Abstract
Breast cancer is one of the most mediated malignant diseases, because of its high incidence and prevalence, but principally due to its physical and psychological invasiveness. The study of this disease using computer science tools resorts often to the image segmentation operation. Image segmentation, although having been extensively studied, is still an open problem. Shortest path algorithms are extensively used to tackle this problem. There are, however, applications where the starting and ending positions of the shortest path need to be constrained, defining a closed contour enclosing a previously detected seed. Mass and calcification segmentation in mammograms and areola segmentation in digital images are two particular examples of interest within the field of breast cancer research. Usually the closed contour computation is addressed by transforming the image into polar coordinates, where the closed contour is transformed into an open contour between two opposite margins. In this work, after illustrating some of the limitations of this approach, we show how to compute the closed contour in the original coordinate space. After defining a directed acyclic graph appropriate for this task, we address the main difficulty in operating in the original coordinate space. Since small paths collapsing in the seed point are naturally favored, we modulate the cost of the edges to counterbalance this bias. A thorough evaluation is conducted with datasets from the breast cancer field. The algorithm is shown to be fast and reliable and suffers no loss in resolution.

Abstract
"Taking less treating better" has been one of the major improvements of breast cancer surgery in the last four decades. The application of this principle translates into equivalent survival of breast cancer conserving treatment (BCT) when compared to mastectomy, with a better cosmetic outcome. While it is relatively easy to evaluate the oncological results of BCT, the cosmetic outcome is more difficult to measure due to the lack of an effective and consensual procedure. The assessment of cosmetic outcome has been mainly subjective, undertaken by a panel of expert observers or/and by patient self-assessment. Unfortunately, the reproducibility of these methods is low. Objective methods have higher values of reproducibility but still lack the inclusion of several features considered by specialists in BCT to be fundamental for cosmetic outcome. The recent addition of volume information obtained with 3D images seems promising. Until now, unfortunately, no method is considered to be the standard of care. This paper revises the history of cosmetic evaluation and guides us into the future aiming at a method that can easily be used and accepted by all, caregivers and caretakers, allowing not only the comparison of results but the improvement of performance. (C) 2014 Wiley Periodicals, Inc.

Abstract
Animal behavior assessment plays an important role in basic and clinical neuroscience. Although assessing the higher functional level of the nervous system is already possible, behavioral tests are extremely complex to design and analyze. Animal's responses are often evaluated manually, making it subjective, extremely time consuming, poorly reproducible and potentially fallible. The main goal of the present work is to evaluate the use of consumer depth cameras, such as the Microsoft's Kinect, for detection of behavioral patterns of mice. The hypothesis is that the depth information, should enable a more feasible and robust method for automatic behavior recognition. Thus, we introduce our depth-map based approach comprising mouse segmentation, body-like per-frame feature extraction and per-frame classification given temporal context, to prove the usability of this methodology.

Abstract

Abstract
The conservative treatment is now the preferred procedure to treat breast cancer mainly due to better aesthetical results obtained. However, the aesthetic outcome is diverse and very difficult to evaluate, which motivates the research on automatic methodologies. The use of three-dimensional (3D) methodologies is increasing; however, the high cost of the equipment and the need for specialised technicians to operate it are import setbacks. Consequently, the search for affordable and easy to perform equipments is highly desirable. This paper studies the application of a Kinect device in this field, addressing issues related to accuracy, resolution and quality of the data. The paper demonstrates a comparative study of state-of-the-art Super-Resolution (SR) algorithms applied to the Kinect depth data, and the importance to improve the quality of images is stressed. The results demonstrate that it is possible to measure volumetric information and that there is agreement between features and the subjective aesthetic evaluation.

Abstract
The extraction of morphometric features from images of biological structures is a crucial task for the study of several diseases. Particularly, concerning neuropathies, the state of the myelination process is vital for neuronal integrity and may be an indicator of the disease type and state. Few approaches exist to automatically analyse nerve morphometry and assist researchers in this time consuming task. The aim of this work is to develop an algorithm to detect axons and myelin contours in myelinated fibres of sciatic nerve images, thus allowing the automated assessment and quantification of myelination through the measurement of the g-ratio. The application of a directional gradient together with an active contour algorithm was able to effectively and accurately determine the degree of myelination in an imagiological dataset of sciatic nerves. It was obtained an average error of 1.80%, in comparison with the manual annotation performed by the specialist in all dataset.