Segmentation and Counting of Elongated Objects inUrine Smear Microscopy Images

Shaeez Usman AbdullaDept. of Electronics and Communication Engineering

College of Engineering, TrivandrumKerala, India, PIN-695016

(E-mail: shaeez@gmail.com)

Abstract—This paper describes a new method for segmentingand counting elongated objects, like E. coli, in urine smearmicroscopy images. This work was undertaken as a part of devel-oping an automated system for detecting significant bacteriuriabased on the distribution density of Gram-Negative Bacilli inurine smears.

The smear image was first subjected to intensity threshold-ing. The number of members represented by each connected-component was obtained using a Radon Transform-based di-mensional analysis. The proposed method was implemented onsix test images and obtained an 98% average accuracy.

Index Terms—Medical Microscopy Image Analysis, UrineSmear, E Coli, Gram-Negative Bacilli, Radon Transform.

I. INTRODUCTION

URINARY TRACT INFECTION (UTI) is one the mostcommonly occuring infection in humans, and also the

most common cause of both community-acquired and noso-comial infections for patients admitted to hospitals [1]-[3].Since 90% of infected cases have bacterial etiology and requireantimicrobial therapy, physicians rely on several microbiolog-ical laboratory techniques and parameters to determine thespecies and severity of bacteriuria1 [4]. The presence of 105 ormore colony forming units (CFU) per ml of urine is generallyconsidered as significant bacteriuria [3], [5], [6].

The most popular method is the detection of bacteria in aurine culture. It is considered as the diagnostic “gold stan-dard” for UTI. Unfortunately, culture results do not becomeavailable until 24 hours after the patient’s presentation, andidentifying specific organism(s) can require an additional 24hours [5], [6].

Spotting of pathogens in urine smears is taken to be the firstclue that infection is present. Extremely wide variations in thepresentation of microscopic images makes their segmentationa very tricky affair. Most popular segmentation techniques inthis area depend on ellipse matching, color and brightnessinformation etc., to differentiate between different parts of theimage [7]-[11].

Cluster formation makes it hard to differentiate betweenmembers. Kothari et al. utilized concavity detection and ellipsefitting for the automated cell counting and cluster segmenta-tion in images of tissue samples [12]. Automatic embryonicstem cells detection and counting in flourescence microscopy

1Bacteriuria is defined as the presence of bacteria in urine not due tocontamination 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 sizeestimation for cluster quantification while analysing bloodsmear images [14], [15]. Sio et al. addressed the problem ofparasitemia estimation in blood smears using edge detectionand 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 imageinto a 3D volume. The Radon Transform is then applied tohelp segment this volume [17].

In this paper, a new computer-aided approach is proposedfor automated segmentation and counting of elongated bio-logical objects in urine smear images. The accuracy of theproposed algorithm is tested using six simulated images.

The methodology is described in Section II. Section IIIpresents the experimental results and Section IV states theconclusions and future work.

II. PROPOSED ALGORITHM

A. Cluster Analysis

Fig.1(a) shows a microscopy image of a typical urinesmear obtained after gram staining, showing the presence ofGram-Negative Bacilli. Upon examining various dimensionalparameters of the objects, it was noted that the maximumlength of a large portion of bacilli in an image belonged to therange of 15 - 45 pixels. Fig.1(b) shows the maximum lengthanalysis graph of each connected element in Fig.1(a).

Each connected component in the image was classifiedinto different groups based on their maximum length (Onlylengths falling in the range of 15-45 pixels were taken intoconsideration). The group having largest number of memberswas taken and the most repeatedly occuring length was noted,and was taken as standard length. Minimum of the areaindividually covered by members having standard length wastaken as standard area. The counting algorithm was designedbased on the following assumptions:

1) All the connected components having an area less thanthe standard area is assumed to be consisting of a singlemember.

2) All the connected components having an area greaterthan the standard area is assumed to be clusters madeof members having standard area.

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 at0.7 luminance threshold, inverted, and noise filtered toget the binary image Kb.

2) Let the image Kb contain n connected components andlet Bi be the ith connected component. Each of thecomponent was subjected to Radon transform analysis.Ri(ρ, θ) represents the values returned by Radon trans-form operation on component Bi (See Appendix A).

3) Rmax(i) be the maximum value returned by the Radontransform operation Ri(ρ, θ) and gives us the maximumlength of Bi.

Rmax(i) = max(Ri(ρ, θ)), i = 1, 2, ....., n (1)

4) We define six sets as shown below,

U1 = {Rmax(i)} if 15 ≤ Rmax(i) ≤ 20 (2)

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)

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 setsdefined in Eq.2 - Eq.7.

6) The connected component having the minimum areaamongst those having maximum length of L was found,let it be Bk.

7) Let A be the area of the rectangle that inscribes Bk.

8) Let Ai denote the area of component Bi. The numberof elements contained by component Bi was defined as

Ti =

{1 if Ai/A ≤ 1[Ai/A] otherwise. (8)

9) The total count of objects-of-interest in image K wasgiven by

T =

n∑i=1

Ti (9)

III. EXPERIMENTAL RESULTS

Six test images were simulated (Fig.2) with known numbersof elements having different lengths. These images wererun through the proposed counting algorithm. The simulationresults are presented in Table I.

TABLE ICOUNTING RESULTS

Image Actual Count Algorithm Result Accuracya 297 283 95.28b 407 403 99.01c 561 572 98.1d 888 839 94.48e 981 967 98.57f 1107 1095 98.91

IV. CONCLUSIONS AND FUTURE WORK

A new Radon Transform-based segmentation and countingalgorithm for elongated objects in urine smear microscopyimages was presented. The accuracy of the algorithm wastested using six simulated test images. An average countingaccuracy of 98% was obtained.

Current research involves the extension of the methods forautomatic segmentation, identification and counting of GNB(Gram-Negative Bacilli) in urine smear images for detectingsignificant bacteriuria. The aim is to investigate if there is anycorrelation between results obtained from urine culture methodand the density of pathogen distribution in urine smears.

If this venture attains appreciable success, plans will bemade for extending the methods for assessing the antibioticsensitivity of pathogens found in urine. The popular Kirby-Bauer method requires 24 - 48 hours for producing results.

Fig. 2. Simulated images used to test counting accuracy proposed counting algorithm.

Proposal for a new method will be made to assess thesensitivity within an hour of patient admission so as to startthe administration of the correct antibiotic as soon as possible.

APPENDIX ARADON TRANSFORM

In mathematics, the Radon transform in two dimensions,named after the Austrian mathematician Johann Radon, is theintegral transform consisting of the integral of a function overstraight lines [18]. It is widely applicable to tomography, thecreation of an image from the scattering data associated withcross-sectional scans of an object [19].

Fig. 3. The Radon transform is the projection of the image intensity alonga radial line oriented at a specific angle [20], [21].

The Radon transform (Fig.3) of a binary object E returnsthe distance, E

′(ρ, θ), travelled through the object, by a

particle following a straight-line trajectory which has a closest

approach distance ρ to the origin and orientation θ [17]. Thisis repeated for a given set of angles, usually for θ = {0, 1, 2,3, ....... , 179}. The angle 180o is not included since the resultwould be identical to the one obtained for angle 0o [20]. Theradial coordinates returned in ρ are the values along the x-axis,which is oriented at θ degrees counterclockwise from the x-axis. 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.VrindaV. 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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