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Background: Detection of clustered microcalcification (MC) from mammograms plays essential roles in computer-aided diagnosis for early stage breast cancer.Objective: To tackle problems associated with the diversity of data structures of MC lesions and the variability of normal breast tissues, multi-pattern sample space learning is required.Methods: In this paper, a novel grouped fuzzy Support Vector Machine (SVM) algorithm with sample space partition based on Expectation-Maximization (EM) (called G-FSVM) is proposed for clustered MC detection. The diversified pattern of training data is partitioned into several groups based on EM algorithm. Then a series of fuzzy SVM are integrated for classification with each group of samples from the MC lesions and normal breast tissues.Results: From DDSM database, a total of 1,064 suspicious regions are selected from 239 mammography, and the measurement of Accuracy, True Positive Rate (TPR), False Positive Rate (FPR) and EVL = TPR* 1-FPR are 0.82, 0.78, 0.14 and 0.72, respectively.Conclusion: The proposed method incorporates the merits of fuzzy SVM and multi-pattern sample space learning, decomposing the MC detection problem into serial simple two-class classification. Experimental results from synthetic data and DDSM database demonstrate that our integrated classification framework reduces the false positive rate significantly while maintaining the true positive rate. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]