Métodos para aproximação poligonal e o desenvolvimento de extratores de características de forma a partir da função tangencial

Abstract

Whereas manually drawn contours could contain artifacts related to hand tremor, automatically detected contours could contain noise and inaccuracies due to limitations or errors in the procedures for the detection and segmentation of the related regions. To improve the further step of description, modeling procedures are desired to eliminate the artifacts in a given contour, while preserving the important and significant details present in the contour. In this work, are presented a couple of polygonal modeling methods, first a method applied direct on the original contour and other derived from the turning angle function. Both methods use the following parametrization Smin e µmax to infer about removing or maintain a given segment. By the using of the mentioned parameters the proposed methods could be configured according to the application problem. Both methods have been shown eficient to reduce the influence of noise and artifacts while preserving relevant characteristic for further analysis. Systems to support the diagnosis by images (CAD) and retrieval of images by content (CBIR) use shape descriptor methods to make possible to infer about factors existing in a given contour or as base to classify groups with dierent patterns. Shape factors methods should represent a value that is aected by the shape of an object, thus it is possible to characterize the presence of a factor in the contour or identify similarity among contours. Shape factors should be invariant to rotation, translation or scale. In the present work there are proposed the following shape features: index of the presence of convex region (XRTAF ), index of the presence of concave regions (V RTAF ), index of convexity (CXTAF ), two measures of fractal dimension (DFTAF e DF1 TAF ) and the index of spiculation (ISTAF ). All derived from the smoothed turning angle function. The smoothed turning angle function represent the contour in terms of their concave and convex regions. The polygonal modeling and the shape descriptors methods were applied on the breast masses classification issue to evaluate their performance. The polygonal modeling procedure proposed in this work provided higher compression and better polygonal fitness. The best classification accuracies, on discriminating between benign masses and malignant tumors, obtain for XRTAF , V RTAF , CXTAF , DFTAF , DF1 TAF and ISTAF , in terms of area under the receiver operating characteristics curve, were 0:92, 0:92, 0:93, 0:93, 0:92 e 0:94, respectively.