Abstract
Breast Cancer Conservative Treatment (BCCT) is now the preferred technique for breast cancer treatment. The limited reproducibility of standard aesthetic evaluation methods led to the development of objective methods, such as Breast Cancer Conservative Treatment. cosmetic results (BCCT. core) software tool. Although the satisfying results, there are still limitations concerning complete automation and the inability to measure volumetric information. With the fundamental premise of maintaining the system as a low-cost tool, the incorporation of the Microsoft Kinect sensor in BCCT evaluations was studied. The aim with this work is to enable the simultaneous detection of breast contour and breast peak points using depth-map data. Experimental results show that the proposed algorithm is accurate and robust for a wide number of patients. Additionally, comparatively to previous research, the procedure for detecting prominent points was automated.

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Media content adaptation is the action of transforming media files to adapt to device capabilities, usually related to mobile devices that require special handling because of their limited computational power, small screen size and constrained keyboard functionality. Image retargeting is one of such adaptations, transforming an image into another with different size. Tools allowing the author to imagery once and automatically retarget that imagery for a variety of different display devices are therefore of great interest. The performance of these algorithms is directly related with the preservation of the most important regions and features of the image. In this work, we introduce an algorithm for automatically retargeting images. We explore and extend a recently proposed algorithm on the literature. The central contribution is the introduction of the stable paths for image resizing, improving both the computational performance and the overall quality of the resulting image. The experimental results confirm the potential of the proposed algorithm.

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Objective: This work presents a novel approach for the automated prediction of the aesthetic result of breast cancer conservative treatment (BCCT). Cosmetic assessment plays a major rote in the study of BCCT. Objective assessment methods are being preferred to overcome the drawbacks of subjective evaluation. Methodology: The problem is addressed as a pattern recognition task. A dataset of images of patients was classified in four classes (excellent, good, fair, poor) by a panel of international experts, providing a gold standard classification. As possible types of objective features we considered those already identified by domain experts as relevant to the aesthetic evaluation of the surgical procedure, namely those assessing breast asymmetry, skin colour difference and scar visibility. A classifier based on support vector machines was developed from objective features extracted from the reference dataset. Results: A correct classification rate of about 70% was obtained when categorizing a set of unseen images into the aforementioned four classes. This accuracy is comparable with the result of the best evaluator from the panel of experts. Conclusion: The results obtained are rather encouraging and the developed tool, could be very helpful in assuring objective assessment of the aesthetic outcome of BCCT.

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