A hybrid medical image segmentation approach based on dual-front evolution model

Hua Li, Anthony Yezzi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, a hybrid medical image segmentation approach is proposed based on a dual front evolution and fast sweeping evolution. This approach is composed of two stages. In the first stage, a fast sweeping evolution with a stopping criterion based upon gradient information is adopted to give a fast and rough initial boundary estimate close to (or overlapping) the actual boundary. Next, a morphological dilation is used to expand this boundary to a narrow region large enough to contain the actual boundary. In the second stage, a dual front evolution model is used to refine the final segmentation result. In this step, the evolution speeds consider the gradient information together with less local image statistics to improve the veracity and compatibility of the algorithm. The experimental results show that this two-stage algorithm can provide close, smooth and accurate final contours with low computational complexity O(N).

Original languageEnglish
Title of host publicationIEEE International Conference on Image Processing 2005, ICIP 2005
Pages810-813
Number of pages4
DOIs
StatePublished - Dec 1 2005
EventIEEE International Conference on Image Processing 2005, ICIP 2005 - Genova, Italy
Duration: Sep 11 2005Sep 14 2005

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2
ISSN (Print)1522-4880

Conference

ConferenceIEEE International Conference on Image Processing 2005, ICIP 2005
CountryItaly
CityGenova
Period09/11/0509/14/05

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    Li, H., & Yezzi, A. (2005). A hybrid medical image segmentation approach based on dual-front evolution model. In IEEE International Conference on Image Processing 2005, ICIP 2005 (pp. 810-813). [1530179] (Proceedings - International Conference on Image Processing, ICIP; Vol. 2). https://doi.org/10.1109/ICIP.2005.1530179