There is a wide set of digital images, where the problem of detecting specific structures is filtering between multiple and complex lines and secondary elements. The real problem is extracting relevant information from images, discarding uninteresting information previously, during and after the segmentation process. In this work, we resume the advantages and disadvantages of each approach, concluding a basic preference of filtering as soon as possible. In this sense, we present a method of filtering during segmentation, which mixes the mobile windows and the seeded regions approaches. Main steps are: 1) The whole image is divided in windows with a size related with the searched structures; 2) Previous knowledge about the location of the searched elements is applied to reduce the number of windows; 3) The number of windows is reduced using distribution and compacity conditions; 4) The population of each work windows is analyzed to fix one threshold; 5) Filtered work pairs are segmented using simple two populations criteria; 6) Analyzing the detected segments, the list of work window-threshold pairs is extended to include new windows. Most relevant result is the definition of a new
border based segmentation approach, which gives good results searching specific objects in complex images.