Segmentation and counting of elongated objects in urine smear microscopy images

Uploaded on

Proc. of SPINCOM 2013

Proc. of SPINCOM 2013

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads


Total Views
On Slideshare
From Embeds
Number of Embeds



Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

    No notes for slide


  • 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: 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. R EFERENCES 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 [1] Randhir Puri and Jaideep Malhotra, “Recurrent Urinary Tract Infection in Women,” South Asian Federation of Obstetrics and Gynecology, JanApril 2009. [2] Ramanath K. V. and Shafiya S. B., “Prescription pattern of antibiotic usage for urinary tract infection treated in a rural tertiary care hospital,” Indian Journal of Pharmacy Practice, Vol. 4, Issue 2, April-June 2011. [3] Najar M. S., Saldanha C. L. and Banday K. A., “Approach to urinary tract infections,” Indian Journal of Nephrology, Vol. 19, Issue 4, pp. 129-139, 2009. [4] Shaon Ray Chaudhuri, Ashoke Ranjan Thakur, Poulomi Nandy and Santanu Samanta, “Urinary Tract Infection-A Survey of Local Population,” American Journal of Infectious Diseases, pp. 117-123, 2008. [5] Arul Prakasam K.C., K. G. Dileesh Kumar and M. Vijayan, “Cross Sectional Study on Distribution of Urinary Tract Infection and Their Antibiotic Utilisation Pattern In Kerala,” International Journal of PharmTech Research, Vol.4, No.3, pp. 1309-1316, July-September 2012. [6] J. Jameson, “Harrison’s Principles of Internal Medicine,” Eighteenth Edition, Tata Mcgraw-Hill Education, 2011. [7] C. T. N. Suzuki, J. F. Gomes, A. X. Falacˇ o, J. P. Papa and S. Hoshinoa Shimizu, “Automatic Segmentation and Classification of Human Intestinal Parasites from Microscopy Images,” Trans. on Biomed. Engg., 2011. [8] S. Mohapatra, D. Patra and K. Kumar, “Blood Microscopic Image Segmentation using Rough Sets,” Proc. of ICIIP, 2011.
  • 4. [9] Q. X. Wu, X. Huang, J. Cai, Y. Wu and M. Lin, “Segmentation of Leukocytes in Blood Smeare Images using Color Processing Mechanism Inspired by the Visual System,” Proc. of BMEI, 2009. [10] M. Park, J. S. Jin, Y. Peng, P. Summons, D. Yu, Y. Cui, S. Luo, F. Wang, L. Santos and M. Xu, “Automatic cell segmentation in microscopic color images using ellipse fitting and watershed,” Proc. of IICME, 2010. [11] S. Kareem, R. C. S. Morling and I. Kale, “A Novel Method to Count the Red Blood Cells in Thin Blood Films,” Proc. of ISCAS, 2011. [12] S. Kothari, Q. Chaudry and M. D. Wang, “Automated Cell Countng and Cluster Segmentation using Concavity Detection and Ellipse Fitting Techniques,” Proc. of ISBI, 2009. [13] G. M. Faustino, M. Gattass, S. Rehen and C. J. P. de Lucena, “Automatic embryonic stem cells detection and counting method in fluorescence microscopy images,” Proc. of ISBI, 2009. [14] A. Benzinuo, Y. Hojeij, Y. Sibiril and A. C. Roudot, “Haematopoitic cell clusters quantification using image analysis,” Biomedical Signal Processing and Control, 2006. [15] M. Habibzadeh, A. Krzyzak, T. Fevens and A. Sadr, “Counting of RBCs and WBCs in noisy normal blood smear microscopic images,” Proc. of SPIE, Vol. 7963, 2011. [16] Sio S. W. S., Sun W., Kumar S., Bin W. Z., Tan S. S., Ong S. H. and Kikuchi H., Oshima Y. and Tan K. S. W. “MalariaCount: An image analysis-based program for the accurate determination of parasitemia,” Journal of Microbiological Methods, 2007. [17] Quan Xue, N. S. Jones, N.S. and M. C. Leake, “A general approach for segmenting elongated and stubby biological objects: Extending a chord length transform with the Radon transform,” Proc. of ISBI : From Nano to Macro, 2010. [18] S. R. Deans, “The Radon Transform and Some of Its Applications,” John Wiley Sons, 1983. [19] Anil K. Jain, “Fundamentals of Digital Image Processing,” Prentice Hall, 1989. [20] Carsten Høilund, “The Radon Transform,” Aalborg University, November 12, 2007. [21] Radon Transform - MATLAB radon - MathWorks India. Web. Available: