We report on a morphological study of 192 breast masses as seen in mammograms, with the aim of discrimination between benign masses and malignant tumors. From the contour of each mass, we computed the fractal dimension (FD) and a few shape factors, including compactness, fractional concavity, and spiculation index. We calculated FD using four different methods: the ruler and box-counting methods applied to each 2-dimensional (2D) contour and its 1-dimensional signature. The ANOVA test indicated statistically significant differences in the values of the various shape features between benign masses and malignant tumors. Analysis using receiver operating characteristics indicated the area under the curve, A(z), of up to 0.92 with the individual shape features. The combination of compactness, FD with the 2D ruler method, and the spiculation index resulted in the highest A(z) value of 0.93.

In this paper, we propose a novel approach for the automatic breast boundary segmentation using spatial fuzzy c-means clustering and active contours models. We will evaluate the performance of the approach on screen film mammographic images digitized by specific scanner devices and full-field digital mammographic images at different spatial and pixel resolutions. Expert radiologists have supplied the reference boundary for the massive lesions along with the biopsy proven pathology assessment. A performance assessment procedure will be developed considering metrics such as precision, recall, F-measure, and accuracy of the segmentation results. A Montecarlo simulation will be also implemented to evaluate the sensitivity of the boundary extracted on the initial settings and on the image noise

Style sketches are an essential tool for the expression and the definition of the shape of industrial products. We present a novel wavelet approach for sketch segmentation and editing expressly addressed to the designer needs. Starting from a 2D curve drawn by a user with a graphics tablet, our algorithm converts it in a B-spline representation, detects the style features, allows interactive smoothing and continuous local and global editing. The novelty compared to the existing literature is the combination of: (i) unified wavelet approach to segmentation, smoothing and editing, (ii) support for endpoint C 0 and C 1 continuity constraints and (iii) implementation with an interactive pen-tablet interface. Our approach requires low memory footprints with time complexity linear in the number of control points. We validated the algorithm with synthetic and practical tests.

No abstract