A computational method was developed for the measurement of breast density using chest computed tomography (CT) images and the correlation between that and mammographic density. Sixty-nine asymptomatic Asian women (138 breasts) were studied. With the marked lung area and pectoralis muscle line in a template slice, demons algorithm was applied to the consecutive CT slices for automatically generating the defined breast area. The breast area was then analyzed using fuzzy c-mean clustering to separate fibroglandular tissue from fat tissues. The fibroglandular clusters obtained from all CT slices were summed then divided by the summation of the total breast area to calculate the percent density for CT. The results were compared with the density estimated from mammographic images. For CT breast density, the coefficient of variations of intraoperator and interoperator measurement were 3.00 % (0.59 %-8.52 %) and 3.09 % (0.20 %-6.98 %), respectively. Breast density measured from CT (22 ± 0.6 %) was lower than that of mammography (34 ± 1.9 %) with Pearson correlation coefficient of r=0.88. The results suggested that breast density measured from chest CT images correlated well with that from mammography. Reproducible 3D information on breast density can be obtained with the proposed CT-based quantification methods.

This article presents the development of automatic clone detection for verification students programs - the Detector of Clones. The Levenshtein Distance is used for the initial assessment of the similarity of the two token sequences which is calculated by the Wagner-Fisher algorithm. Verification of two programs is carried out by the fingerprint method and by the winnowing method. To improve the results of plagiarism assessment, an additional calculation was added to the implementation of the winnowing method: the detector compares not only the labels themselves, but also the sequences near the matching labels. The detector of clones is formed for the cycle of disciplines Object Oriented Programming, Object Oriented Analysis and Design on the Moodle platform. These tools support C-like languages (C++ and C#). In general, the work of the detector undoubtedly made it possible to increase the consistency in verification of student programs.