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Global Journal of Computer Science and Technology: F
Graphics & vision
Volume 18 Issue 1 Version 1.0 Year 2018
Type: Double Blind Peer Reviewed International Research Journal
Publisher: Global Journals
Online ISSN: 0975-4172 & Print ISSN: 0975-4350
New Algorithm for Detection of Spinal Cord Tumor using
OpenCV
By Raihan Uddin Ahmed, Tanvir Ahmed Chowdhury & A. S. M Mahmudul Hasan
The International University of Scholars
Abstract- The spinal cord, one of the most sensitive and significant parts of the human body, lies
protected inside the spine (the backbone) and contains bundles of nerves. Any minor problem in the
spinal cord can cause debilitation of internal and external functions of the human body. One of the
complications in the spinal cord is tumor - abnormal growth of tissue. In this project, we present a
new algorithm based on OpenCV to detect spinal cord tumors from MRI sagittal image without
human intervention. The new algorithm can detect tumor-like substances adjacent to the spinal cord.
Tests carried out on spinal cord MRI images 33 cervical spinal images showed approximately
90.91% of accuracy rate in detecting tumors.
Keywords: medical image processing, spinal cord, tumor, MRI, OpenCV, computer aided diagnostics.
GJCST-F Classification: I.3.3
NewAlgorithmforDetectionofSpinalCordTumorusingOpenCV
Strictly as per the compliance and regulations of:
New Algorithm for Detection of Spinal Cord
Tumor using OpenCV
Raihan Uddin Ahmed α, Tanvir Ahmed Chowdhury σ & A. S. M Mahmudul Hasan ρ
Abstract-
The spinal cord, one of the most sensitive and
significant parts of the human body, lies protected inside the
spine (the backbone) and contains bundles of nerves. Any
minor problem in the spinal cord can cause debilitation of
internal and external functions of the human body. One of the
complications in the spinal cord is tumor - abnormal growth of
tissue. In this project, we present a new algorithm based on
OpenCV to detect spinal cord tumors from MRI sagittal image
without human intervention. The new algorithm can detect
tumor-like substances adjacent to the spinal cord. Tests
carried out on spinal cord MRI images 33 cervical spinal
images showed approximately 90.91% of accuracy rate in
detecting tumors.
Keywords: medical image processing, spinal cord,
tumor, MRI, OpenCV, computer aided diagnostics.
I. INTRODUCTION
pinal cord [1] resides inside the spine of a human
body (Fig. 1). The Central Nervous System
consists of the brain and the spinal cord [2]. The
role of the central nervous system is to control the
majority of the functions of both the body and the mind.
The spinal cord has tubular bundle of long and thin
nervous tissue. It also have other support cells which
extends to the lumbar region of the vertebral column
from the medulla. It’s around 45 cm (18 inches) in men
and approximately 43 cm (17 inches) long in women.
Thirty one pairs of spinal nerves rise because of that [7].
Fig. 1: The spine of a human body
Author α: Department of Computer Science & Engineering, The
International University of Scholars, Dhaka, Bangladesh.
e-mail: ruabng2008@gmail.com
Author σ: Infomation Technology (IT), University of Information
Technology & Sciences, Dhaka, Bangladesh.
e-mail: tanvir.latim@gmail.com
Author ρ: School of Science Technology, Bangladesh Open University.
e-mail: mahmud.bou@gmail.com
a) Spinal Cord Tumor
A spinal cord tumor [3] is an abnormal mass of
tissue within or on the surface of the spinal cord or
spinal column (Fig. 2). Spinal cord tumors generally
develop slowly and worsen over time. But the symptoms
are visible at a very early stage because spinal cord
controls the many functions of the body like movements
and senses. In most cases, an affected spinal cord
becomes bent or distorted, that causes loss of bodily
functions. It is essential to detect spinal cord tumors at
an early stage. An automated system for this purpose
can reduce the time and effort required to diagnose the
tumors and thus help the treatment process.
Following are some facts [6] about spinal cord
tumors around the world:
- 0.74 of the population in 100,000 are affected per
year by spinal cord tumors.
- This rate becomes 1.80 for the 75-84 age group.
- Only 64% among these people survives for ten
years if they are able to detect spinal cord tumors at
a primary stage.
Fig. 2: Spinal cord tumor
The spinal cord tumors can be either cancerous
or non-cancerous. Another classification can be as
follows:
- Extradural - outside the dura mater lining (most
common)
- Intradural - part of the dura
- Intramedullary - inside the spinal cord
- Extramedullary- inside the dura, but outside the
spinal cord
Spinal tumors may occur in the areas like
cervical, thoracic, lumbar, and sacrum.
b) Spinal Cord Tumor Diagnostics: MRI
One of the most common diagnosis processes
for Spinal Cord tumor is MRI. The full form of MRI [8] is –
S
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“Magnetic Resonance Imaging.” It is considered to be a
better diagnostics method than X-ray, ultrasound, or
computerized tomography (CT) scan because it gives
more information about the structures of the body.
