A Review on Computer Assisted Follicle Detection Techniques and Polycystic Ovarian Syndrome (PCOS) Diagnostic Systems

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2015 by IJCTT Journal
Volume-28 Number-1
Year of Publication : 2015
Authors : I. O. Rabiu, A. D. Usman, A. M. S. Tekanyi
  10.14445/22312803/IJCTT-V28P109

MLA

I. O. Rabiu, A. D. Usman, A. M. S. Tekanyi "A Review on Computer Assisted Follicle Detection Techniques and Polycystic Ovarian Syndrome (PCOS) Diagnostic Systems". International Journal of Computer Trends and Technology (IJCTT) V28(1):41-45, October 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Polycystic Ovarian Syndrome (PCOS) caused infertility in women if not diagnosed and treated early. Transvaginal ultrasound machine is a non-invasive method of imaging human ovary with the aim of revealing salient features necessary for PCOS diagnosis. Numbers of follicles and their sizes are the main features that characterize ovarian images. Hence, PCOS is diagnosed by counting the numbers of follicles and measuring their sizes manually. This process is laborious, prone to error and time consuming. This paper surveys various computer assisted techniques for the detection of follicles and PCOS diagnoses in the ultrasound images of the ovary. Performances of some of the previous works are identified and compared. Finally, future research directions to improve on some of the observed limitations are provided.

References
[1] York, G. and Kim, Y, “Ultrasound Processing and Computing: Review and Future Directions”. Annual Review of Biomedical Engineering, 1(1), 559-581, 1999.
[2] Tegnoor, J. R, “Automated Ovarian Classification in Digital Ultrasound Images Using SVM”. International Journal of Engineering Research & Technology, 1(6), 1-17, 2012.
[3] Fauser, B. C., Tarlatzis, B. C., Rebar, R. W., Legro, R. S., Balen, A. H., Lobo, R., … and Barnhart, K, “Consensus on Women‟s Health Aspects of Polycystic Ovary Syndrome (PCOS)”. Human Reproduction, 27(1), 14-24, 2012.
[4] Catteau-Jonard, S. C., Bancquart, J., Poncelet, E., Lefebvre- Maunoury, C., Robin, G. and Dewailly, D, “Polycystic Ovaries at Ultrasound: Normal Variant or Silent Polycystic Ovary Syndrome”. Ultrasound Obstet Gynecol, 40(2), 223– 229, 2012.
[5] Vause, T. D. R., Ottawa O. N., Cheung, A. P. and Vancouver, B. C, “Ovulation Induction in Polycystic Ovary Syndrome”. Journal of Obstet Gynaecol Can, 32(5), 495–502, 2010.
[6] Mehrotra, P., Chakraborty, C., Ghoshdastidar, B., Ghoshdastidar, S., and Ghoshdastidar, K, “Automated Ovarian Follicle Recognition for Polycystic Ovary Syndrome”. International Conference on Image Information Processing (ICIIP), 1-4, 2011.
[7] Potocnik, B., Cigale, B. and Zazula, D, “The XUltra Project – Automated Analysis Ovarian Ultrasound Images”. In Computer-Based Medical Systems, (CBMS), Proceedings of the 15th IEEE Symposium, 262-267, 2002.
[8] Potocnik, B. and Zazula, D, “Automated Ovarian Follicle Segmentation Using Region Growing”. First Int'l Workshop on Image and Signal Processing and Analysis, 157-162, 2000.
[9] Potocnik, B., Zazula, D. and Korze, D, “Automated Computer-Assisted Detection of Follicles in Ultrasound Images of Ovary”. Journal of Medical Systems, 21(6), 445- 457, 1997.
[10] Hiremath, P. S. and Tegnoor, J. R, “Automatic Detection of Follicles in Ultrasound Images of Ovaries Using Active Contours Method”. In proceedings of IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 28-29, 2010.
[11] Hiremath, P.S. and Tegnoor, J. R, “Automatic Detection of Follicles in Ultrasound Images of Ovaries Using Edge Based Method”. International Journal of Computer Application: Special Issue on Recent Trends in Image Processing and Pattern Recognition, 8(3), 120-125, 2010b.
[12] Hiremath, P.S. and Tegnoor, J. R, “Automated Ovarian Classification in Digital Ultrasound Images”. International Journal of Biomedical Engineering and Technology, 11(1), 46-65, 2013.
[13] Krivanek, A. and Sonka, M, “Ovarian Ultrasound Image Analysis: Follicle Segmentation”. Medical Imaging, IEEE Transactions on, 17(6), 935-955, 1998.
[14] Chen, T., Zhang, W., Good, S., Zhou, K. S., and Comaniciu, D, “Automatic Follicle Quantification from 3D Ultrasound Data Using Global/Local Context with Database Guided Segmentation”. In Computer Vision, IEEE 12th International Conference, 795-802, 2009.
[15] Deng, Y., Wanga, Y., and Shenb, Y, “An Automated Diagnostic System of Polycystic Ovary Syndrome Based on Object Growing”. Artificial Intelligence in Medicine, 51, 199–209, 2011.
[16] Kumar, H. P. and Srinivasan, S, “Despeckling of Polycystic Ovary Ultrasound Images by Improved Total Variation Method”. International Journal of Engineering and Technology (IJET), 6(4), 1877-1884, 2014a.
[17] Kumar, H. P. and Srinivasan, S, “Segmentation of Polycystic Ovary in Ultrasound Images”. 2nd International Conference on Current Trends in Engineering and Technology, ICCTET, 237-240, 2014b.
[18] Lehtinen, J-C., Forsstriim, J., Koskinen, P., Penttila, T-A., Jarvi, T., and Anttila, L, “Visualization of Clinical Data with Neural Networks, Case Study: Polycystic Ovary Syndrome”. International Journal of Medical Informatics, 44, 145-155, 1997.
[19] Lawrence, M. J., Eramian, M. G., Pierson, R. A. and Neufeld, E, “Computer Assisted Detection of Polycystic Ovary Morphology in Ultrasound Images”. Fourth IEEE Canadian Conference on Computer and Robot Vision (CRV), 7, 105- 112, 2007.
[20] Viher, B., Dobnikar, A. and Zazula, A, “Cellular Automata and Follicle Recognition Problem and Possibilities of Using Cellular Automata for Image Recognition Purposes”. International Journal of Medical Informatics, 49, 231–241, 1998.
[21] Bian, N., Eramian, M. G., and Pierson, R. A, “Evaluation of Texture Features for Analysis of Ovarian Follicular development”. Med Image Comput Comput Assist Interv, 1- 11, 2011.
[22] Ashika, R, “Ovarian Follicle Detection for Polycystic Ovary Syndrome using Fuzzy C- Means Clustering”. International Journal of Computer Trends and Technology, 4(7), 2146- 2149, 2013.
[23] Kiruthika, V. and Ramya, M. M, “Automatic Segmentation of Ovarian Follicle Using K-Means Clustering”. Fifth International Conference on Signals and Image Processing, 137-141, 2014.

Keywords
Polycystic Ovarian Syndrome, Follicle Detection, Ultrasound Machine, Diagnostic System, Ovary, Infertility.