Medical Image Segmentation using a Self-organizing Neural Network and Clifford Geometric Algebra

In this paper we present a method based on self-organizing neural networks to extract the shape of an object which is useful for segmentation tasks. For that, the method uses a set of transformations expressed as versors in the conformal geometric algebra framework. Such transformations, when applied to any geometric entity of this geometric algebra, define the shape of the object. The utility of this approach is showed with one synthetic and several medical images (computer tomography and magnetic resonance images), where the object of interest is well segmented even if there is no well defined contours in the original image. In fact, the segmentation results obtained are better than the results using the ggvf-snake, no matter if the initialization of the snake is given inside, outside or over the (blurred) contour of the object.