According to Wikipedia, there are many medical
imaging techniques like MRI (Magnetic Resonance
Imaging), NMRI (Nuclear Magnetic Resonance
Imaging), or MRT (Magnetic Resonance Tomography)
to visualize the structure and the physiological
processes of the body. Using strong magnetic fields,
radio waves, and field gradients, MRI scanners form the
images of the body.
Use of MRI images (Fig. 3) in the detection
process of spinal tumors gives better results in terms of
accuracy. That is why, in this research, MRI scans of the
spinal cord are used to detect the anomaly.
Fig. 3: MRI image of the cervical spine
c) Medical Image Processing
Medical Image Processing is an extended
version of image processing. By using medical images,
it is possible to diagnosis the human organs
automatically. The main challenge behind medical
image processing is that the images need to have a very
high level of accuracy along with sufficient details in
each case of use, as it is directly related to human
health. Medical image processing techniques are
improving rapidly day by day, and many automated
applications are being available in medical diagnostics.
These are reducing the time required for finding
diseases and batch processing of images and thus
accelerating the treatment process along with the
chances of survival.
II. RELATED WORKS
In the paper titled “Diagnosis of Disc Herniation
Based on Classifiers and Features Generated from
Spine MR Images”[10], Jaehan Koh, Vipin Chaudhary,
and Gurmeet Dhillon discussed using perceptron, SVM,
and Least-mean-square after segmentation and feature
generation of lumbar vertebrae to identify disk
herniation. They gained 97% accuracy.
E. Lopez Arce-Vivas, Francisco Javier Cisneros,
Rq Fuentes, Alejandro García-González, J. Gonzalez-
Cruz, and Jose Maria Jiménez-Avila also worked on disk
herniation using semiautomatic approach. But their
accuracy rate was low. They published their word with
the title- “Application of a semiautomatic classifier for
Modic and disk hernia changes in magnetic
resonance”[11]. It is not that much effective to use for
clinical diagnosis. The outcome of the research shows
that it has only 60-65% and 58-61% certainty for the
MODIC classification and herniated discs respectively
as compared with clinical experience. This inspired us to
develop a semiautomatic classifier with effectiveness of
diagnostic reliability as doctors.
In their paper “Computer-Aided Diagnosis of
Lumbar Disc Herniation”[12], Khaled Alawneh, Mays Al-
dwiekat, Mohammad Alsmirat and Mahmoud Al-Ayyoub
proposed methods based on ROI Extraction, ROI
Enhancement, Feature Extraction, Classification
Algorithms or Classifiers to auto-diagnose lumbar disk
herniation. Their method shows 100 % accuracy rate.
B. Karlık and S. Kul show 95% accuracy in the
detection of disk herniation using Artificial Neural Net.
They described their work in the paper “Diagnosis of
Lumbar Disc Hernia Using Wavelet Transform and
Neural Network” [13].
In the paper titled “Computer-Aided Detection
of Brain Tumor in Magnetic Resonance Images”[14],
Abhishek Raj, Alankrita, Akansha Srivastava and Vikrant
Bhateja worked on the automated diagnosis of brain
tumor using Wavelet Transform, Multi-scale Analysis,
Morphological filtering, Wavelet-Based Sub-band
Coding, Non-linear Enhancement Operator.
None of these scholarly works try to identify
spinal cord tumor from MRI images. They mostly deal
with a brain tumor or herniation of protective disks in the
spinal column.
III. PROPOSED SYSTEM
The proposed system has three major modules.
The first module involves image retrieval of images and
their normalization to make all images of the same width
(Fig. 4). The second module enhances the images by
blurring noises, detects edges for finding out the ROI
(Region of Interest) and then applies segmentation to
isolate the ROIs. The final module analyzes the ROIs
and makes a final decision based on the novel Simple
Average Deviation (SAD) algorithm.
Fig. 4: System Processes for Spinal Tumor Detection System
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a) Image Retrieval
The first step in image processing is the retrieval
of images. We have implemented the retrieval process
for a single picture only.
