A Detailed Research on Detection of Polycystic Ovary Syndrome from Ultrasound Images of Ovaries
C. Gopalakrishnan1, M. Iyapparaja2
1C. Gopalakrishnan, Research Scholar, SITE, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
2M. Iyapparaja, Associate Professor, SITE, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
Manuscript received on 11 October 2019 | Revised Manuscript received on 20 October 2019 | Manuscript Published on 02 November 2019 | PP: 467-472 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B10720982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1072.0982S1119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: A common endocrine disorder named polycystic ovarian syndrome (PCOS) usually occurs to several women’s during their reproductive age. This type of disease leads to infertility which represent with amenorrhea and hirsutism. The combination of clinical, endocrinological, and biochemical abnormalities are known to be PCOS which particularly related with the metabolism of estrogen and androgens. The periphery of ovaries is identified with the most common immature follicles which are less than 10mm. Analyzing this kind of situations in women is a major challenge which now doctors used ultrasound images which have the necessary details like number of follicles, size, and position. For real-time analysis of PCOS is a major task as follicles contains different sizes and highly connected with tissues and blood vessels which results in error prone. Several researchers have proposed different techniques for analyzing the PCOS using the ultrasound images of ovaries. In this study, we analyze the important factors and techniques used in detection of PCOS by using the ultrasound images taken from the women’s ovary and compared the results of existing works.
Keywords: Polycystic Ovary Syndrome (PCOS), Ultrasound Image, Follicle Detection, Clustering Techniques.
Scope of the Article: Image Security