A mammography provides a grayscale image of the breast. The main challenge of analyzing mammography images is to extract the region boundary of the breast abnormality for further analysis. In computer vision, this method is also known as image segmentation. The variational level set mathematical model has been proven to be effective for image segmentation. Several selective types of variational level set models have recently been formulated to accurately segment a specific object on images. However, these models are incapable of handling complex intensity inhomogeneity images, and the segmentation process tends to be slow. Therefore, this study formulated a new selective type of the variational level set model to segment mammography images that incorporate a machine learning algorithm known as Self-Organizing Map (SOM). In addition to that, the Gaussian function was applied in the model as a regularizer to speed up the processing time. Then, the accuracy of the segmentation’s output was evaluated using the Jaccard, Dice, Accuracy and Error metrics, while the efficiency was assessed by recording the computational time. Experimental results indicated that the new proposed model is able to segment mammography images with the highest segmentation accuracy and fastest computational speed compared to other iterative models.

Missing data is a recurring issue in psychology questionnaire when a respondent does not respond to questions due to personal reasons. In general, two types of imputation techniques are used to replace missing data: single imputation and multiple imputation (MI). The single imputation technique generates a single value to impute each missing data. The simplest methods of single imputation are mean, mode and median. In contrast, the multiple imputation technique imputes each missing data several times resulting in multiple complete datasets. The most popular method in MI that can deal with numerical and categorical data type is the predictive mean matching (PMM). The aim of this article is to compare and visualize how the mode imputation method in the single imputation technique will lead to a biased data distribution and the PMM method in the MI techniques will reduce this issue. Both methods, mode imputation and PMM are often considered when dealing with categorical data types. The mode imputation replaces a missing data with the most frequent value of an item in a survey. Meanwhile, the predictive mean matching is an extension of regression model that apply donor selection strategy to replace a missing data. Results from bar charts visualize the multiple imputation shows less discrepancy between the original distribution and imputed distribution. Thus, in this research, it can be concluded that the PMM method in MI technique shows a less biased distribution than implementing the mode imputation method. A comparison of imputation methods with different missing rates on a survey dataset should be considered for future work.

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Unsupervised learning of finite Gaussian mixture model (FGMM) is used to learn the distribution of population data. This paper proposes the use of the wild bootstrapping to create the variability of the imputed data in single missing data imputation. We compare the performance and accuracy of the proposed method in single imputation and multiple imputation from the R-package Amelia II using RMSE, R-squared, MAE and MAPE. The proposed method shows better performance when compared with the multiple imputation (MI) which is indeed known as the golden method of missing data imputation techniques.