Title :
Feature space region growing
Author :
Revol-Muller, C. ; Grenier, T. ; Ting Li ; Benoit-Cattin, H.
Author_Institution :
CREATIS, Univ. de Lyon 1, Lyon, France
fDate :
Sept. 30 2012-Oct. 3 2012
Abstract :
We propose a framework for the segmentation by region growing approach leveraging on feature space. It has the advantages to deal with multidimensional data and easily specify locally adaptive segmentation. It relies upon the definition of a robust neighborhood which drives the region growing. We propose two applications to illustrate this framework: a segmentation of physical parameters maps of MRI by using n-dimensional region growing and a segmentation of highly noisy image by using adaptive region growing.
Keywords :
feature extraction; image segmentation; magnetic resonance imaging; MRI; adaptive region growing approach segmentation; feature space region; multidimensional data; n-dimensional region; noisy image segmentation; physical parameters maps segmentation; robust neighborhood; Estimation; Image segmentation; Indexes; Kernel; Magnetic resonance imaging; Noise measurement; Robustness; Feature space; Region growing; Segmentation;
Conference_Titel :
Image Processing (ICIP), 2012 19th IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
978-1-4673-2534-9
Electronic_ISBN :
1522-4880
DOI :
10.1109/ICIP.2012.6467427