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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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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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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