Fig. 5: The image retrieval and normalization
The retrieved images are converted to grayscale
to simplify the processing with only two colors – black
and white. It also makes it easier to generate binary
images later on or analyze the color histogram. The
picture also normalized so that all have a standard width
of 294 pixels. To reshape the image and calculate the
area based on the width only, we keep the height of the
pictures relative to the original aspect ratio. This
approach gives the optimum result in an application.
b) Image Enhancements
In the enhancement stage, a blurring filter is
applied to reduce noises in the image. We have used
the Gaussian filter [17] for this purpose.
To implement the Gaussian blur (mainly used
as a smoothing operator) needs to be careful.
Fig. 7: Image after enhancement
As it can hide some crucial details of the image
in the process of blurring noises from the image (Fig. 6
and Fig. 7).
c) Thresholding
A thresholding process usually applies a fixed
level threshold to the whole image. It takes a threshold
pixel range through its parameters and creates a binary
version of the image (black and white) depending on the
threshold.
Fig. 8: An MRI image after thresholding
In our system, we have used the Adaptive
Threshold [19] technique, which is a better version of
the original threshold.
d) ROI and Edge Detection
In this stage Region of Interest (ROI), the spinal
cord is determined. Another reason for indentifying ROI
is that some parts of the spinal cord lie in line with the
boundary edges. It makes the edge detection difficult.
On the other hand, grabbing the ROI helps us to get a
fixed height of the spinal cord, which is very useful in the
segmentation of a spinal cord (Fig. 9).
After determining the ROI, the Canny algorithm
for edge detection is used to find the connected
spinal cord.
Figure 9: Image after edge detection
The Canny algorithm has less error rate and has
a localization meaning that the distance between edge
pixels are easily detected. It works best in our system as
we compared it to other edge detection methods like
Sobel [20].
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Fig. 6:Image before enhancement
e) Segmentation
After detecting edges for the connected
regions, segments were separated (Figure 10) by
filtering them according to their area and height.
Fig. 10: Segmentation result of a spinal MRI image
Then the whole connected region was
calculated, and we preserved the X and Y coordinated
of the spinal column for later use by the tumor detection
algorithm called Simple Average Deviation (SAD)
algorithm.
f) The SAD Algorithm
The Simple Average Deviation (SAD) algorithm
is the contribution of this paper. It’s developed to find
any irregular deviation of the curves in the spinal cord
boundary. We have applied a prototype of the algorithm
which can determine whether a spinal cord is normal or
not. If a spinal cord doesn’t exist in the list of contours
found in the segmentation stage, then we can say that
the spinal cord has tumors or another type of
abnormalities. Our prototype can also detect tumors at a
primary stage if they are present at the edge of the
spinal cord itself. The technical specification of this
algorithm is as follows-
The spinal cord has two balanced curves on
both sides. By those, we mean the curves that don’t
have small waves having wavelength less than a
threshold. There will have small waves if the body of the
spinal cord has tumors attached to it. We have
implemented the algorithm by a simple function named
simple Average Deviation (). This function needs some
values captured at the segmentation stage. We have
saved the coordinates of the edges from the respective
region which we got by filtering them. Then we have
passed the dynamic array of X coordinates, the
minimum value among the Y indexes, and the maximum
value among the Y indexes. The minimum and
maximum Y index value is required to ensure top to
bottom traversing of the coordinate values and also to
analyze both sides of the spinal cord.
In the core of the implementation, we divide the
X coordinates into different sequential blocks of 10
values each.
- Let, denote the first block of 10 coordinates =
block1
- Let, the total number of blocks = n
- Let, the first value among the X coordinates inside
block1 = x1
As there are ten coordinates in each block, we
can represent the sum of X coordinates inside block1 as
x1+x2+…………+x10. So, the equation will be,
- sumBlock1 = x1 + x2 + ………… + x10, where
sumBlock1 is the sum of all values inside block1.
Now we can say,
- avgBlock1 = sumBlock1 / 10, where avgBlock1 is
the average of all x coordinates inside block1.
Similarly, sumBlock2 and avgBlock2 are
representing the sum and the average of all values
inside block2 respectively. The respective equations can
be represented as,
- sumblock2 = x11 + x12 + ………… + x20
- avgBlock2 = sumBlock2 / 10
Once we have all the averages, we can easily
find the differences among them. We can then consider
these differences as the deviations and compare
against a threshold. We figured out that it can find
abnormalities if the threshold is ranging from -16 (minus
16) to 16.5. The reason behind setting a positive and a
negative limit for the range is that the waves caused by
tumors can deviate either on the left edge or the right
edge of the spinal cord or even both. We also found out
that a single instance of abnormally didn’t point out to
the existence of tumors in any of our test cases. It might
simply indicate that the segment we are dealing with
isn’t smooth in all edge points. So, we can presume that
there have to be at least two instances where we find
average differences not following the thresholds as
mentioned above.
