Over-Segmentation of 3D Medical Images Volumes based on Monogenic Cues

MonoSLIC employs the transformation of the image content to its monogenic signal as primal representation of the image. The phase of the monogenic signal is invariant to contrast and brightness and by selecting a kernel size matched to the estimated average size of the superpixels it highlights the locally most dominant image edge. Employing an agglomeration step similar to the one used in SLIC superpixels yields superpixels/-voxels with high fidelity to local edge information while being of regular size and shape. MonoSLIC does not need any parameter tweaking, is robust to noise, invariant to contrast and fast in 3D, making it perfect for the use with 2D and 3D medical images.


Paper of the Computer Vision Winter Workshop 2014: paper bibtex

Matlab Code: code


by Markus Holzer and Rene Donner.


Work that uses MonoSLIC:

Johannes Hofmanninger, and Georg Langs. "Mapping Visual Features to Semantic Profiles for Retrieval in Medical Imaging" Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2015.