NUI Galway – UL Alliance First Annual ENGINEERING AND - ARAN ...

Image Recognition and Classification of tuberculosis Bacilli.
Abstract
In this project we present a new method for identifying
tuberculosis in microscopic images of sputum. The
problem with automatic identification is that in an
image there are many other objects that are not the
bacillus. In order to be able to correctly identify the
objects we use a method called shape profile that
creates a profile based on the distance from the centre
of an object to its border. The bacilli shapes have a
similar profile to each other but are different enough to
other objects to distinguish between them. We then use
dynamic time warping to perform nearest-neighbour
classification on the objects.
1. Introduction
Tuberculosis detection is done by obtaining a sputum
sample from a patient and examining it under a
microscope for bacilli.
The problem with manual bacilli detection is that it is
a very tedious task which requires skilled personal
resources and can lead to low sensitivity. Depending on
their angle and the position of the bacilli there can be
much dispute about what a bacillus is as the shape of
the bacillus can vary greatly from cane shapes Figure
1(a) and concave Figure 1(b). Sometimes even doctors
will dispute what a bacillus is.
Image detection has taken many forms before the
most obvious tell tale of any bacillus is its bright
florescent color which has been used before to identify
them [1].The profile method we are using has been used
to identify tumors in cancer tissue [2] and predict the
models of cars by measuring and comparing the profiles
of objects found in images [4].
Figure 1(a).Cane-shape Figure 1(b).Concave (c).Image after
edge detection of (a).
2. Method
In order to create our profile we convert the image to
a grey scale image and apply the Sobel edge detection
algorithm on it Figure 1(c). We pick out all objects with
a closed boundary with a certain area threshold; this
allows us to discard objects that would be too large or
too small to be a bacillus. Taking our acquired objects
boundary we first find our centre point and convert the
pixel coordinates from Cartesian to polar.
Every boundary pixel has two values r, which is the
distance between it and the centre, and θ which is the
angle. To normalize our profiles we start from the pixel
John O Malley
Ricardo Santiago-Mozos; Michael G. Madden
j.omalley8@nuigalway.ie
37
which has the greatest r and we set its θ to 0 and set our
last θ to 2π.
2.1 Shape Profile Representation of Bacilli
In order to get high classification results our shape
profiles must be distinguishable from non bacilli
objects. The problem with this is that bacilli can have
many different shapes. This can lead to incorrect
classifications of both bacilli and non-bacilli objects.
Once we have our θ and r values for each pixel we can
represent them on a graph. One problem that may occur
in bacillus of concave shape is that θ does not increase
in a monotonic manner and leads to inconsistent
representation of our objects.
To get around this problem we edit the θ values slightly
and output them in pixel order. This allows us to
interpolate our profile and create a vector of a fixed
length even though we may have different size objects.
Distance from centre
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6
5
4
3
2
1
0 1 2 3 4 5 6 7
Angle
Figure 3.Profile of figure 1(c)
3. Classification
In order to classify our profiles we will be using
dynamic time warping [3] and K-NN. This works by
obtaining the distance of the test profile to the training
profiles using DTW and then outputting the class of the
closest match. That is the sample with the minimum
distance.
4. References
[1.] M. Forero, F. Sroubek, and G. Cristóbal, Identification of
tuberculosis bacteria based on shape and color. Real-Time
Imaging 10:251—262, 2004
[2] .J. Thiran and B. Macq. Morphological feature extraction
for the classification of digital images of cancerous tissue.
IEEE Transactions on Biomedical Engineering 43:1011—
1020, 1996.
[3] H. Sakoe and S. Chiba. Dynamic programming algorithm
optimization for spoken word recognition. IEEE Transactions
on Acoustics, Speech and Signal Processing 26:43—49, 1978.
[4] D. Munroe, M. Madden, Multi-class and single-class
classification approaches to vehicle Model recognition from
images. Proceedings of IEEE AICS, 2005