Image Denoising for the Detection of Follicle in Polycystic Ovarian Syndrome Images

Authors

  • A. Saravanan Assistant Professor, Division of Computer and Information Science Annamalai University, Tamil Nadu, India
  • S. Sathiamoorthy Assistant Professor, Division of Computer and Information Science Annamalai University, Tamil Nadu, India

DOI:

https://doi.org/10.51983/ajcst-2018.7.2.1868

Keywords:

Polycystic ovarian syndrome, Noise removal, Super-pixel clustering method, Gradient-based approach, Fuzzy C means clustering

Abstract

Polycystic Ovarian Syndrome is an endocrine ailment affecting women of reproductive age. This syndrome is largely found in women whose age is in between 25 and 35. Without knowing the accurate region of a follicle in ovary, the hazard rigorousness of the patient cannot be exposed. Since, super-pixels can be functional on segmentation and image representation, it has turned out to be essential for refining the competence in computer vision systems. Thus, in this paper, a novel image denoising methodology for the detection of a follicle in the PCOS has been suggested by exploring the super-pixel clustering and Fuzzy C means clustering.

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Published

05-08-2018

How to Cite

Saravanan, A., & Sathiamoorthy, S. (2018). Image Denoising for the Detection of Follicle in Polycystic Ovarian Syndrome Images. Asian Journal of Computer Science and Technology, 7(2), 118–122. https://doi.org/10.51983/ajcst-2018.7.2.1868