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Peer-Reviewed Academic JournalInternational Journal of Applied Methods in Electronics and Computers
ISSN: 3023-4409DOI Prefix: 10.58190/ijamec
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Research ArticlesverifiedPeer-ReviewedOpen Access
Pages: 203-208

Follicle Detection for Polycystic Ovary Syndrome by using Image Processing Methods

Perihan Gülşah YILMAZ fingerprint•
Güzin ÖZMEN fingerprint
Publication DateDecember 31, 2020
Volume / IssueVol. 8, No. 4 (pp. 203-208)
subject

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 detectionImage processingPolycystic ovary syndromeUltrasonography images

Author Affiliations

  • Perihan Gülşah YILMAZ — SELÇUK ÜNİVERSİTESİ, FEN BİLİMLERİ ENSTİTÜSÜ, ELEKTRİK-ELEKTRONİK MÜHENDİSLİĞİ (YL) (TEZLİ)fingerprint0000-0001-6749-332X
  • Güzin ÖZMEN — SELÇUK ÜNİVERSİTESİ, TEKNOLOJİ FAKÜLTESİ, BİYOMEDİKAL MÜHENDİSLİĞİ BÖLÜMÜfingerprint0000-0003-3007-5807
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References (13)

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How to Cite This Article

P. G. Y., G. Ö. (2020). Follicle Detection for Polycystic Ovary Syndrome by using Image Processing Methods. International Journal of Applied Methods in Electronics and Computers, 203-208. https://doi.org/10.18100/ijamec.803400