  We propose a novel method, the adaptive local window, for improving level set
segmentation technique. The window is estimated separately for each contour
point, over iterations of the segmentation process, and for each individual
object. Our method considers the object scale, the spatial texture, and changes
of the energy functional over iterations. Global and local statistics are
considered by calculating several gray level co-occurrence matrices. We
demonstrate the capabilities of the method in the domain of medical imaging for
segmenting 233 images with liver lesions. To illustrate the strength of our
method, those images were obtained by either Computed Tomography or Magnetic
Resonance Imaging. Moreover, we analyzed images using three different energy
models. We compare our method to a global level set segmentation and to local
framework that uses predefined fixed-size square windows. The results indicate
that our proposed method outperforms the other methods in terms of agreement
with the manual marking and dependence on contour initialization or the energy
model used. In case of complex lesions, such as low contrast lesions,
heterogeneous lesions, or lesions with a noisy background, our method shows
significantly better segmentation with an improvement of 0.25+- 0.13 in Dice
similarity coefficient, compared with state of the art fixed-size local windows
(Wilcoxon, p < 0.001).
