Research Article
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Year 2020, Volume: 8 Issue: 4, 203 - 208, 31.12.2020

Perihan Gülşah Yılmaz Güzin Özmen

https://doi.org/10.18100/ijamec.803400
Cited By: 3

Abstract

References

  • P. S. Hiremath, R. Tegnoor, “Follicle Detection and Ovarian Classification in Digital Ultrasound Images of Ovaries”, Advancements and Breakthroughs in Ultrasound Imaging, Chapter 7, 2013, 167-197.
  • M. Sheikhha, M. Seyed, G. Nasrin, “Genetics of Polycystics Ovary Syndrome”, Iranian Journal of Reproductive Medicine, 2006, Vol.5,1-5.
  • F. Broekmans, E. Knauff, O. Valkenburg, J. Laven, M. Eijkemans, B. Fauser, “PCOS accoriding to the Rotterdam consensus criteria: change in prevalence among WHO-II anovulation and association with metabolic factors”, BJOG, 2006, 1210-1217.
  • B. Purnama, U.N. Wisesty, K. Adiwijaya, F. Nhita, A. Gayatri, T. Mutiah, “A Classification of Polycystic Ovary Syndrome Based on Follicle Detection of Ultrasound Images”, 3 rd Internetional Conference on Information and Communication Technology (ICoICT), 2015, 396-401.
  • P. Hiremath, J. Tegnoor, Automatic Detection of Follicles in Ultrasound Images of Ovaries using Edge Based Method”, IJCA Special Issue on “Recent Trends in Image Processing and Pattern Recognition”, RTIPPR 2010, 120-124.
  • U.N. Wisesty, T. Mutiah, “Implementasi Gabor Wavelet dan Support Vector Machine pada Deteksi Polycystic Ovary (PCO) Berdasarkan Citra Ultrasonografi”, IND Journal on Computing Vol.1, 2016, 67-82.
  • M. J. Rao, R. K. Kumar, “Follicle Detection in Digital Ultrasound Images using BEMD and Adaptive K-means Clustering Algorithm”, International Journal of Applied Engineering Research Vol. 14, 2019, 397-403.
  • A. A. Nazarudin, N. Zulkarnain, A. Hussain, S. S. Mokri, I. N. A. M. Nordin, “Review on automated follicle identification for polycystic ovarian syndrome”, Bulletin of Electrical Engineering and Informatics, 2020, 588-593.
  • M. J. Lawrence, M. G. Eramian, R. A. Pierson, E. Neufeld, “Computer Assisted Detection of Polycystic Ovary Morphology in Ultrasound Images”, Fourth Canadian Conference on Computer and Robot Vision., 2007.
  • P. S. Hiremath, R. Tegnoor, “Automatic detection of follicles in ultrasound images of ovaries using active contours method”, 2010 IEEE International Conference on Computational Intelligence and Computing Research, 2010, 286-291.
  • V. Kiruthika, M.M. Ramya, “Automatic segmentation of ovarian follicle using K-means clustering”, 2014 Fifth International Conference on Signal and Image Processing, Bangalore, India, 2014, 137-141.
  • R. Sitheswaran, S. Malarkhodi,“An Effective Automated System in Follicle Identification for Polycystic Ovary Syndrome Using Ultrasound Images”, 2014 International Conference on Electronics and Communication System, ICECS 2014.
  • S. Rihana, H. Mousallem, C. Skaf, C. Yaacoub, “Automated algorithm for ovarian cysts detection in ultrasonogram”, 2013 2nd International Conference on Advances in Biomedical Engineering, 2013,219-222.

Follicle Detection for Polycystic Ovary Syndrome by using Image Processing Methods

Year 2020, Volume: 8 Issue: 4, 203 - 208, 31.12.2020

Perihan Gülşah Yılmaz Güzin Özmen

https://doi.org/10.18100/ijamec.803400
Cited By: 3

Abstract

Polycystic ovary syndrome is a hormonal disorder seen in many women. It occurs by the combination of many small and benign cysts in the ovaries. These cysts, called follicles, create a special pattern in the ovaries observed with ultrasound imaging. The number, structure, and size of these follicles provide important information for the diagnosis of ovarian diseases. In this study, two different methods of follicle detection are tested for Polycystic Ovary Syndrome. The first method consists of noise filtering, contrast adjustment, binarization, and morphological processes. For this method, Median Filter, Average Filter, Gaussian Filter, and Wiener Filter were used for noise reduction, and then histogram equalization and adaptive thresholding were tested. For the second method, Gaussian Filter and Wavelet Transform were selected for noise reduction, and k-means clustering and morphological operations were applied to the images. In the segmentation phase performed for both methods, follicles were detected with the Canny Edge Detection algorithm. False Acceptance Rate (FAR) and False Rejection Rate (FRR) were used to evaluate the accuracy of the results. Our results show that the most accurate follicle detection was obtained by using the Wiener Filter and Gaussian Filter.

