Title :
Unsupervised hierarchical multi-scale image segmentation level set, wavelet and additive splitting operator
Author :
Jeon, M. ; Alexander, M. ; Pizzi, N. ; Pedrycz, W.
Author_Institution :
Inst. for Biodiagnostics, Nat. Res. Council of Canada, Winnipeg, Man., Canada
Abstract :
This paper presents an unsupervised hierarchical multi-scale segmentation method for multi-phase images based on a single level set, a multi-scale analysis using wavelets, and the semi-implicit additive operator splitting (AOS) scheme which is stable, fast, and easy to implement The method successively segments image subregions found at each step of the hierarchy using a decision criterion based on the variance of intensity across the current subregion. Each step starts with segmenting a down-sized image, and the solution is mapped back to the original size and used as an initial contour for further processing. While there is some overhead related to processing a down-sized image, there is a substantial speedup in processing the full-sized image and selecting the subimage to be segmented.
Keywords :
image segmentation; partial differential equations; wavelet transforms; down-sized image; level set; multiphase images; semiimplicit additive operator splitting; unsupervised hierarchical multiscale image segmentation; wavelets; Active contours; Councils; Histograms; Image analysis; Image edge detection; Image processing; Image segmentation; Level set; Merging; Wavelet analysis;
Conference_Titel :
Fuzzy Information, 2004. Processing NAFIPS '04. IEEE Annual Meeting of the
Print_ISBN :
0-7803-8376-1
DOI :
10.1109/NAFIPS.2004.1337380