2019
Journal article  Open Access

Spatial logics and model checking for medical imaging

Banci Buonamici F., Belmonte G., Ciancia V., Latella D., Massink M.

Closure spaces  Distance Transform  Statistical texture analysis  Medical imaging  Segmentation  Magnetic resonance imaging  Spatial logics  Model checking  Information Systems  Software 

Recent research on spatial and spatio-temporal model checking provides novel image analysis methodologies, rooted in logical methods for topological spaces. Medical imaging (MI) is a field where such methods show potential for ground-breaking innovation. Our starting point is SLCS, the Spatial Logic for Closure Spaces--closure spaces being a generalisation of topological spaces, covering also discrete space structures--and topochecker, a model checker for SLCS (and extensions thereof). We introduce the logical language ImgQL ("Image Query Language"). ImgQL extends SLCS with logical operators describing distance and region similarity. The spatio-temporal model checker topochecker is correspondingly enhanced with state-of-the-art algorithms, borrowed from computational image processing, for efficient implementation of distance-based operators, namely distance transforms. Similarity between regions is defined by means of a statistical similarity operator, based on notions from statistical texture analysis. We illustrate our approach by means of an example of analysis of Magnetic Resonance images: segmentation of glioblastoma and its oedema.

Source: International journal on software tools for technology transfer (Internet) 22 (2019): 195–217. doi:10.1007/s10009-019-00511-9

Publisher: Springer, Berlin , Germania


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BibTeX entry
@article{oai:it.cnr:prodotti:409696,
	title = {Spatial logics and model checking for medical imaging},
	author = {Banci Buonamici F. and Belmonte G. and Ciancia V. and Latella D. and Massink M.},
	publisher = {Springer, Berlin , Germania},
	doi = {10.1007/s10009-019-00511-9},
	journal = {International journal on software tools for technology transfer (Internet)},
	volume = {22},
	pages = {195–217},
	year = {2019}
}