Keywords

Follicle detection Image processing Polycystic ovary syndrome Ultrasonography images

References

  • P. S. Hiremath, R. Tegnoor, “Follicle Detection and Ovarian Classification in Digital Ultrasound Images of Ovaries”, Advancements and Breakthroughs in Ultrasound Imaging, Chapter 7, 2013, 167-197.
  • M. Sheikhha, M. Seyed, G. Nasrin, “Genetics of Polycystics Ovary Syndrome”, Iranian Journal of Reproductive Medicine, 2006, Vol.5,1-5.
  • F. Broekmans, E. Knauff, O. Valkenburg, J. Laven, M. Eijkemans, B. Fauser, “PCOS accoriding to the Rotterdam consensus criteria: change in prevalence among WHO-II anovulation and association with metabolic factors”, BJOG, 2006, 1210-1217.
  • B. Purnama, U.N. Wisesty, K. Adiwijaya, F. Nhita, A. Gayatri, T. Mutiah, “A Classification of Polycystic Ovary Syndrome Based on Follicle Detection of Ultrasound Images”, 3 rd Internetional Conference on Information and Communication Technology (ICoICT), 2015, 396-401.
  • P. Hiremath, J. Tegnoor, Automatic Detection of Follicles in Ultrasound Images of Ovaries using Edge Based Method”, IJCA Special Issue on “Recent Trends in Image Processing and Pattern Recognition”, RTIPPR 2010, 120-124.
  • U.N. Wisesty, T. Mutiah, “Implementasi Gabor Wavelet dan Support Vector Machine pada Deteksi Polycystic Ovary (PCO) Berdasarkan Citra Ultrasonografi”, IND Journal on Computing Vol.1, 2016, 67-82.
  • M. J. Rao, R. K. Kumar, “Follicle Detection in Digital Ultrasound Images using BEMD and Adaptive K-means Clustering Algorithm”, International Journal of Applied Engineering Research Vol. 14, 2019, 397-403.
  • A. A. Nazarudin, N. Zulkarnain, A. Hussain, S. S. Mokri, I. N. A. M. Nordin, “Review on automated follicle identification for polycystic ovarian syndrome”, Bulletin of Electrical Engineering and Informatics, 2020, 588-593.
  • M. J. Lawrence, M. G. Eramian, R. A. Pierson, E. Neufeld, “Computer Assisted Detection of Polycystic Ovary Morphology in Ultrasound Images”, Fourth Canadian Conference on Computer and Robot Vision., 2007.
  • P. S. Hiremath, R. Tegnoor, “Automatic detection of follicles in ultrasound images of ovaries using active contours method”, 2010 IEEE International Conference on Computational Intelligence and Computing Research, 2010, 286-291.
  • V. Kiruthika, M.M. Ramya, “Automatic segmentation of ovarian follicle using K-means clustering”, 2014 Fifth International Conference on Signal and Image Processing, Bangalore, India, 2014, 137-141.
  • R. Sitheswaran, S. Malarkhodi,“An Effective Automated System in Follicle Identification for Polycystic Ovary Syndrome Using Ultrasound Images”, 2014 International Conference on Electronics and Communication System, ICECS 2014.
  • S. Rihana, H. Mousallem, C. Skaf, C. Yaacoub, “Automated algorithm for ovarian cysts detection in ultrasonogram”, 2013 2nd International Conference on Advances in Biomedical Engineering, 2013,219-222.
There are 13 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Article
Authors

Perihan Gülşah Yılmaz 0000-0001-6749-332X

Güzin Özmen 0000-0003-3007-5807

Publication Date December 31, 2020
Published in Issue Year 2020 Volume: 8 Issue: 4

Cite

APA Yılmaz, P. G., & Özmen, G. (2020). Follicle Detection for Polycystic Ovary Syndrome by using Image Processing Methods. International Journal of Applied Mathematics Electronics and Computers, 8(4), 203-208. https://doi.org/10.18100/ijamec.803400
AMA Yılmaz PG, Özmen G. Follicle Detection for Polycystic Ovary Syndrome by using Image Processing Methods. International Journal of Applied Mathematics Electronics and Computers. December 2020;8(4):203-208. doi:10.18100/ijamec.803400
Chicago Yılmaz, Perihan Gülşah, and Güzin Özmen. “Follicle Detection for Polycystic Ovary Syndrome by Using Image Processing Methods”. International Journal of Applied Mathematics Electronics and Computers 8, no. 4 (December 2020): 203-8. https://doi.org/10.18100/ijamec.803400.
EndNote Yılmaz PG, Özmen G (December 1, 2020) Follicle Detection for Polycystic Ovary Syndrome by using Image Processing Methods. International Journal of Applied Mathematics Electronics and Computers 8 4 203–208.
IEEE P. G. Yılmaz and G. Özmen, “Follicle Detection for Polycystic Ovary Syndrome by using Image Processing Methods”, International Journal of Applied Mathematics Electronics and Computers, vol. 8, no. 4, pp. 203–208, 2020, doi: 10.18100/ijamec.803400.
ISNAD Yılmaz, Perihan Gülşah - Özmen, Güzin. “Follicle Detection for Polycystic Ovary Syndrome by Using Image Processing Methods”. International Journal of Applied Mathematics Electronics and Computers 8/4 (December 2020), 203-208. https://doi.org/10.18100/ijamec.803400.
JAMA Yılmaz PG, Özmen G. Follicle Detection for Polycystic Ovary Syndrome by using Image Processing Methods. International Journal of Applied Mathematics Electronics and Computers. 2020;8:203–208.
MLA Yılmaz, Perihan Gülşah and Güzin Özmen. “Follicle Detection for Polycystic Ovary Syndrome by Using Image Processing Methods”. International Journal of Applied Mathematics Electronics and Computers, vol. 8, no. 4, 2020, pp. 203-8, doi:10.18100/ijamec.803400.
Vancouver Yılmaz PG, Özmen G. Follicle Detection for Polycystic Ovary Syndrome by using Image Processing Methods. International Journal of Applied Mathematics Electronics and Computers. 2020;8(4):203-8.

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