Abstract
Two programs were recently developed for the aesthetic evaluation of results in breast cancer conservative treatment: the Breast Cancer Conservative Treatment cosmetic results (BCCT.core) and the Breast Analyzing Tool (BAT). Both make use of a face-only photographic view of the patient and were developed to overcome the lack of reproducibility observed with subjective visual evaluation. The BCCT.core analyses several parameters related to asymmetry, color differences and scar appearance, while the BAT considers only asymmetry measurements. The purpose of this study was to compare the performance of these two methods. Material and methods Digital pictures of 59 patients from Porto and 60 from Vienna were evaluated subjectively by two panels using the four-class Harris scale. The Porto photographs had a similar backlight and better quality, and were evaluated by an international panel of 23 experts. The Vienna photographs had different backlight and lower quality, and were evaluated by four students and two breast cancer specialists. All 119 cases were submitted to analysis using the BCCT.core and BAT. Agreement between the software programs and the subjective evaluation was calculated using kappa (k), weighted kappa statistics (wk) and error rate (er). Results In overall analysis, BCCT.core program obtained a better agreement with the subjective evaluation (k = 0.56; wk = 0,64; er = 0.20) than the BAT software (k = 0.39; wk = 0.46; er = 0.42) (P < 0.0007). Results were again better for the BCCT.core program, when analysing the photographs obtained in Porto (k = 0.71; wk = 0.78; er = 0.14) than for the BAT (k = 0.35; wk = 0.41; er = 0.51) (P < 0.0003) while no significant differences in agreement were obtained regarding the Vienna images (P > 0.1). Conclusions The results suggest that the inclusion of multiple parameters in image analyses of aesthetic results has the potential to improve results. However, picture quality is probably important for analysis of other features besides asymmetry.

Abstract

Abstract
Byte stuffing is a technique to allow the transparent transmission of arbitrary sequences with constrained sequences. To date, most of the existing algorithms, such as PPP, attain a low average overhead by sacrificing the worst-case scenario. An exception is COBS which was designed for a low worst-case overhead; however, it imposes always a nonzero overhead, even on small packets. In this work is proposed a byte stuffing algorithm that simultaneously controls the average and worst-case overhead, performing close to the theoretical bound. It is shown analytically that the proposed algorithm achieves improved average and worst-case rates over state of the art methods. Furthermore, this technique is generalized to hybrid methods, with lower computing complexity. It is further analysed and compared experimentally the behaviour of the proposed algorithm against established algorithms in terms of byte overhead and computational time.

Abstract
Many real life problems require the classification of items in naturally ordered classes. These problems are traditionally handled by conventional methods for nominal classes, ignoring the order. This paper introduces a new training model for feedforward neural networks, for multiclass classification problems, where the classes are ordered. The proposed model has just one output unit which takes values in the interval [0,1]; this interval is then subdivided into K subintervals (one for each class), according to a specific probabilistic model. A comparison is made with conventional approaches, as well as with other architectures specific for ordinal data proposed in the literature. The new model compares favourably with the other methods under study, in the synthetic dataset used for evaluation.

Abstract
Text line segmentation in freestyle handwritten documents remains an open document analysis problem. Curvilinear text lines and small gaps between neighbouring text lines present a challenge to algorithms developed for machine-printed or hand-printed documents. We investigate a general-purpose, knowledge-free method for the automatic detection of text lines based on a stable path approach. Lines affected by curvature and inclination are robustly detected. The proposed methodology was tested on a modern set of handwritten images made available on the ICDAR 2009 handwriting segmentation competition, with promissing results. © 2009 IEEE.

Abstract
Breast cancer conservative treatment (BCCT) is considered the gold standard of breast cancer treatment. However, aesthetic results are heterogeneous and difficult to evaluate in a standardised way. The limited reproducibility of subjective aesthetic evaluation in BCCT forced the research on objective methods. A recent computer system was developed to objectively and automatically evaluate the aesthetic result of BCCT In this system, the detection of the breast contour on the digital photograph of the patient is necessary to extract the features subsequently used in the evaluation process. In this paper we extend an algorithm based on the shortest path on a graph to detect automatically the breast contour. The advantage of graph algorithms is that they are guaranteed to find the global optimum of the problem; the difficulty is that they make it hard to enforce shape constraints. We define and compare different techniques to introduce the a priory knowledge of the mammary contour. Experimental results show that the proposed techniques consistently outperform the base method.