Let, the positive threshold limit = pt, the
negative threshold = nt and the difference between the
first block average and the second block average =
diff1.
Then, diff1 = avgBlock1 - avgBlock2
Similarly, diff2 = avgBlock2 - avgBlock3 and so on.
According to the findings above, if diff1 < nt or
diff1 > pt then we can say that the first abnormal
deviation, abn1 resides in either block1 or block2
(the first two blocks) or both.
If we find another abnormal deviation for the
average values then we can assume that a tumor might
be present in the current segment. Otherwise, we can
assume that there are none in this segment.
We have used the Y coordinate values in the
actual implementation from the contours for
distinguishing between the left edge and the right edge
separately as that increases the \chance of proper
detection of tumors.
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Fig. 11: The spinal tumor detection result
IV. EXPERIMENTATION METHODS OVERVIEW
Here, we used a data set of 33 cervical MRI
images. We collected those from different medical
archives and via google search. The images are of
different dimensions. One common aspect of those is
that all of them consist of a part of the spinal cord, which
is directly attached to the brain. Ten of those images are
of spinal cords with no abnormality, which means that
there is no tumor visible on them. The other 23 either
have tumors or have another type of abnormalities like
distortion, too many noises, a very few details and so
on. Images were used for academic purpose only as
permitted by the authors. Thus, they preserve the
copyrights.
We gather the images in a single folder from the
dataset. Each of these images was opened one by one
and tested using the system. Fig. 12 shows the results
collected from these tests.
Fig. 12: The spreadsheet for collecting
experimentation results
Here in Fig.12, the spreadsheet has some
labels that we used for different purposes. We used the
“data name” label for storing test image names. The
“Original Condition” label for storing the information
about those test images whether they have a spinal
tumor in or not. The "Test Result” label for storing the
feedback from our application prototype (whether the
system found out tumor or not). The “Result type” label
for storing a single color for each image- either green or
red. ‘Green’ specifies an accurate identification and ‘red’
specifies a wrong indication by the application etc. the
labels named “Type color” and “Type name” describe
the result types.
V. RESULTS AND DISCUSSIONS
Our system identifies a spinal cord as normal if
and only if it finds a single instance of the spinal cord
after applying the SAD algorithm. We used a dataset
consisting of 33 cervical MRI images (10 without
abnormality and 23 affected with tumors or distorted),
and our system acquired up to 90.91% accuracy.
a) True Positives
We have used the term “true positive” whenever
we found the case where the original image has a spinal
cord without abnormality and the application also states
it as the same. We have found out eight true positives in
total from the experiment.
b) True Negatives
We have used the term “true negative”
whenever we found the case where the original image
has a spinal cord with some abnormalities or tumors
associated with it, and the application also states that
the spinal cord is not normal. By the end of the
experiment, we have found out twenty two such cases.
c) False Positives
We have used the term “false positive”
whenever we found the case that the original image has
an abnormal spinal cord, but our application says that
the spinal cord is normal. We found one false positive in
our experiment.
d) False Negatives
We have used the term “false negatives”
whenever we found the case that the original image
represents the spinal cord as normal, but the application
denotes that it has an abnormal or defective spinal cord.
We found two false negatives in our experiment.
e) Result Analysis
The “true positives” and the “true negatives”
yield the total number of accurate detection, which is 30
in total and 90.91 in percentage. The “false positives”
and the “false negatives” shows the total number of
errors in the detection process, which is 3 in total and
9.09%. We can conclude that our system has 90.91%
accuracy, which is a decent one as the research topic is
unique. The following Figure (Fig. 13) shows the result
statistics-
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Fig. 13: The Statistics of the Result Derived from the
Experimentation
VI. LIMITATIONS OF THE SYSTEM
The proposed system has the following weaknesses:
- Unable to detect tumors adjacent to the spinal canal
wall.
- Unable to detect if there is too much noise around
the spinal cord.
- The system was tested only on the sagittal view of
the cervical spine region as generated by an MRI
scan.
- The accuracy level (90.91%) needs to be improved.
The system can’t identify any characteristics of
the tumor other than its location; it can’t distinguish
between a malignant and a benign tumor.
VII. CONCLUSION
The main objective of this research was to
develop an automated way of finding tumors in Spinal
Cords by computer vision tools like OpenCV and using
various image processing techniques. We have
developed an algorithm (Simple Average Deviation or
SAD) to find irregularities in the shape of the Spinal
Cord. The algorithm shows 90.91% accuracy in
detecting tumor/abnormalities. Currently, the algorithm
only works on sagittal MRI images and also suffers
several other limitations. We will work on those in the
future.
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