Abstract
One among the frequent disorder existing in females is caused due to the hormonal change in reproductive age group which is called as polycystic ovarian syndrome (PCOS). PCOS is mostly interrelated with type 2 diabetes mellitus, obesity in addition to high cholesterol levels, hence it is necessitated to detect in early stage besides treatment. Various forms of ovulatory failure require to be recognized and diagnosed ensuing to infertility, which is regarded a significant part. The characteristic of PCOS is that several follicles are formed in the ovary, and this may be regarded as an endocrine disorder. The various effects due to this disorder are cardiovascular disease, obesity, diabetes, and infertility. Ultrasound imaging has an eminent role in PCOS diagnosis since significant information about the number of follicles in addition to size is acquired. The follicles obtained through manual detection may be prone to error and laborious. An adaptive K-means clustering method is greatly utilized for small follicles recognition and rapid segmentation. The testing is accomplished on PCOS ultrasound images, and it is validated that this method outperforms well in contrary with prevailing methods.
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Nilofer, N.S., Ramkumar, R. (2021). An Adaptive K-Means Segmentation for Detection of Follicles in Polycystic Ovarian Syndrome in Ultrasound Image. In: Goyal, V., Gupta, M., Trivedi, A., Kolhe, M.L. (eds) Proceedings of International Conference on Communication and Artificial Intelligence. Lecture Notes in Networks and Systems, vol 192. Springer, Singapore. https://doi.org/10.1007/978-981-33-6546-9_41
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DOI: https://doi.org/10.1007/978-981-33-6546-9_41
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