DocumentCode :
2989770
Title :
An evolutionary snake algorithm for the segmentation of nuclei in histopathological images
Author :
Roula, M.A. ; Bouridane, Ahmed ; Kurugollu, F.
Author_Institution :
Sch. of Comput. Sci., Queen´´s Univ., Belfast, UK
Volume :
1
fYear :
2004
fDate :
24-27 Oct. 2004
Firstpage :
127
Abstract :
This paper addresses the problem of automatic segmentation of nuclei in histopathological images. A novel method, inspired from active contour models is proposed. An evolutionary based approach, which guarantees convergence to global minimum energies has been used to solve the combinatorial optimization problem of snakes. The computational complexity, often associated with evolutionary approaches, has been reduced by short cutting the natural evolution step by means of replacing standard mutation with an oriented stochastic mutation process. Results have shown the efficiency of this method both in terms of accuracy and fast computation.
Keywords :
combinatorial mathematics; computational complexity; convergence of numerical methods; evolutionary computation; image segmentation; medical image processing; stochastic processes; active contour models; automatic nuclei segmentation; combinatorial optimization problem; computational complexity; evolutionary snake algorithm; global minimum energy convergence; histopathological images; oriented stochastic mutation; Active contours; Cancer; Computer science; Convergence; Dynamic programming; Genetic mutations; Image segmentation; Pathology; Shape; Stochastic processes;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing, 2004. ICIP '04. 2004 International Conference on
ISSN :
1522-4880
Print_ISBN :
0-7803-8554-3
Type :
conf
DOI :
10.1109/ICIP.2004.1418706
Filename :
1418706
Link To Document :
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