Abstract
Machine learning can be applied to the diagnosis of polycystic ovarian syndrome (PCOS), one of criteria for PCOS patients is presence polycystic ovary (PCO). PCO is the presence of the least 12 follicles in the ovary or follicular diameter between 2 and 9 mm and/or increased ovarian volume 10 cm3. In this research, a computational model for the detection of follicles of various sizes and extracting relevant features of the follicle and calculate the diameter and the number of follicles is proposed. The proposed model consists of pre-processing, speckle noise reduction, follicular segmentation, feature extraction, feature selection, and calculate the diameter of number follicles. The segmentation method uses active contour to divide objects base on the similarity of follicle shape feature so that it is more accurate in calculating the number and diameter of follicles. The performance of this method is tested on a dataset of ovarian ultrasound images of patients at Sardjito Hospital, Yogyakarta using Probabilistic Rand Index (PRI) and Global Consistency Error (GCE).
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References
Bagasjvara, R., Candradewi, I., Hartati, S., Harjoko, A.: Automated detection and classification techniques of Acute leukemia using image processing: a review. In: 2016 2nd International Conference on Science and Technology-Computer (ICST), pp. 35–43 (2016)
Hartati, S., Harjoko, A., Rosnelly, R., Chandradewi, I., Faizah, : Performance of SVM and ANFIS for classification of malaria parasite and its Life-Cycle-Stages in blood smear. In: Yap, B., Mohamed, A., Berry, M. (eds.) Soft Computing in Data Science, SCDS 2018, Communications in Computer and Information Science, vol. 937, pp. 110–121. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-3441-2_9
Soetanto, H., Hartati, S., Wardoyo, R., Wibowo, S.: Hypertension drug suitability evaluation based on patient condition with improved profile matching. Indones. J. Electr. Eng. Comput. Sci. 11(2), 453 (2018)
Bilal, M., Haseeb, A., Rehman, A.: Relationship of polycystic ovarian syndrome with cardiovascular risk factors. Diab. Metab. Syndr. Clin. Res. Rev. 12(3), 375–380 (2018)
Fernandez, E.D.T., Adams, K.V., Syed, M., Maranon, R.O., Romero, D.G.: Long-lasting androgen-induced cardiometabolic effects in polycystic ovary syndrome. J. Endocr. Soc. 2(8), 949–964 (2018)
Ali, H.I., Elsadawy, M.E., Khater, N.H.: Ultrasound assessment of polycystic ovaries: ovarian volume and morphology; which is more accurate in making the diagnosis?! Egypt. J. Radiol. Nucl. Med. 47(1), 347–350 (2016)
Coelho Neto, M.A., et al.: Counting ovarian antral follicles by ultrasound: a practical guide. Ultrasound Obstet. Gynecol. 51(1), 10–20 (2018)
Azziz, R.: Definition, diagnosis, and epidemiology of the polycystic ovary syndrome. In: Azziz, R. (ed.) The Polycystic Ovary syndrome, Current Concepts on pathogenesis and Clinical care, Endocrine Updates, vol. 27, pp. 1–15. Springer, Boston (2007). https://doi.org/10.1007/978-0-387-69248-7_1
Karakas, S.E.: New biomarkers for diagnosis and management of polycystic ovary syndrome. Clin. Chim. Acta 471, 248–253 (2017)
Al-Shaikh, S.F.M.H., Al-Mukhatar, E.J., Al-Zubaidy, A.A., Al-Rubaie, B.J.U., Al-Khuzaee, L.: Use of clomiphene or letrozole for treating women with polycystic ovary syndrome related subfertility in Hilla city. Middle East Fertil. Soc. J. 22(2), 105–110 (2017)
Caburet, S., Fruchter, R.B., Legois, B., Fellous, M., Shalev, S., Veitia, R.A.: A homozygous mutation of GNRHR in a familial case diagnosed with polycystic ovary syndrome. Eur. J. Endocrinol. 176(5), K9–K14 (2017)
Skiba, M.A., Islam, R.M., Bell, R.J., Davis, S.R.: Understanding variation in prevalence estimates of polycystic ovary syndrome: a systematic review and meta-analysis. Hum. Reprod. Update. 24(6), 694–709 (2018)
Porru, C., Fulghesu, A.M., Canu, E., Cappai, A.: Ultrasound diagnosis of polycystic ovarian syndrome : current guidelines, criticism and possible update. Austin J. Obstet Gynecol. 4(2), 1074 (2017)
Gonzalez, R.C., Woods, R.E.: Digital image processing. p. 976, Nueva Jersey (2008)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1988)
Chan, T.F., Vese, L.A.: Active contours without edges. Br. Dent. J. 142(2), 73 (2001)
Unnikrishnan, R., Pantofaru, C., Hebert, M.: Toward objective evaluation of image segmentation algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 929–944 (2007)
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The author would like to thank the Research Directorate of Universitas Gadjah Mada for funding this research in the RTA (Rekognisi Tugas Akhir) scheme 2019 program.
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Eliyani, Hartati, S., Musdholifah, A. (2019). Machine Learning Assisted Medical Diagnosis for Segmentation of Follicle in Ovary Ultrasound. In: Berry, M., Yap, B., Mohamed, A., Köppen, M. (eds) Soft Computing in Data Science. SCDS 2019. Communications in Computer and Information Science, vol 1100. Springer, Singapore. https://doi.org/10.1007/978-981-15-0399-3_6
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DOI: https://doi.org/10.1007/978-981-15-0399-3_6
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