Automated Parasite’s Detection in Microscopic Images of Stools Using Distance Regularized Level Set Evolution Initialized with Hough Transform
International Journal of Biomedical Engineering and Clinical Science
Volume 5, Issue 3, September 2019, Pages: 45-58
Received: Aug. 20, 2019;
Accepted: Sep. 16, 2019;
Published: Oct. 23, 2019
Views 471 Downloads 124
Oscar Takam Nkamgang, Research Unity of Condensed Matter Electronics and Signal Processing (URMACETS), Department of Physics, Faculty of Science, University of Dschang, Bandjoun, Cameroon; Research Unity of Automatic and Applied Informatics (URAIA), University Institute of Technology Fotso-Victor, University of Dschang, Bandjoun, Cameroon
Daniel Tchiotsop, Research Unity of Automatic and Applied Informatics (URAIA), University Institute of Technology Fotso-Victor, University of Dschang, Bandjoun, Cameroon
Beaudelaire Saha Tchinda, Research Unity of Automatic and Applied Informatics (URAIA), University Institute of Technology Fotso-Victor, University of Dschang, Bandjoun, Cameroon
Hilaire Bertand Fotsin, Research Unity of Condensed Matter Electronics and Signal Processing (URMACETS), Department of Physics, Faculty of Science, University of Dschang, Bandjoun, Cameroon
Background and purpose: The analysis of biomedical microscopic images is carried out manually in medical laboratories. The manual analysis of clinical images lets to both repetitive tasks and management of huge amounts of data. This is tedious and times consuming for laboratory technicians. Inevitably, it is also prone to human errors. Our objective in this work is to contribute to the automation of the analysis of microscopic images of stools using Distance Regularized Level Set Evolution automatically initialized by Hough transform. Method: We firstly converted the microscopic images to edge maps using canny algorithm. Next, we located the parasite through circular Hough transform and draw circles around them. Those circles stand as initial contours of DRLSE. The contours evolve until they fit the boundaries of the parasites. The final extraction is performed using a complementary method based on the signed distance character of the level set function. Results: The Distance Regularized Level Set Evolution has been automatically initialized. We applied our method to the detection of intestinal parasites in microscopic images. Experimental results show accurate, efficient and less time consuming of our scheme compared to others recently proposed in the literature. Conclusion: This is a notable contribution to the automation of stools examination in the medical laboratories. In forthcoming works, we plan to include this segmentation process in an expert system of parasitic diseases diagnosis.
Oscar Takam Nkamgang,
Beaudelaire Saha Tchinda,
Hilaire Bertand Fotsin,
Automated Parasite’s Detection in Microscopic Images of Stools Using Distance Regularized Level Set Evolution Initialized with Hough Transform, International Journal of Biomedical Engineering and Clinical Science.
Vol. 5, No. 3,
2019, pp. 45-58.
Levy L. E., handbook of basic techniques for the medical laboratory, WHO (1999). Vol 1 (912p) & Vol 2 (1000p).
Saha T. B., Tchiotsop D., Tchinda R., Kenne G., Automated Extraction of the Intestinal Parasite in the Microscopic Images Using Active Contours and the Hough Transform, Current Medical Imaging Reviews, Vol. 11, No. 4, pp. 233-246, 2015.
Tchiotsop D., Saha T. B., Tchinda R., Kenne G., Edge detection of intestinal parasites in stool microscopic images using multi-scale wavelet transform, Springer-Verlag London SIViP, vol 9 (Suppl 1), pp. 121–134, 2015. DOI 10.1007/s11760-014-0716-6.
Lecellier F., Jehan-Besson S., Fadili F., Statistical region-based active contours for segmentation: An overview, IRBM (2014), http://dx.doi.org/10.1016/j.irbm.2013.12.002, In press.
Fowlkes C., Martin D. and Malik J., Learning to detect natural image boundaries using local brightness, color and texture cues., IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 1, 2004.
Chickanosky V. and Mirchandani G., Wreath products for edge detection, in Proceedings. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), USA, pp. 2953-2956, 1998.
Kamgar-Parsi B. and Rosenfeld A., Optimally isotropic Laplacian operator., Image Processing, IEEE Transactions on magnetics, vol. 8, no. 10, pp. 1467–1472, 1999.
Ying-Don Q., Cheng-Song C., San-Ben C., and Jin-Quan L., A fast sub pixel edge detection method using Sobel-Zernike moments operator., Image and Vision Computing, vol. 23, no. 1, pp. 11–17, 2005.
