Segmentation and counting of elongated objects in urine smear microscopy images
1.
Segmentation and Counting of Elongated Objects in
Urine Smear Microscopy Images
Shaeez Usman Abdulla
Dept. of Electronics and Communication Engineering
College of Engineering, Trivandrum
Kerala, India, PIN-695016
(E-mail: shaeez@gmail.com)
Abstract—This paper describes a new method for segmenting
and counting elongated objects, like E. coli, in urine smear
microscopy images. This work was undertaken as a part of developing an automated system for detecting signiﬁcant bacteriuria
based on the distribution density of Gram-Negative Bacilli in
urine smears.
The smear image was ﬁrst subjected to intensity thresholding. The number of members represented by each connectedcomponent was obtained using a Radon Transform-based dimensional analysis. The proposed method was implemented on
six test images and obtained an 98% average accuracy.
Index Terms—Medical Microscopy Image Analysis, Urine
Smear, E Coli, Gram-Negative Bacilli, Radon Transform.
I. I NTRODUCTION
U
RINARY TRACT INFECTION (UTI) is one the most
commonly occuring infection in humans, and also the
most common cause of both community-acquired and nosocomial infections for patients admitted to hospitals [1]-[3].
Since 90% of infected cases have bacterial etiology and require
antimicrobial therapy, physicians rely on several microbiological laboratory techniques and parameters to determine the
species and severity of bacteriuria1 [4]. The presence of 105 or
more colony forming units (CFU) per ml of urine is generally
considered as signiﬁcant bacteriuria [3], [5], [6].
The most popular method is the detection of bacteria in a
urine culture. It is considered as the diagnostic “gold standard” for UTI. Unfortunately, culture results do not become
available until 24 hours after the patient’s presentation, and
identifying speciﬁc organism(s) can require an additional 24
hours [5], [6].
Spotting of pathogens in urine smears is taken to be the ﬁrst
clue that infection is present. Extremely wide variations in the
presentation of microscopic images makes their segmentation
a very tricky affair. Most popular segmentation techniques in
this area depend on ellipse matching, color and brightness
information etc., to differentiate between different parts of the
image [7]-[11].
Cluster formation makes it hard to differentiate between
members. Kothari et al. utilized concavity detection and ellipse
ﬁtting for the automated cell counting and cluster segmentation in images of tissue samples [12]. Automatic embryonic
stem cells detection and counting in ﬂourescence microscopy
1 Bacteriuria is deﬁned as the presence of bacteria in urine not due to
contamination from urine sample collection [1].
images is done in [13] by using the luminance information.
Benzinou et al. and Habibzadeh et al. have relied on size
estimation for cluster quantiﬁcation while analysing blood
smear images [14], [15]. Sio et al. addressed the problem of
parasitemia estimation in blood smears using edge detection
and splitting of large clumps made up from erythrocytes [16].
Xue et al. describes a scheme for segmenting overlapped E.
coli using information like center-of-mass and orientation.
Chord Length Transform was used to convert the 2D image
into a 3D volume. The Radon Transform is then applied to
help segment this volume [17].
In this paper, a new computer-aided approach is proposed
for automated segmentation and counting of elongated biological objects in urine smear images. The accuracy of the
proposed algorithm is tested using six simulated images.
The methodology is described in Section II. Section III
presents the experimental results and Section IV states the
conclusions and future work.
II. P ROPOSED A LGORITHM
A. Cluster Analysis
Fig.1(a) shows a microscopy image of a typical urine
smear obtained after gram staining, showing the presence of
Gram-Negative Bacilli. Upon examining various dimensional
parameters of the objects, it was noted that the maximum
length of a large portion of bacilli in an image belonged to the
range of 15 - 45 pixels. Fig.1(b) shows the maximum length
analysis graph of each connected element in Fig.1(a).
Each connected component in the image was classiﬁed
into different groups based on their maximum length (Only
lengths falling in the range of 15-45 pixels were taken into
consideration). The group having largest number of members
was taken and the most repeatedly occuring length was noted,
and was taken as standard length. Minimum of the area
individually covered by members having standard length was
taken as standard area. The counting algorithm was designed
based on the following assumptions:
1) All the connected components having an area less than
the standard area is assumed to be consisting of a single
member.
2) All the connected components having an area greater
than the standard area is assumed to be clusters made
of members having standard area.
2.
Fig. 1. (a) A typical urine smear image. The dark objects seen are Gram-Negative Bacilli. (b) A graph showing the maximum length of members in Fig.1(a).
