funding were utilized to conduct this research.
Mrs. Amy Adams provided her guidance
throughout the Aspiring Scientists Summer
Internship Program. Vikas Kotari supervised
the familiarization process of the MIPAV
software. Special thanks to Dr. Vasiliki
Ikonomidou for constantly providing support
and direction throughout the project.
1Osteoporosis is a disease that causes thinning and
weakening of bones in a silent and progressive manner. More
than 10 million people in America have osteoporosis and half
of women aged 50 and older will have an osteoporosis
related fracture in their lifetime. Since the disease does not
display any symptoms until a fracture occurs, it is often left
undiagnosed. 2When fractures occur in older people, they
become bed ridden and their limited mobility leads to failure
of other body organs.
DEXA scans are currently utilized to diagnose osteoporosis
through detection of bone density ratio. A setback of this
method is that it is often not prescribed to patients until an
older age, and even when it is, the recommendations are
ignored. Thus, medical scans that patients are already
receiving, such as magnetic resonance imaging (MRI), can
be used for further assessment at a minimal cost. 3Texture is
the characteristic of an image based on the distribution of
gray-tone levels; the differences in gray-tone between
neighboring pixels of an image prompts the quantification of
values corresponding to what is known as object texture. This
research study used texture analysis techniques on knee MRI
images of patients receiving medicine for osteoporosis and of
healthy patients to quantify differences in texture.
• Patients who matched the criteria of Caucasian
females aged 50-59 years were selected from the
Osteoarthritis Initiative Database, 4a study that
evaluated biomarkers of osteoarthritis.
• Two categories of twenty patient images were created
based on whether the patient took bisphosphonates to
treat osteoporosis or not.
Selection of
patient
images
• Regions of interest (ROIs) were drawn
surrounding the medial and lateral epicondyles
and condyles on each of the COR_IN_TSE_LEFT
images with the use of Medical Images
Processing and Visualization (MIPAV) software.
• The mask of each image was created.
Drawing of
ROIs
Analysis of
mean values
COR_IN_TSE_LEFT image ROI is drawn on the image Image is masked
The code above multiplies the masked image
with the original patient image to isolate the
ROIs and outputs a set of mean values.
Each patient had a set of MATLAB mean values
that was used to perform t-tests and create box-
and-whisker plots based on different rows in Excel.
1Zelman, D. (2015, February 27). What is
Osteoporosis? - WebMD. Retrieved July 23, 2015.
2“Osteoporosis: A Resource from the American College
of Preventive Medicine,” ACPM: American College of
Preventive Medicine, 2009.
3R. M. Haralick, K. Shanmugam, and I. Dinstein,
“texture Features for Image Classification,” IEEE
Transactions on Systems, Man and Cybernetics, vol.
SMC-3, no. 6, pp. 610–621, Nov. 1973.
4“Study Overview and Objectives,” Osteoarthritis
Initiative: a knee health study, 2013. [Online]. Available:
http://oai.epi-ucsf.org/datarelease/StudyOverview.asp.
Retrieved July 23, 2015.
Image courtesy of Your Home Companion
The results from t-Tests show that there is a significant difference in the knee
MRI images obtained from patients taking medicine to treat osteoporosis and
patients not taking medicine. Therefore, texture analysis is a viable method to
use to detect osteoporosis in patients.
pant.suhani97@gmail.com
• The patient images and their masks were multiplied using
MATLAB and a set of mean values were obtained. These values
were used to conduct two-sample assuming unequal variances t-
Tests for the two patient groups.
• Box-and-whisker plots were created for the mean value data from
each experimental group to differentiate between the two.
Rows P-values
3 0.012718
4 0.015685
5 0.006502
9 0.009937
19 0.016945
22 0.010435
37 0.007393
41 0.005151
This table represents the P-
values obtained from t-Tests
performed for the medicine
and no medicine patient
groups.
Statistical analysis shows that
there is a significant difference
among the two patient groups.
This box-and-whisker plot
represents the distribution
of the mean value data for
Row 3 of each patient
group. Although the data
were spread out through
similar ranges for both
groups, it is evident that
the means and distribution
were separate, which
allows for the two patient
groups to be distinguished.
0.00008 0.0000820.0000840.0000860.000088 0.00009 0.0000920.0000940.0000960.000098 0.0001
Medicine Group
No Medicine Group
Mean Values
Mean values distribution for Row 3