Resumen en español

Fast and accurate computation of the Euclidean distance map transformation is presented using the python programming language in conjunction with the vtk and itk toolkits. Two algorithms are compared on the basis of their efficiency and computational speed; Saitho algorithm and Danielsson’s four-points Sequential Euclidean Distance (4SED). An algorithm is used to compute a scalar distance map from a 3D data set or volume, which can be used to extract specific distance values. The performance time for the Saitho computation speed was less than the Danielsson’s 4SED computation allowing a faster calculation of the Euclidean distance map. A software analysis application was implemented using the Saitho algorithm for the computation of the scalar distance maps; it also included an underlying segmentation method to allow the computation of Euclidean distance maps on micro-CT images of segmented bone structures. In the future, this application could be used in conjunction with other image processing software applications of bone analysis