DocumentCode :
3248523
Title :
Medical image segmentation based on watershed and graph theory
Author :
Zhang, Yan ; Cheng, Xiaoping
Author_Institution :
Sch. of Comput. & Inf. Sci., Southwest China Univ., Chongqing, China
Volume :
3
fYear :
2010
fDate :
16-18 Oct. 2010
Firstpage :
1419
Lastpage :
1422
Abstract :
Strong noise, poor gray-scale contrast, blurred margins of tissue are characteristics of medical images. Extracting object of interest in medical images is challenging. A segmentation approach that combines watershed algorithm with graph theory is proposed in this paper. This algorithm reconstructs gradient before watershed segmentation, based on the reconstruction, a floating-point active-image is introduced as the reference image of watershed transform. Finally, a graph theory based algorithm Grab Cut is used for fine segmentation. False contours of over-segmentation are effectively excluded and total segmentation quality significant improved as suitable for medical image segmentation.
Keywords :
biomedical imaging; graph theory; image segmentation; fine segmentation; floating-point active-image; graph theory; image reconstruction; medical image segmentation; watershed algorithm; Graph theory; Image reconstruction; Image segmentation; Medical diagnostic imaging; Noise; Pixel; Grab Cut method; floating-point active-image; medical images; watershed algorithm;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image and Signal Processing (CISP), 2010 3rd International Congress on
Conference_Location :
Yantai
Print_ISBN :
978-1-4244-6513-2
Type :
conf
DOI :
10.1109/CISP.2010.5646332
Filename :
5646332
Link To Document :
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