Affiliations: Department of Instrumentation Engineering, MIT Campus, Anna University, Chennai, Tamilnadu, India
Correspondence:
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Corresponding author: H. Prasanna Kumar, Department of Instrumentation Engineering, MIT Campus, Anna University, Chennai-600044, Tamilnadu, India. Tel.: +91 9945389567; Fax: +91 222324403; E-mail: [email protected].
Abstract: Background:In recent times there has been a significant change in lifestyle in many parts of the world, with most people experiencing a more sedentary existence combined with an abundance of food. This has resulted in the modern epidemic of obesity and consequent hyperinsulinemia – situations which in women may precipitate expression of fertility problems; effective methods to evaluate the fertility status are required. Ultrasonographic imaging is an effective, easy to use, safe, and readily available noninvasive means to evaluate fertility potential. Objective:Manual recognition of the follicles in terms of area measurement and counting the number of follicles is laborious; often fatigue may lead to error-prone conclusions. The paper attempts an automated classification of the ovaries based on the biomarking done by the physician. Also, biomarked data correlates with the hormones values such as androgen, testosterone and leutinizing hormone. Methods:Despeckled images are segmented by improved active contour with split-Bregman optimization. The features are extracted from images using geometric and intensity method. The significant features selected by particle swarm optimization and dimension reduction by principal component analysis and classification by probabilistic neural network. Results:Proposed probabilistic neural network achieves maximum efficiency of 97% compared to SVM 92% and RBF 88%. Conclusions:The results obtained show that using a very large number of features combined with a feature selection approach allows us to achieve high classification rates.
Keywords: Polycystic ovary syndrome, particle swarm optimization, principal component analysis, support vector machine, probabilistic neural network
