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Optimized Segmentation Technique for Detecting PCOS in Ultrasound Images

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Congress on Intelligent Systems

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 114))

Abstract

PCOS-polycystic ovary syndrome is one of the prominent disorders called endocrine that occurred in the reproductive system of the female lifestyle. Ovulation issues are frequently created by PCOS, which extends to infertility and endometrial cancers. Recently, infertility problem is enrolling major issues for females. According to a survey, 10–15% of married women is affected by infertility and identified by finding the follicles in ovary portions like count, size, the position of the ovary, and hormonal secretions. Automatic detection of follicles is quite a challenging task in predicting polycystic ovary (PCO). It happens to lead a inaccurate detection because of the more noise and low contrast of ultrasound images. To overcome this trouble, an optimized segmentation algorithm has been proposed along with suitable preprocessing techniques, respectively, morphological operations and filtering. The proposed segmentation techniques fix the accurate boundary box for selecting the area to detect follicles in the ovary images. The algorithm has been tested with 50 images of ovaries in different types like normal cyst, ovarian cyst, and PCOS and detecting the follicle in the ovaries for addressing the PCOS accurately.

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References

  1. Padmapriya B, Kesavamurthy T (2016) Detecting of follicles in poly cystic ovarian syndrome in ultrasound images using morophological operations. J Med Imaging Health Inf 6:240–243

    Article  Google Scholar 

  2. Mehrotra P, Chakraborty C, Ghoshdastidar B (2011) Automated ovarian follicle recognition for polycystic ovary syndrome. In: Proceeding of international conference on image information processing (ICIIP 2011). IEEE, 978-1-61284-861-7

    Google Scholar 

  3. Prasanna Kumar H, Srinivasan S (2014) Despeckling of polycystic ovary ultrasound images by improved total variation method. Int J Eng Technol (UET) 6(4):0975–4024

    Google Scholar 

  4. Krivanek A, Sonka M (1998) Ovarian ultrasound image analysis; follicle segmentation. IEEE Trans Med Imaging 17(6):0278–0062

    Google Scholar 

  5. Cigale B, Zazula D (2014) Segmentation of ovarian ultrasound images using cellular neural networks. Int J Patt Recogn Artif Intell 18:563–581

    Article  Google Scholar 

  6. Usha BS, Sandyas S (2013) Measurement of ovarian size and shape parameters. In: Annual IEEE Indian conference (INDICON), 978-4799-2275-8

    Google Scholar 

  7. Sathiya S, Ramya MM (2019) Automatic texture and intensity based ovarian classification. J Med Eng Technol 0309–1902 (Print) 1464–522X

    Google Scholar 

  8. Li H, Fang J, Liu S, Liang X, Yang X, Mai Z, Van MT, Wang T, Chen Z, Ni D (2020) CR-Unet: a composite network for ovary and follicle segment in ultrasound images. J IEEE 24(4):974–983

    Google Scholar 

  9. Narra RT, Singhal N, Narayan NS, Ramaraju GA (2018) Automated ovarian volume quantification in transvaginal ultrasound. In: IEEE 15th international symposium on biomedical images (ISBI), pp 1945–8452

    Google Scholar 

  10. Parekh AM, Shah NB (2017) Classification of ovarian cyst using soft computing techniques. IEEE. https://doi.org/10.1109/ICCCNT.2017.8203965.2017

    Article  Google Scholar 

  11. Hiremath PS, Tegnoor JR (2010) Automatic detection of follicles in ultrasound images of ovaries using edge based method. Int J Comput Appl (Special issue on Recent Trends Image Processing Pattern Recogn), 15–16

    Google Scholar 

  12. Marques S, Carvalho C, Peixoto C, Pignatelli D, Beires J, Silva J, Campilho A (2019) Segmentation of gynaecological ultrasound images using different U-Net based approaches. In: IEEE international ultrasonic’s symposium (IUS), Glasgow, Scotland, 978–1–7281–4596–9/19

    Google Scholar 

  13. Lawrence MJ, Eramian MG, Pierson RA, Neufeld E (2007) Computer assisted detection of polycystic ovary morphology in ultrasound images. In: Fourth Canadian conference on computer and robot vision (CRV’07). IEEE, 0-7695-2786-8

