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Description
Localization and segmentation of tumors within Magnetic Resonance Imaging (MRI), using grouping algorithms of automatic learning or clustering. These algorithms are based on Physics and Computing Science concepts. We have developed and applied these algorithms to the segmentation of brain tumors and uveal melanoma.Other information
Number of researchers:
1
Development status:
In research and development phase
Differentiation in the market:
Quality
Applicability of technology:
Yes
Companies and markets:
Any healthcare center with Magnetic Resonance Imaging equipment.
Advantages:
Automatized method that detects, fast and efficiently, the presence of tumors in MRI images.
UNESCO Code:
2212 - Theoretical physics
 | Comunicación en Congreso: Cabria, I.; Gondra, I. Automated Localization of Brain Tumors in MRI Using Potential-K-means Clustering Algorithm, 12th Conference on Computer and Robot Vision (CRV); Halifax, Canadá, 2015 See / Download resource |
| Comunicación en Congreso: Gondra, I.; Cabria, I. Automated Segmentation of Brain Tumors in MRI Using Potential Field Clustering, International Conference on Computer as a Tool (EUROCON), IEEE ; Salamanca, 2015 See / Download resource |
| Publicación especializada: Inf. Fusion, 2017, 36, 1–9. DOI: 10.1016/j.inffus.2016.10.003 See / Download resource |
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