Computational Vision and Medical Image Processing: Recent by Ahmed El-Rafei, Tobias Engelhorn (auth.), João Manuel R. S.

By Ahmed El-Rafei, Tobias Engelhorn (auth.), João Manuel R. S. Tavares, R. M. Natal Jorge (eds.)

This e-book includes prolonged types of papers provided on the overseas convention VIPIMAGE 2009 – ECCOMAS Thematic convention on Computational imaginative and prescient and clinical photograph, that was once held at Faculdade de Engenharia da Universidade do Porto, Portugal, from 14th to sixteenth of October 2009. This convention used to be the second one ECCOMAS thematic convention on computational imaginative and prescient and clinical picture processing. It lined themes on the topic of photograph processing and research, scientific imaging and computational modelling and simulation, contemplating their multidisciplinary nature. The booklet collects the state of the art learn, tools and new tendencies as regards to computational imaginative and prescient and clinical photo processing contributing to the improvement of those wisdom areas.

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4 Image Processing One of the most important systems in this kind of application is the image processing. It must be robust enough so that the tracking algorithm can have solid bases to work with. In this case the image processing system is based on blob definition and each player is seen as a colour blob. In order to have a clean interface with the images received from the camera we used the OpenCV library [15]. The following subsections describe the steps towards the players’ detection. 1 Team Definition The first step consists on defining the colour blobs for each team that correspond to sub-spaces of the entire colour space.

In particular, perineal, introital and trans-vaginal ultrasound has become an imaging platform for the evaluation of the PF and for the treatment planning of many uro-gynecological conditions [8]. By its nature, 2D ultrasound imaging provides a very large amount of dynamic data that cannot be visually assimilated by the observer in its totality, particularly during fast occurring events. Such dynamic events contain information relating the integrity of the supporting structures of the bladder neck, the role of the PF, and the compliance of pelvic floor structures [5].

Start initial point the first non-zero point the contour of the segmentation Choose a initial point Fix one end of the ruler on the initial point Rotate the ruler until it hit a non-zero point Fix one end of the ruler on the non-zero point the trace of the virtual ruler Segmentation complete? No Yes Smooth the boundary using curve fitting End Fig. 3 Flow chart of automatic image segmentation. (Modified and reprinted in part from Peng et al. 2007) ruler was then moved to the non-zero point. The virtual ruler was rotated around the fixed point again until it hit a new non-zero point on the edge.

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