    Google Scholar 

  14. Purnamal B, Wisesti UN, Nhita F, Gayatri A, Mutiah T (2015) A classification of polycystic ovary syndrome based on follicle detection of ultrasound images. In: 3rd international conference on information and communication technology (ICoICT). IEEE. https://doi.org/10.1109/ICoICT.2015.7231458

  15. Deshpandei SS, Wakankar A (2014) Automated detection of polycystic ovarian syndrome using follicle recognition. In: International conference on advanced communication control and computing technologies (ICACCCT) IEEE. 978-1-4799-3914-5/14

    Google Scholar 

  16. Prasanna Kumar H, Shrinivasan S (2012) Perfonnance analysis of filters for speckle reduction in medical polycystic ovary ultrasound images. In: 3rd international conference on computing communication and networking technologies (ICCCNT). IEEE 20180, 13252184

    Google Scholar 

  17. Liu J, Chen H (2016) Automated detection of follicle in ultrasound images of cattle ovarian using MLC method. In: IEEE international conference on systems, man and cybemetics, 978-1-5090-1897-0/16

    Google Scholar 

  18. Sarty GE, Liang W, Sonka M, Pierson RA (1998) Semiautomated segmentation of pvarian follicular ultrasound images using a knowledge-based algorithm. Ultrasound Med Biol 24:27–42

    Article  Google Scholar 

  19. Wanderley DS, Carvalho CB, Domingues A, Peixoto C, Pignatelli D, Beires J, Silva J, Campilho A (2019) End-to-end ovarian structures segmentation. Springer Nature, Switzerland, AG, 978-3-030-13468-6

    Google Scholar 

  20. Gopalakrishna C, Lyapparaja M (2019) Detection of polycystic ovary syndrome from ultrasound images using SIFT descriptors. Bonfring Int Soft Comput 9(2). ISSN 2277-5099

    Google Scholar 

  21. Sudha S, Suresh GR, Sukanesh R (2009) Speckle noise reduction in ultrasound images by wavelet thresholding based on weighted variance. Int J Comput Theory Eng 1(1):1793–8201

    Google Scholar 

  22. Mehrotra P, Chakraborty C, Ghoshdastidar B, Ghoshdastidar S, Ghoshdastidar K (2011) Automated ovarian follicle recognition for polycystic ovary syndrome. In: 2011 IEEE international conference on image information processing (ICIIP 2011), 978-1-61284-861-7/11

    Google Scholar 

  23. Gonzalez RC, Woods RC (2009) Digital image processing, 3rd edn. Pearson Education, New Delhi

    Google Scholar 

  24. Mukhopadhyay S, Chanda B (2000) A multiscale approach to local contrast enhancement. Sig Process 80:685–697

    Article  Google Scholar 

  25. Mandal A, Saha D, Sarkar M (2021) Follicle segmentation using K-means clustering from ultrasound image of ovary. In: Proceedings of international conference on frontiers in computing and systems. Advances in intelligent systems and computing, vol 1255. Springer Nature, Singapore Pte Ltd

    Google Scholar 

  26. Katiyar SK, Arun PV (2014) Comparative analysis of common edge detection techniques in context of object extraction. IEEE TGRS 50(11b)

    Google Scholar 

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Conflicts of Interest

N. Priya and S. Jeevitha declare that they have no conflict of interest. All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation.

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Authors and Affiliations

  1. Department of Computer Science, Shrimathi Devkunvar Nanalal Bhatt Vaishnav College for Women, University of Madras, Chennai, India

    S. Jeevitha & N. Priya

Authors
  1. S. Jeevitha
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  2. N. Priya
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Editor information

Editors and Affiliations

  1. Department of Computer Science & Engineering and Information Technology, Jaypee Institute of Information Technology, Noida, India

    Mukesh Saraswat

  2. Department of Computer Science and Engineering, Rajasthan Technical University, Kota, India

    Harish Sharma

  3. Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, India

    K. Balachandran

  4. Korea University, Seoul, Korea (Republic of)

    Joong Hoon Kim

  5. South Asian University, New Delhi, India

    Jagdish Chand Bansal

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Jeevitha, S., Priya, N. (2022). Optimized Segmentation Technique for Detecting PCOS in Ultrasound Images. In: Saraswat, M., Sharma, H., Balachandran, K., Kim, J.H., Bansal, J.C. (eds) Congress on Intelligent Systems. Lecture Notes on Data Engineering and Communications Technologies, vol 114. Springer, Singapore. https://doi.org/10.1007/978-981-16-9416-5_56

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