J. Canny., A computational approach to edge detection, IEEE Trans. Pattern Anal. and Mach. Intell., vol. 8, pp. 679-714, Nov. 1986.
Juneja M., Sandhu P. S., Performance evaluation of edge detection techniques for images in spatial domain., Internationnal Journal of Computer Theory and Engineering 2009; December 2009, pp. 614-621.
Mokate U. B. and Kasturi R., An Algorithm for Recognition of Circles in Graphics., Computer Engineering Technical Report TR-88-061, the Pennsylvania State University, University Park, Pennsylvania 16802, 1988.
Amir I., Algorithm for Finding the Center of Circular Fiducials., Computer Vision, Graphics and Image Processing, 1990. pp 398–406.
Hough P. V. C., Methods and Means for Recognizing Complex Patterns., U.S. Patent vol. 3, 1962 pp 654-669.
Duda R. O. and Hart P. E., Use of Hough transform to detect lines and curves in pictures, ACM Community Management, vol. 15, pp 11–15, 1972.
Ballard D. H., Generalizing the Hough Transform to detect arbitrary shapes, Pattern Recognition vol. 13, 1981, pp 111–122
Davies E. R., A modified Hough scheme for general circle location, Pattern Recognition Lett. Vol. 7, 1988, pp 37–43.
Ho C. T. and Chen L. H., A fast ellipse/circle detector using geometric symmetry, Pattern Recognition vol. 28, 1995, pp 117–124.
Ho C. T. and Chen L. H., A high-speed algorithm for elliptical object detection, IEEE Transactions on Image Processing, vol. 5 No. 3, 1996, pp 547–550.
Ioannou D., Huda W. and Laine A. F., Circle recognition through a 2D Hough transform and radius histogramming, Image Vision Computer, vol. 17, 1999, pp 15–26.
Olson C. F., Constrained Hough transforms for curve detection, Computer Vision Image Understanding Vol. 73, No. 3, March 1999, pp 329–345.
Osher S. and Sethian J. A., Fronts propagating with curvature dependent speed: Algorithms based on Hamilton-jacobi formulations. Journal of Computational Physics, vol. 79, 1988, pp 12-49.
Sethian J., Level Set Methods and Fast Marching Methods., Cambridge University Press, 1999, book.
Osher S. and Fedkiw R., Level Set Methods and Dynamic Implicit Surfaces., Springer-Verlag New York, Applied Mathematical Sciences, 153, 2002, book
Li C, Xu C, Gui C, Fox M. D., Distance regularized level set evolution and its application to image segmentation. IEEE Transaction on Image Processing, 2010, Vol 19, No: 12, pp 3243-3254.
Yang F, Qin W, Xie Y, Wen T and Gu J., A shape-optimized framework for kidney segmentation in ultrasound images using NLTV denoising and DRLSE., Biomedical Engineering Online (2012) DOI: 10.1186/1475-925X-11-82
Sussman M., Smereka P. and Osher S., A level set approach for computing solutions to incompressible two-phase flow, Journal of Computer Physics, vol. 114, no. 1, Sep. 1994, pp 146–159.
Pochet C. (1), «Parasites des aliments», available on line on Internet site: http://bioimage.free.fr/par_image/parasites_aliments.htm. (Date of access: 08.11.2016, 12h30).
Pochet C. (3), « Plan d’étude des formes végétatives et kystiques des protozoaires », available on line on Internet site: http://bioimage.free.fr/par_image/etudeproto.pdf. (Date of access: 08.11.2016, 13h40: 08.11.2016, 15h50).
Li C., Kao C., Gore J. C., and Ding Z., Minimization of region-scalable fitting energy for image segmentation, IEEE Transaction on Image Process., vol. 17, no. 10, Oct. 2008, pp. 1940–949.
Nkamgang OT, Tchiotsop D, Tchinda BS, Fotsin HB, A neuro-fuzzy system for automated detection and classification of human intestinal parasites, Informatics in Medicine Unlocked (2018), doi: https://doi.org/10.1016/j.imu.2018.10.007.
Nkamgang OT, Tchiotsop D, Fotsin HB, Talla PK, Louis Dorr Valérie, Wolf D, Automating the clinical stools exam using image processing integrated in an expert system, Informatics in Medicine Unlocked (2019), doi: https://doi.org/10.1016/j.imu.2019.100165.