B. Counting Algorithm
1) The image K was subjected to intensity thresholding at
0.7 luminance threshold, inverted, and noise ﬁltered to
get the binary image Kb .
2) Let the image Kb contain n connected components and
let Bi be the ith connected component. Each of the
component was subjected to Radon transform analysis.
Ri (ρ, θ) represents the values returned by Radon transform operation on component Bi (See Appendix A).
3) Rmax (i) be the maximum value returned by the Radon
transform operation Ri (ρ, θ) and gives us the maximum
length of Bi .
Rmax (i) = max(Ri (ρ, θ)), i = 1, 2, ....., n
Ti =
1
if Ai /A ≤ 1
[Ai /A] otherwise.
(8)
9) The total count of objects-of-interest in image K was
given by
n
Ti
T =
(9)
i=1
III. E XPERIMENTAL R ESULTS
(1)
Six test images were simulated (Fig.2) with known numbers
of elements having different lengths. These images were
run through the proposed counting algorithm. The simulation
results are presented in Table I.
(2)
COUNTING RESULTS
4) We deﬁne six sets as shown below,
U1 = {Rmax (i)} if 15 ≤ Rmax (i) ≤ 20
8) Let Ai denote the area of component Bi . The number
of elements contained by component Bi was deﬁned as
U2 = {Rmax (i)} if 21 ≤ Rmax (i) ≤ 25
(3)
U3 = {Rmax (i)} if 26 ≤ Rmax (i) ≤ 30
(4)
U4 = {Rmax (i)} if 31 ≤ Rmax (i) ≤ 35
(5)
U5 = {Rmax (i)} if 36 ≤ Rmax (i) ≤ 40
(6)
U6 = {Rmax (i)} if 41 ≤ Rmax (i) ≤ 45
(7)
TABLE I
Image
a
b
c
d
e
f
Actual Count
297
407
561
888
981
1107
Algorithm Result
283
403
572
839
967
1095
Accuracy
95.28
99.01
98.1
94.48
98.57
98.91
IV. C ONCLUSIONS AND F UTURE W ORK
for i = 1, ....n.
5) Let L denote the mode of the values in the set that
has the maximum number of elements among the sets
deﬁned in Eq.2 - Eq.7.
6) The connected component having the minimum area
amongst those having maximum length of L was found,
let it be Bk .
7) Let A be the area of the rectangle that inscribes Bk .
A new Radon Transform-based segmentation and counting
algorithm for elongated objects in urine smear microscopy
images was presented. The accuracy of the algorithm was
tested using six simulated test images. An average counting
accuracy of 98% was obtained.
Current research involves the extension of the methods for
automatic segmentation, identiﬁcation and counting of GNB
(Gram-Negative Bacilli) in urine smear images for detecting
signiﬁcant bacteriuria. The aim is to investigate if there is any
correlation between results obtained from urine culture method
and the density of pathogen distribution in urine smears.
If this venture attains appreciable success, plans will be
made for extending the methods for assessing the antibiotic
sensitivity of pathogens found in urine. The popular KirbyBauer method requires 24 - 48 hours for producing results.
3.
Fig. 2.
Simulated images used to test counting accuracy proposed counting algorithm.
Proposal for a new method will be made to assess the
sensitivity within an hour of patient admission so as to start
the administration of the correct antibiotic as soon as possible.
A PPENDIX A
R ADON T RANSFORM
In mathematics, the Radon transform in two dimensions,
named after the Austrian mathematician Johann Radon, is the
integral transform consisting of the integral of a function over
straight lines [18]. It is widely applicable to tomography, the
creation of an image from the scattering data associated with
cross-sectional scans of an object [19].
approach distance ρ to the origin and orientation θ [17]. This
is repeated for a given set of angles, usually for θ = {0, 1, 2,
3, ....... , 179}. The angle 180o is not included since the result
would be identical to the one obtained for angle 0o [20]. The
radial coordinates returned in ρ are the values along the x-axis,
which is oriented at θ degrees counterclockwise from the xaxis. The origin of both axes is the center pixel of the image
[21].
ACKNOWLEDGMENT
S.U.A would like to thank Dr.Sohanlal T.,MD,DNB, Dr.Vrinda
V. Nair, Dr.Shybin Usman,DNB, Dr.M. R. Baiju, Dr.Jiji C. V.
and Benazeer C. P. for all their help and encouragement.
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Fig. 3. The Radon transform is the projection of the image intensity along
a radial line oriented at a speciﬁc angle [20], [21].
The Radon transform (Fig.3) of a binary object E returns
the distance, E (ρ, θ), travelled through the object, by a
particle following a straight-line trajectory which has a closest
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